#!/usr/bin/env python
# coding: utf-8

# In[1]:


from VidProM.isc.io import read_descriptors


# In[2]:


vid_name, vid_feature = read_descriptors(['./VidProM/vidprom_embed.hdf5'])


# In[3]:


vid_feature.shape


# In[4]:


import re

def remove_numbers_and_words(s):
    # 删除所有数字
    s = re.sub(r'\d+', '', s)
    # 删除指定的单词
    s = re.sub(r'(image|message|attachment|quot|make)', '', s, flags=re.IGNORECASE)
    return s


# In[5]:


import pandas as pd
df = pd.read_csv('./prompts4video_unique.csv')
imdb_reviews = list(df['prompt'])
imdb_reviews_clean = [i.split('-')[0] for i in imdb_reviews]
vidprom_prompts = [remove_numbers_and_words(i) for i in imdb_reviews_clean]


# In[6]:


len(vidprom_prompts)


# In[7]:


diffdb_name, diffdb_feature = read_descriptors(['./DiffusionDB/diffusiondb_embed.hdf5'])


# In[8]:


diffdb_feature.shape


# In[1]:


import pandas as pd
path_to_prompt_parquet = "DiffusionDB/metadata-large.parquet"
prompts = pd.read_parquet(
    path_to_prompt_parquet,
    columns=['prompt']
)
diffdb_prompts = sorted(list(set(prompts['prompt'])))
print("Length of prompts: ", len(diffdb_prompts))


# In[2]:


diffdb_prompts_1 = list(set(prompts['prompt']))


# In[3]:


with open("diffusiondb_prompts.txt", 'w', encoding='utf-8') as file:
    for fruit in diffdb_prompts_1:
        file.write(fruit + '\n')


# In[5]:


len(diffdb_prompts_1)


# In[ ]:


hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/data_vidprom_diffusiondb.ndjson", \
               path_in_repo="data_vidprom_diffusiondb.ndjson", repo_id="WenhaoWang/VidProM", \
               repo_type="dataset")


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import umap
import numpy as np
embedding_0 = umap.UMAP(n_neighbors=60,
                      min_dist=0.1,
                      metric='correlation').fit_transform(np.concatenate([vid_feature,diffdb_feature]))


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np.save('umap_diffusiondb_vidprom.npy', embedding_0)


# In[10]:


import numpy as np
embedding_0 = np.load('umap_diffusiondb_vidprom.npy')


# In[11]:


texts = vidprom_prompts + diffdb_prompts
xs = embedding_0[:, 0].astype(float).tolist()
ys = embedding_0[:, 1].astype(float).tolist()


# In[12]:


from glob import glob
from os.path import exists, join, basename
from tqdm import tqdm
from json import load, dump
from matplotlib import pyplot as plt
from collections import Counter

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from quadtreed3 import Quadtree, Node
from scipy.sparse import csr_matrix
from sklearn.neighbors import KernelDensity
from scipy.stats import norm
from typing import Tuple
from io import BytesIO
from umap import UMAP

import pandas as pd
import numpy as np
import ndjson
import requests
import urllib
import wizmap

SEED = 20230501

plt.rcParams['figure.dpi'] = 300


# In[13]:


labels = [0]*len(vidprom_prompts) + [1] *len(diffdb_prompts)


# In[14]:


len(labels)


# In[15]:


group_names = ["VidProM", "DiffusionDB"]


# In[16]:


grid_dict = wizmap.generate_grid_dict(embedding_0[:, 0].astype(float).tolist(), \
                          embedding_0[:, 1].astype(float).tolist(), \
                          texts, \
                          embedding_name = 'VidProM_DiffusionDB', \
                          labels = labels, \
                          group_names = group_names)


# In[17]:


print(grid_dict.keys())


# In[18]:


data_list = wizmap.generate_data_list(xs, ys, texts, labels = labels)


# In[19]:


get_ipython().system('mkdir wizmap_vidprom_diffusiondb_final')


# In[20]:


wizmap.save_json_files(data_list, grid_dict, output_dir='./wizmap_vidprom_diffusiondb_final')


# In[21]:


get_ipython().system('mv ./wizmap_vidprom_diffusiondb_final/data.ndjson ./wizmap_vidprom_diffusiondb_final/data_vidprom_diffusiondb.ndjson')


# In[22]:


get_ipython().system('mv ./wizmap_vidprom_diffusiondb_final/grid.json ./wizmap_vidprom_diffusiondb_final/grid_vidprom_diffusiondb.json')


# In[6]:


import os

# os.environ["HF_ENDPOINT"] = "http://localhost:5564"
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

from huggingface_hub import HfApi, logging

logging.set_verbosity_debug()
hf = HfApi(
    endpoint="https://huggingface.co", # Can be a Private Hub endpoint.
    token="xxxx", # Token is not persisted on the machine.
)


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hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/grid_vidprom_diffusiondb.json", \
               path_in_repo="grid_vidprom_diffusiondb.json", repo_id="WenhaoWang/VidProM", \
               repo_type="dataset")


# In[24]:


hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/grid_vidprom_diffusiondb.json", \
               path_in_repo="grid_vidprom_diffusiondb.json", repo_id="WenhaoWang/VidProM", \
               repo_type="dataset")


# In[25]:


hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/data_vidprom_diffusiondb.ndjson", \
               path_in_repo="data_vidprom_diffusiondb.ndjson", repo_id="WenhaoWang/VidProM", \
               repo_type="dataset")