AutoAndroidController/py/venv/Lib/site-packages/transformers/models/pixtral/processing_pixtral.py

# coding=utf-8
# Copyright 2024 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 Pixtral.
"""

from typing import Optional, Union

import numpy as np

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


if is_vision_available():
    from .image_processing_pixtral import get_resize_output_image_size


logger = logging.get_logger(__name__)


class PixtralProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding": False,
            "return_mm_token_type_ids": False,
        },
        "images_kwargs": {},
        "common_kwargs": {
            "return_tensors": "pt",
        },
    }


# Copied from transformers.models.idefics2.processing_idefics2.is_url
def is_url(val) -> bool:
    return isinstance(val, str) and val.startswith("http")


# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
def is_image_or_image_url(elem):
    return is_url(elem) or is_valid_image(elem)


class PixtralProcessor(ProcessorMixin):
    r"""
    Constructs a Pixtral processor which wraps a Pixtral image processor and a Pixtral tokenizer into a single processor.

    [`PixtralProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
    [`~PixtralProcessor.__call__`] and [`~PixtralProcessor.decode`] for more information.

    Args:
        image_processor ([`PixtralImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`LlamaTokenizerFast`], *optional*):
            The tokenizer is a required input.
        patch_size (`int`, *optional*, defaults to 16):
            Patch size from the vision tower.
        spatial_merge_size (`int`, *optional*, defaults to 1):
            The downsampling factor for the spatial merge operation.
        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
            in a chat into a tokenizable string.
        image_token (`str`, *optional*, defaults to `"[IMG]"`):
            Special token used to denote image location.
        image_break_token (`str`, *optional*, defaults to `"[IMG_BREAK]"`):
            Special token used to denote the end of a line of pixels in an image.
        image_end_token (`str`, *optional*, defaults to `"[IMG_END]"`):
            Special token used to denote the end of an image input.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(
        self,
        image_processor=None,
        tokenizer=None,
        patch_size: int = 16,
        spatial_merge_size: int = 1,
        chat_template=None,
        image_token="[IMG]",  # set the default and let users change if they have peculiar special tokens in rare cases
        image_break_token="[IMG_BREAK]",
        image_end_token="[IMG_END]",
        **kwargs,
    ):
        self.patch_size = patch_size
        self.spatial_merge_size = spatial_merge_size
        self.image_token = image_token
        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
        self.image_break_token = image_break_token
        self.image_end_token = image_end_token
        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
        self.image_break_token_id = tokenizer.convert_tokens_to_ids(self.image_break_token)
        self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token)
        self.image_ids = [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    def __call__(
        self,
        images: Optional[ImageInput] = None,
        text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
        audio=None,
        videos=None,
        **kwargs: Unpack[PixtralProcessorKwargs],
    ) -> 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 LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to
        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
        of the above two methods for more information.

        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).
            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`.
        """

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

        patch_size = self.patch_size * self.spatial_merge_size

        if images is not None:
            image_inputs = self.image_processor(images, patch_size=patch_size, **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
            image_sizes = iter(image_inputs["image_sizes"])
            prompt_strings = []
            replace_strings = []

            for sample in text:
                while self.image_token in sample:
                    height, width = next(image_sizes)
                    num_height_tokens = height // patch_size
                    num_width_tokens = width // patch_size
                    replace_tokens = [
                        [self.image_token] * num_width_tokens + [self.image_break_token]
                    ] * num_height_tokens
                    # Flatten list
                    replace_tokens = [item for sublist in replace_tokens for item in sublist]
                    replace_tokens[-1] = self.image_end_token
                    replace_str = "".join(replace_tokens)
                    replace_strings.append(replace_str)
                    sample = sample.replace(self.image_token, "<placeholder>", 1)

                while "<placeholder>" in sample:
                    replace_str = replace_strings.pop(0)
                    sample = sample.replace("<placeholder>", replace_str, 1)
                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:
            array_ids = np.array(text_inputs["input_ids"])
            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
            mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()

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

            size = images_kwargs.get("size", None) or self.image_processor.size
            patch_size = self.patch_size * self.spatial_merge_size

            num_image_tokens = []
            for height, width in image_sizes:
                resized_height, resized_width = get_resize_output_image_size(
                    np.zeros((height, width, 3)),
                    size=(size["longest_edge"], size["longest_edge"]),
                    patch_size=(patch_size, patch_size),
                )
                num_height_tokens = resized_height // patch_size
                num_width_tokens = resized_width // patch_size
                num_image_tokens.append((num_width_tokens + 1) * num_height_tokens)

            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)

    @property
    def model_input_names(self):
        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 + ["image_sizes"]


__all__ = ["PixtralProcessor"]