image-match/python/py/Lib/site-packages/transformers/models/glm4v/processing_glm4v.py

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# Copyright 2025 The ZhipuAI Inc. team and HuggingFace Inc. team. All rights reserved.
#
# 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.

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import auto_docstring, logging
from ...video_utils import VideoInput


logger = logging.get_logger(__name__)


class Glm4vProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding": False,
            "return_token_type_ids": False,
            "return_mm_token_type_ids": True,
        },
        "videos_kwargs": {"return_metadata": True},
    }


@auto_docstring
class Glm4vProcessor(ProcessorMixin):
    def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
        self.image_token = "<|image|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
        self.video_token = "<|video|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
        self.image_token_id = (
            tokenizer.image_token_id
            if getattr(tokenizer, "image_token_id", None)
            else tokenizer.convert_tokens_to_ids(self.image_token)
        )
        self.video_token_id = (
            tokenizer.video_token_id
            if getattr(tokenizer, "video_token_id", None)
            else tokenizer.convert_tokens_to_ids(self.video_token)
        )
        super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
        self.video_start_id = tokenizer.convert_tokens_to_ids("<|begin_of_video|>")
        self.video_end_id = tokenizer.convert_tokens_to_ids("<|end_of_video|>")

    @auto_docstring
    def __call__(
        self,
        images: ImageInput | None = None,
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
        videos: VideoInput | None = None,
        **kwargs: Unpack[Glm4vProcessorKwargs],
    ) -> BatchFeature:
        r"""
        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`.
        """
        output_kwargs = self._merge_kwargs(
            Glm4vProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        if images is not None:
            image_inputs = self.image_processor(images=images, **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.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
            # If user has not requested video metadata, pop it
            if not kwargs.get("return_metadata"):
                video_metadata = videos_inputs.pop("video_metadata")
            else:
                video_metadata = videos_inputs["video_metadata"]
            video_grid_thw = videos_inputs["video_grid_thw"]
        else:
            videos_inputs = {}
            video_grid_thw = None

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

        text = text.copy()  # below lines change text in-place
        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]:
                    num_image_tokens = image_grid_thw[index].prod() // merge_length
                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.image_token)

        if video_grid_thw is not None:
            merge_length = self.video_processor.merge_size**2
            video_index = 0
            for i in range(len(text)):
                while self.video_token in text[i]:
                    num_frames = video_grid_thw[video_index][0]
                    video_structure = ""

                    metadata = video_metadata[video_index]
                    if metadata.fps is None:
                        logger.warning_once(
                            "SmolVLM requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
                        )
                    metadata.fps = 24 if metadata.fps is None else metadata.fps
                    timestamps = metadata.timestamps[::2]  # mrope

                    unique_timestamps = []
                    for idx in range(0, len(timestamps)):
                        unique_timestamps.append(timestamps[idx])

                    selected_timestamps = unique_timestamps[:num_frames]
                    while len(selected_timestamps) < num_frames:
                        selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)

                    for frame_idx in range(num_frames):
                        timestamp_sec = selected_timestamps[frame_idx]
                        frame_structure = self.replace_frame_token_id(timestamp_sec)
                        video_structure += frame_structure

                    text[i] = text[i].replace(self.video_token, video_structure, 1)
                    num_image_tokens = (
                        video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0]
                    )
                    for frame_idx in range(num_frames):
                        if self.image_token in text[i]:
                            text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)

                    video_index += 1

                text[i] = text[i].replace("<|placeholder|>", self.image_token)
        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])

        if return_mm_token_type_ids:
            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)

    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
        """
        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
        Args:
            image_sizes (`list[list[int]]`, *optional*):
                The input sizes formatted as (height, width) per each image.
            video_sizes (`list[list[int]]`, *optional*):
                The input sizes formatted as (num_frames, height, width) per each video.
        Returns:
            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
            input modalities, along with other useful data.
        """

        vision_data = {}
        if image_sizes is not None:
            images_kwargs = Glm4vProcessorKwargs._defaults.get("images_kwargs", {})
            images_kwargs.update(kwargs)
            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size

            num_image_patches = [
                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
                for image_size in image_sizes
            ]
            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})

        if video_sizes is not None:
            videos_kwargs = Glm4vProcessorKwargs._defaults.get("videos_kwargs", {})
            videos_kwargs.update(kwargs)
            num_video_patches = [
                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
                for video_size in video_sizes
            ]
            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
            vision_data["num_video_tokens"] = num_video_tokens

        return MultiModalData(**vision_data)

    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):
        model_input_names = super().model_input_names
        model_input_names.append("mm_token_type_ids")
        return model_input_names

    def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
        # We have to iterate for each list separately because inputs
        # might be non-padded lists and we can't cast numpy on that!
        # Then cast numpy as each input for faster indexing
        mm_token_type_ids = []
        for input in input_ids:
            array_ids = np.array(input)
            mm_token_types = np.zeros_like(input)

            # Replace 0 -> 2 only inside video segments because GLM4v
            # uses the same special token to denote images and video
            # Otherwise replace 0 -> 1 for image modality
            starts = np.cumsum(array_ids == self.video_start_id, axis=0)
            ends = np.cumsum(array_ids == self.video_end_id, axis=0)
            is_video_modality = starts > ends

            mm_token_types[(array_ids == self.image_token_id) & is_video_modality] = 2
            mm_token_types[(array_ids == self.image_token_id) & (~is_video_modality)] = 1
            mm_token_type_ids.append(mm_token_types.tolist())
        return mm_token_type_ids

    def replace_frame_token_id(self, timestamp_sec):
        return f"<|begin_of_image|>{self.image_token}<|end_of_image|>{int(timestamp_sec)}"


__all__ = ["Glm4vProcessor"]