processing_paddleocr_vl.py

from typing import List, Union
import numpy as np
import torch
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import (
    ProcessingKwargs,
    ProcessorMixin,
    Unpack,
    VideosKwargs,
)
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput


ImageInput = Union[
    "PIL.Image.Image",
    np.ndarray,
    "torch.Tensor",
    List["PIL.Image.Image"],
    List[np.ndarray],
    List["torch.Tensor"],
]  # noqa


VideoInput = Union[
    List["PIL.Image.Image"],
    "np.ndarray",
    "torch.Tensor",
    List["np.ndarray"],
    List["torch.Tensor"],
    List[List["PIL.Image.Image"]],
    List[List["np.ndarrray"]],
    List[List["torch.Tensor"]],
]  # noqa


class PaddleOCRVLVideosProcessorKwargs(VideosKwargs, total=False):
    fps: Union[List[float], float]


class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False):
    videos_kwargs: PaddleOCRVLVideosProcessorKwargs
    _defaults = {
        "text_kwargs": {
            "padding": False,
        },
        "videos_kwargs": {"fps": 2.0},
    }


class PaddleOCRVLProcessor(ProcessorMixin):
    r"""
    [`PaddleOCRVLProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`Qwen2TokenizerFast`]. See the
    [`~PaddleOCRVLProcessor.__call__`] and [`~PaddleOCRVLProcessor.decode`] for more information.
    Args:
        image_processor ([`SiglipImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`Qwen2TokenizerFast`], *optional*):
            The tokenizer is a required input.
        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
            in a chat into a tokenizable string.
    """

    attributes = ["image_processor", "tokenizer"]
    valid_kwargs = [
        "chat_template",
        "image_std",
        "min_pixels",
        "image_mean",
        "merge_size",
        "image_processor_type",
        "temporal_patch_size",
        "patch_size",
        "max_pixels",
    ]

    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(
        self, image_processor=None, tokenizer=None, chat_template=None, **kwargs
    ):
        self.image_token = (
            "<|IMAGE_PLACEHOLDER|>"
            if not hasattr(tokenizer, "image_token")
            else tokenizer.image_token
        )
        self.video_token = (
            "<|video_pad|>"
            if not hasattr(tokenizer, "video_token")
            else tokenizer.video_token
        )
        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    def __call__(
        self,
        images: ImageInput = None,
        text: Union[
            TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
        ] = None,
        videos: VideoInput = None,
        **kwargs: Unpack[PaddleOCRVLProcessorKwargs],
    ) -> 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 Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
        SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `vision_infos` is not `None`.

        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
                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:
                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.

        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 videos to be fed to a model. Returned when `videos` is not `None`.
            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
        """
        output_kwargs = self._merge_kwargs(
            PaddleOCRVLProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        if images is not None:
            image_inputs = self.image_processor(images=images, return_tensors="pt")
            image_inputs["pixel_values"] = image_inputs["pixel_values"]
            image_grid_thw = image_inputs["image_grid_thw"]

        else:
            image_inputs = {}
            image_grid_thw = None

        if videos is not None:
            # TODO: add video processing
            videos_inputs = self.image_processor(
                images=None, videos=videos, **output_kwargs["images_kwargs"]
            )
            video_grid_thw = videos_inputs["video_grid_thw"]

            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
            if isinstance(fps, (int, float)):
                second_per_grid_ts = [
                    self.image_processor.temporal_patch_size / fps
                ] * len(video_grid_thw)
            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
                second_per_grid_ts = [
                    self.image_processor.temporal_patch_size / tmp for tmp in fps
                ]
            else:
                raise ValueError(
                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
                )
            videos_inputs.update(
                {"second_per_grid_ts": torch.tensor(second_per_grid_ts)}
            )

        else:
            videos_inputs = {}
            video_grid_thw = None

        if not isinstance(text, list):
            text = [text]

        if image_grid_thw is not None:
            index = 0
            for i in range(len(text)):
                while self.image_token in text[i]:
                    text[i] = text[i].replace(
                        self.image_token,
                        "<|placeholder|>"
                        * (
                            image_grid_thw[index].prod()
                            // self.image_processor.merge_size
                            // self.image_processor.merge_size
                        ),
                        1,
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.image_token)

        if video_grid_thw is not None:
            index = 0
            for i in range(len(text)):
                while self.video_token in text[i]:
                    text[i] = text[i].replace(
                        self.video_token,
                        "<|placeholder|>"
                        * (
                            video_grid_thw[index].prod()
                            // self.image_processor.merge_size
                            // self.image_processor.merge_size
                        ),
                        1,
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.video_token)

        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])

        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    def post_process_image_text_to_text(
        self,
        generated_outputs,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False,
        **kwargs,
    ):
        """
        Post-process the output of the model to decode the text.

        Args:
            generated_outputs (`torch.Tensor` or `np.ndarray`):
                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
                or `(sequence_length,)`.
            skip_special_tokens (`bool`, *optional*, defaults to `True`):
                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
            Clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
            **kwargs:
                Additional arguments to be passed to the tokenizer's `batch_decode method`.

        Returns:
            `List[str]`: The decoded text.
        """
        return self.tokenizer.batch_decode(
            generated_outputs,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        names_from_processor = list(
            dict.fromkeys(tokenizer_input_names + image_processor_input_names)
        )
        return names_from_processor + ["second_per_grid_ts"]


__all__ = ["PaddleOCRVLProcessor", "PaddleOCRVLProcessor"]