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processing_aero.py

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+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for LLaVa-Onevision.
+"""
+
+import math
+import os
+from typing import List, Optional, Union
+
+import numpy as np
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput, VideoInput
+from transformers.models.auto import AutoFeatureExtractor
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class AeroProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "audio_kwargs": {},
+    }
+
+
+class AeroProcessor(ProcessorMixin):
+    attributes = ["tokenizer", "audio_processor"]
+    valid_kwargs = [
+        "chat_template",
+        "audio_token",
+    ]
+    tokenizer_class = "AutoTokenizer"
+    audio_processor_class = "AutoFeatureExtractor"
+
+    def __init__(
+        self,
+        tokenizer=None,
+        audio_processor=None,
+        chat_template=None,
+        audio_token="<|AUDIO|>",
+        **kwargs,
+    ):
+        self.audio_token = (
+            tokenizer.audio_token if hasattr(tokenizer, "audio_token") else audio_token
+        )
+        if chat_template is None:
+            chat_template = self.default_chat_template
+        super().__init__(
+            tokenizer,
+            audio_processor,
+            chat_template=chat_template,
+        )
+
+    def __call__(
+        self,
+        text: Union[
+            TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
+        ] = None,
+        audios: Union[np.ndarray, List[np.ndarray]] = None,
+        videos: VideoInput = None,
+        images: ImageInput = None,
+        sampling_rate: Optional[int] = None,
+        **kwargs: Unpack[AeroProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+        of the above two methods for more information.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
+            - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
+        """
+
+        output_kwargs = self._merge_kwargs(
+            AeroProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if isinstance(text, str):
+            text = [text]
+        elif not isinstance(text, list) and not isinstance(text[0], str):
+            raise ValueError(
+                "Invalid input text. Please provide a string, or a list of strings"
+            )
+
+        audio_inputs = {}
+
+        if audios is not None:
+            audio_inputs = self.audio_processor(
+                audios,
+                sampling_rate=sampling_rate,
+                return_attention_mask=True,
+                padding="max_length",
+                **kwargs,
+            )
+            audio_inputs["audio_attention_mask"] = audio_inputs.pop(
+                "attention_mask"
+            )  # rename attention_mask to prevent conflicts later on
+            audio_inputs["audio_values"] = audio_inputs.pop(
+                "input_features"
+            )  # rename input_features to audio_features for clarification
+            # Computes the output length of the convolutional layers and the output length of the audio encoder
+            input_lengths = (audio_inputs["audio_attention_mask"].sum(-1) - 1) // 2 + 1
+            num_audio_tokens = (input_lengths - 2) // 2 + 1
+            text = self.expand_audio_tokens(text, num_audio_tokens, self.audio_token)
+
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        return BatchFeature(data={**text_inputs, **audio_inputs})
+
+    def expand_audio_tokens(
+        self,
+        text: List[TextInput],
+        num_audio_tokens: List[int],
+        special_token: str,
+    ):
+        prompt_strings = []
+        current_audio_idx = 0
+        for sample in text:
+            while special_token in sample:
+                num_audio_token = num_audio_tokens[current_audio_idx]
+                sample = sample.replace(
+                    special_token, "<placeholder>" * num_audio_token, 1
+                )
+                current_audio_idx += 1
+            prompt_strings.append(sample)
+        text = [
+            sample.replace("<placeholder>", special_token) for sample in prompt_strings
+        ]
+        return text
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def batch_encode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_encode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_encode(*args, **kwargs)
+
+    def encode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.encode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.encode(*args, **kwargs)
+
+    @property
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+    @property
+    def default_chat_template(self):
+        # fmt: off
+        return (
+            "{% set audio_count = namespace(value=0) %}"
+            "{% for message in messages %}"
+                "{% if loop.first and message['role'] != 'system' %}"
+                    "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+                "{% endif %}"
+                "<|im_start|>{{ message['role'] }}\n"
+                "{% if message['content'] is string %}"
+                    "{{ message['content'] }}<|im_end|>\n"
+                "{% else %}"
+                    "{% for content in message['content'] %}"
+                        "{% if 'audio' in content or 'audio_url' in content %}"
+                            "{% set audio_count.value = audio_count.value + 1 %}"
+                            "<|AUDIO|>\n"
+                        "{% elif 'text' in content %}"
+                            "{{ content['text'] }}"
+                        "{% endif %}"
+                    "{% endfor %}"
+                    "<|im_end|>\n"
+                "{% endif %}"
+            "{% endfor %}"
+            "{% if add_generation_prompt %}"
+                "<|im_start|>assistant\n"
+            "{% endif %}"
+        )
+        # fmt: on