processing_hithinkomni.py

from typing import List, Optional, Union

import numpy as np

from transformers import BatchFeature
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput
from transformers.image_utils import ImageInput, VideoInput
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs


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


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


class HithinkOmniProcessor(ProcessorMixin):
    r"""
    Constructs a HithinkOmni processor which wraps a Qwen2.5-VL image processor and a HithinkOmni tokenizer into a single processor.
    [`HithinkOmniProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`PreTrainedTokenizerFast`]. See the
    [`~HithinkOmniProcessor.__call__`] and [`~HithinkOmniProcessor.decode`] for more information.
    Args:
        image_processor ([`Qwen2VLImageProcessor`], *optional*):
            The image processor is a required input.
        feature_extractor ([`WhisperFeatureExtractor`], *optional*):
            The feature extractor is a required input.
        tokenizer ([`PreTrainedTokenizerFast`], *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", "feature_extractor", "tokenizer"]
    valid_kwargs = ["chat_template"]

    image_processor_class = "Qwen2VLImageProcessor"
    feature_extractor_class = "WhisperFeatureExtractor"
    tokenizer_class = "PreTrainedTokenizerFast"

    def __init__(self, image_processor=None, feature_extractor=None, tokenizer=None, chat_template=None, **kwargs):
        tokenizer.model_input_names = ["input_ids", "attention_mask"]  # do not include token_type_ids
        super().__init__(image_processor, feature_extractor, tokenizer, chat_template=chat_template)
        self.image_token = getattr(tokenizer, 'image_token', '<|image_pad|>')
        self.video_token = getattr(tokenizer, 'video_token', '<|video_pad|>')
        self.chat_template = tokenizer.chat_template if chat_template is None else chat_template

    def __call__(
        self,
        images: ImageInput = None,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        videos: VideoInput = None,
        audios: Union[np.ndarray, List[np.ndarray]] = None,
        sampling_rate: Optional[int] = None,
        **kwargs: Unpack[HithinkOmniProcessorKwargs],
    ) -> 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 PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__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
        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__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.
            audios (`np.ndarray`, `List[np.ndarray]`):
                The audio or batch of audios to be prepared. Each audio can be a NumPy array.
            sampling_rate (`int`, defaults to 16000):
                The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
            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(
            HithinkOmniProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        if images is not None:
            image_inputs = self.image_processor(images=images, videos=None, **output_kwargs["images_kwargs"])
            image_grid_thw = image_inputs["image_grid_thw"]
        else:
            image_inputs = {}
            image_grid_thw = None

        if videos is not None:
            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": 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:
            merge_length = self.image_processor.merge_size**2
            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() // merge_length),
                        1,
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.image_token)

        if video_grid_thw is not None:
            merge_length = self.image_processor.merge_size**2
            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() // merge_length),
                        1,
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.video_token)

        if audios is not None:
            audio_inputs = self.feature_extractor(
                audios, sampling_rate=sampling_rate, return_attention_mask=True, padding="max_length", **kwargs
            )
            audio_inputs["feature_attention_mask"] = audio_inputs.pop(
                "attention_mask"
            )  # rename attention_mask to prevent conflicts later on
            audio_output_lengths = self.get_feat_extract_output_lengths(
                audio_inputs['feature_attention_mask'].sum(-1)
            )
            index = 0
            for i in range(len(text)):
                while "<|AUDIO|>" in text[i]:
                    text[i] = text[i].replace(
                        "<|AUDIO|>", "<|placeholder|>" * audio_output_lengths[index], 1
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", "<|AUDIO|>")
        else:
            audio_inputs = {}

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

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

    @staticmethod
    def get_feat_extract_input_length(audio_length):
        """
        Computes the input length of the audio encoder (i.e. output of the feature extractor)
        e.g. 30-second audio has 480,000 samples (sampling_rate = 16,000), the feature length will be 3,000
        """
        return int(np.ceil((audio_length - 40) / 160))  # 第一帧需要200样本，后续每帧需要160样本

    @staticmethod
    def get_feat_extract_output_lengths(input_lengths):
        """
        Computes the output length of the convolutional layers and the output length of the audio encoder
        """
        input_lengths = (input_lengths - 1) // 2 + 1
        output_lengths = (input_lengths - 2) // 2 + 1
        return output_lengths

    def featurize_audio_chunk(self, audio: np.ndarray, is_last: bool, n_extracted_frames: int = 0, **kwargs):
        """
        Extract the features from the audio chunk during streaming inference
        """
        n_frames = (len(audio) - 40) / 160  # 第一帧需要200样本，后续每帧需要160样本
        n_frames = int(np.ceil(n_frames) if is_last else np.floor(n_frames))
        n_new_frames = n_frames - n_extracted_frames
        i_end = n_frames * 160 + 40
        i_start = max(0, (n_extracted_frames + 1 - 3) * 160)  # 滑窗需要400样本，即最少3帧
        if n_new_frames <= 0 or n_frames < 2:
            return
        a = audio[i_start: i_end]  # 截取计算new frames需要的chunk
        if is_last and (n_pad := int(np.ceil(len(a) / 160)) * 160 - len(a)):  # pad to multiple of 160
            a = np.pad(a, [0, n_pad])
        features = self.feature_extractor(
            a, sampling_rate=self.feature_extractor.sampling_rate, padding='do_not_pad', **kwargs
        )['input_features']
        return features[:, :, -n_new_frames:]

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizerFast'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 PreTrainedTokenizerFast'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):
        """
        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,)`.

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

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        feature_extractor_input_names = self.feature_extractor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names
                                  + feature_extractor_input_names + ["feature_attention_mask"]))  # audio