image-match/python/py/Lib/site-packages/transformers/models/internvl/processing_internvl.py

# Copyright 2025 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 ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput, concatenate_list, make_flat_list_of_images
from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import auto_docstring
from ...video_utils import VideoInput


class InternVLProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding_side": "left",
            "return_mm_token_type_ids": False,
        },
        "images_kwargs": {
            "crop_to_patches": True,
        },
        "videos_kwargs": {
            "return_tensors": "pt",
        },
    }


@auto_docstring
class InternVLProcessor(ProcessorMixin):
    def __init__(
        self,
        image_processor=None,
        tokenizer=None,
        video_processor=None,
        image_seq_length: int = 256,
        chat_template=None,
        **kwargs,
    ):
        r"""
        image_seq_length (`int`, *optional*, defaults to 256):
            The number of image token to use per image patch. it should be set so that:
            image_seq_length = (config.image_size // config.patch_size) ** 2 * (config.scale_factor**2)
        """
        super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template, **kwargs)

        self.image_seq_length = image_seq_length
        self.start_image_token = tokenizer.start_image_token
        self.end_image_token = tokenizer.end_image_token
        self.start_image_token_id = tokenizer.start_image_token_id
        self.end_image_token_id = tokenizer.end_image_token_id
        self.image_token = tokenizer.context_image_token
        self.video_token = tokenizer.video_token
        self.image_token_id = tokenizer.context_image_token_id
        self.image_ids = [self.image_token_id, self.start_image_token_id, self.end_image_token_id]

    def _insert_media_placeholders(
        self,
        text: list[str],
        image_pixel_values,
        video_pixel_values,
        image_num_patches: list[int],
        video_num_patches: list[int],
        image_num_patches_indices: np.ndarray,
        video_num_patches_indices: np.ndarray,
        video_patch_indices: np.ndarray,
    ):
        """
        Processes interleaved text with <image> and <video> placeholders, replacing them with appropriate
        image and video tokens while keeping track of the patches used.
        """
        image_index = 0
        video_index = 0
        processed_text = []
        image_video_patches = []
        replace_strings = []
        # Support interleaved image and video in prompts:
        # Processed patches of images and videos are inserted in `image_video_patches` in the order they appear in the prompts
        for prompt in text:
            new_prompt = prompt
            while self.image_token in new_prompt or self.video_token in new_prompt:
                if self.image_token in new_prompt and (
                    self.video_token not in new_prompt
                    or new_prompt.index(self.image_token) < new_prompt.index(self.video_token)
                ):
                    # Get the slice of patches corresponding to the current image
                    start_index = image_num_patches_indices[image_index - 1] if image_index > 0 else 0
                    end_index = image_num_patches_indices[image_index]
                    image_video_patches.append(image_pixel_values[start_index:end_index])
                    # Replace the corresponding image placeholder with the correct number of image tokens
                    new_prompt = new_prompt.replace(self.image_token, "<placeholder>", 1)
                    replace_strings.append(
                        f"{self.start_image_token}{self.image_token * self.image_seq_length * image_num_patches[image_index]}{self.end_image_token}"
                    )
                    image_index += 1
                else:
                    # Get the slice of patches corresponding to the current video
                    # Here we need to account for both the multiple video frames and the potential multiple patches per frame
                    # As of now, InternVL only supports one patch per frame, but we keep the code flexible for future updates
                    current_patch_index = video_patch_indices[video_index]
                    end_patch_index = video_patch_indices[video_index + 1]
                    start_index = video_num_patches_indices[current_patch_index]
                    end_index = video_num_patches_indices[end_patch_index]
                    image_video_patches.append(video_pixel_values[start_index:end_index])
                    # Get the number of patches per frame and replace the video placeholder with the correct number of image tokens
                    num_patches = list(video_num_patches[current_patch_index:end_patch_index])
                    video_prompt = "\n".join(
                        f"Frame{i + 1}: {self.start_image_token}{self.image_token * self.image_seq_length * num_patches[i]}{self.end_image_token}"
                        for i in range(len(num_patches))
                    )
                    replace_strings.append(video_prompt)
                    new_prompt = new_prompt.replace(self.video_token, "<placeholder>", 1)
                    video_index += 1
            while "<placeholder>" in new_prompt:
                replace_str = replace_strings.pop(0)
                new_prompt = new_prompt.replace("<placeholder>", replace_str, 1)
            processed_text.append(new_prompt)

        return processed_text, image_video_patches, image_index, video_index

    @auto_docstring
    def __call__(
        self,
        images: ImageInput | None = None,
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
        videos: VideoInput | None = None,
        **kwargs: Unpack[InternVLProcessorKwargs],
    ) -> 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`.
        """
        if text is None:
            raise ValueError("You have to specify text.")

        output_kwargs = self._merge_kwargs(
            InternVLProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

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

        # Process images and videos separately, as videos don't support crop_to_patches
        image_num_patches = []
        image_pixel_values = None
        image_num_patches_indices = np.array([0])
        if images is not None:
            images = self.image_processor.fetch_images(images)
            images = make_flat_list_of_images(images)
            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
            image_num_patches = image_inputs.pop("num_patches")
            image_pixel_values = image_inputs.pop("pixel_values")
            image_num_patches_indices = np.cumsum(image_num_patches)

        video_num_patches = []  # per frame
        video_pixel_values = None
        video_patch_indices = np.array([0])
        video_num_patches_indices = np.array([0])
        if videos is not None:
            video_kwargs = output_kwargs["videos_kwargs"]
            video_inputs = self.video_processor(videos=videos, **video_kwargs)
            video_pixel_values = video_inputs.pop("pixel_values_videos")

            batch_size, num_frames, *_ = video_pixel_values.shape
            num_frames_per_video = np.full(batch_size, num_frames)
            num_frames = sum(num_frames_per_video)  # total
            video_patch_indices = np.empty(batch_size + 1, int)
            video_patch_indices[0] = 0
            video_patch_indices[1:] = np.cumsum(num_frames_per_video)
            video_num_patches = [1] * num_frames
            video_num_patches_indices = np.empty(num_frames + 1, int)
            video_num_patches_indices[0] = 0
            video_num_patches_indices[1:] = np.cumsum(video_num_patches)
            video_pixel_values = video_pixel_values.flatten(0, 1)

        image_videos_inputs = {}
        if images is not None or videos is not None:
            text, image_video_patches, image_index, video_index = self._insert_media_placeholders(
                text,
                image_pixel_values,
                video_pixel_values,
                image_num_patches,
                video_num_patches,
                image_num_patches_indices,
                video_num_patches_indices,
                video_patch_indices,
            )
            if images is not None and image_index != len(images):
                raise ValueError("Number of image placeholders in the prompt does not match the number of images.")
            if videos is not None and video_index != len(num_frames_per_video):
                raise ValueError("Number of video placeholders in the prompt does not match the number of videos.")

            # Concatenate the interleaved image and video patches (function agnostic to the patches type (list, numpy array, torch tensor))
            image_videos_inputs = {"pixel_values": concatenate_list(image_video_patches)}

        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", None)
        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
        self._check_special_mm_tokens(text, text_inputs, modalities=["image"])

        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_videos_inputs}, tensor_type=return_tensors)

    def _get_num_multimodal_tokens(self, image_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.

        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 = InternVLProcessorKwargs._defaults.get("images_kwargs", {})
            images_kwargs.update(kwargs)

            num_image_patches = [
                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
                for image_size in image_sizes
            ]
            # Add 2 for BOI and EOI tokens
            num_image_tokens = [2 + (self.image_seq_length * num_patches) for num_patches in num_image_patches]
            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})

        return MultiModalData(**vision_data)

    @property
    def model_input_names(self):
        # Overwritten because InternVL renames video inputs to `pixel_values` before returning
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return tokenizer_input_names + image_processor_input_names


__all__ = ["InternVLProcessor"]