image-match/python/py/Lib/site-packages/transformers/models/llava/processing_llava.py

# Copyright 2023 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.
"""

from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput, get_image_size, to_numpy_array
from ...processing_utils import (
    MultiModalData,
    ProcessingKwargs,
    ProcessorMixin,
    Unpack,
)
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import auto_docstring, logging


logger = logging.get_logger(__name__)


class LlavaProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {"padding": False, "return_mm_token_type_ids": False},
    }


@auto_docstring
class LlavaProcessor(ProcessorMixin):
    def __init__(
        self,
        image_processor=None,
        tokenizer=None,
        patch_size=None,
        vision_feature_select_strategy=None,
        chat_template=None,
        image_token="<image>",  # set the default and let users change if they have peculiar special tokens in rare cases
        num_additional_image_tokens=0,
        **kwargs,
    ):
        r"""
        patch_size (`int`, *optional*):
            Patch size from the vision tower.
        vision_feature_select_strategy (`str`, *optional*):
            The feature selection strategy used to select the vision feature from the vision backbone.
            Should be same as in model's config
        image_token (`str`, *optional*, defaults to `"<image>"`):
            Special token used to denote image location.
        num_additional_image_tokens (`int`, *optional*, defaults to 0):
            Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
            extra tokens appended, no need to set this arg.
        """
        self.patch_size = patch_size
        self.num_additional_image_tokens = num_additional_image_tokens
        self.vision_feature_select_strategy = vision_feature_select_strategy
        self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
        self.image_token_id = tokenizer.encode(self.image_token, add_special_tokens=False)[0]
        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    @auto_docstring
    def __call__(
        self,
        images: ImageInput | None = None,
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
        **kwargs: Unpack[LlavaProcessorKwargs],
    ) -> 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 images is None and text is None:
            raise ValueError("You have to specify at least one of `images` or `text`.")

        output_kwargs = self._merge_kwargs(
            LlavaProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        if images is not None:
            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
        else:
            image_inputs = {}

        if isinstance(text, str):
            text = [text]
        elif not isinstance(text, list) and not isinstance(text[0], str):
            raise TypeError("Invalid input text. Please provide a string, or a list of strings")

        # try to expand inputs in processing if we have the necessary parts
        prompt_strings = text
        if image_inputs.get("pixel_values") is not None:
            # Replace the image token with the expanded image token sequence
            pixel_values = image_inputs["pixel_values"]
            height, width = get_image_size(to_numpy_array(pixel_values[0]))
            num_image_tokens = (height // self.patch_size) * (
                width // self.patch_size
            ) + self.num_additional_image_tokens
            if self.vision_feature_select_strategy == "default":
                num_image_tokens -= 1

            prompt_strings = []
            for sample in text:
                sample = sample.replace(self.image_token, self.image_token * num_image_tokens)
                prompt_strings.append(sample)

        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(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
        self._check_special_mm_tokens(prompt_strings, 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_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 = LlavaProcessorKwargs._defaults.get("images_kwargs", {})
            images_kwargs.update(kwargs)
            crop_size = images_kwargs.get("crop_size", None) or self.image_processor.crop_size
            resized_height, resized_width = crop_size["height"], crop_size["width"]

            num_image_tokens = (resized_height // self.patch_size) * (resized_width // self.patch_size)
            num_image_tokens += self.num_additional_image_tokens
            if self.vision_feature_select_strategy == "default":
                num_image_tokens -= 1

            num_image_tokens = [num_image_tokens] * len(image_sizes)
            num_image_patches = [1] * len(image_sizes)
            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})

        return MultiModalData(**vision_data)


__all__ = ["LlavaProcessor"]