Lightstar/venv/Lib/site-packages/transformers/models/instructblipvideo/processing_instructblipvideo.py

# coding=utf-8
# 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
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"""
Processor class for InstructBLIP. Largely copy of Blip2Processor with addition of a tokenizer for the Q-Former.
"""

import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import VideoInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import (
    AddedToken,
    BatchEncoding,
    PaddingStrategy,
    PreTokenizedInput,
    TextInput,
    TruncationStrategy,
)
from ...utils import TensorType, logging
from ..auto import AutoTokenizer


logger = logging.get_logger(__name__)


class InstructBlipVideoProcessor(ProcessorMixin):
    r"""
    Constructs an InstructBLIPVideo processor which wraps a InstructBLIP image processor and a LLaMa/T5 tokenizer into a single
    processor.

    [`InstructBlipVideoProcessor`] offers all the functionalities of [`InstructBlipVideoImageProcessor`] and [`AutoTokenizer`]. See the
    docstring of [`~InstructBlipVideoProcessor.__call__`] and [`~InstructBlipVideoProcessor.decode`] for more information.

    Args:
        image_processor (`InstructBlipVideoImageProcessor`):
            An instance of [`InstructBlipVideoImageProcessor`]. The image processor is a required input.
        tokenizer (`AutoTokenizer`):
            An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
        qformer_tokenizer (`AutoTokenizer`):
            An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
        num_query_tokens (`int`, *optional*):
            Number of tokens used by the Qformer as queries, should be same as in model's config.
    """

    attributes = ["image_processor", "tokenizer", "qformer_tokenizer"]
    valid_kwargs = ["num_query_tokens"]
    image_processor_class = "InstructBlipVideoImageProcessor"
    tokenizer_class = "AutoTokenizer"
    qformer_tokenizer_class = "AutoTokenizer"

    def __init__(self, image_processor, tokenizer, qformer_tokenizer, num_query_tokens=None, **kwargs):
        self.video_token = AddedToken("<video>", normalized=False, special=True)
        tokenizer.add_tokens([self.video_token], special_tokens=True)
        self.num_query_tokens = num_query_tokens
        super().__init__(image_processor, tokenizer, qformer_tokenizer)

    def __call__(
        self,
        images: VideoInput = None,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_token_type_ids: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        This method uses [`InstructBlipVideoImageProcessor.__call__`] method to prepare image(s) or video(s) for the model, and
        [`BertTokenizerFast.__call__`] to prepare text for the model.

        Please refer to the docstring of the above two methods for more information.
        """
        if images is None and text is None:
            raise ValueError("You have to specify at least one of images or text.")

        encoding = BatchFeature()

        if text is not None:
            if isinstance(text, str):
                text = [text]
            elif not isinstance(text, list) and not isinstance(text[0], str):
                raise ValueError("Invalid input text. Please provide a string, or a list of strings")

            _text_encoding = self.tokenizer(
                text=text,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=return_offsets_mapping,
                return_token_type_ids=return_token_type_ids,
                return_length=return_length,
                verbose=verbose,
                return_tensors=None,  # required to concatenate below
                **kwargs,
            )

            # if we know how many query tokens, expand text inside processor. We need this hacky manipulation
            # because BLIP expects image tokens to be at the beginning even before BOS token
            if self.num_query_tokens is not None and images is not None:
                text_encoding = {}
                video_tokens = (
                    self.video_token.content * self.num_query_tokens * 4
                )  # InstrucBLIP works with 4 frames only
                video_token_encoding = self.tokenizer(
                    [video_tokens] * len(text), add_special_tokens=False, return_tensors=None
                )
                for k in _text_encoding:
                    text_encoding[k] = [
                        img_encoding + txt_encoding
                        for img_encoding, txt_encoding in zip(video_token_encoding[k], _text_encoding[k])
                    ]
            else:
                text_encoding = _text_encoding
                if images is not None:
                    logger.warning_once(
                        "Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
                        "Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
                        "Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
                    )

            # cast to desired return tensors type after concatenating
            text_encoding = BatchEncoding(text_encoding, tensor_type=return_tensors)
            encoding.update(text_encoding)
            qformer_text_encoding = self.qformer_tokenizer(
                text=text,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=return_offsets_mapping,
                return_token_type_ids=return_token_type_ids,
                return_length=return_length,
                verbose=verbose,
                return_tensors=return_tensors,
                **kwargs,
            )
            encoding["qformer_input_ids"] = qformer_text_encoding.pop("input_ids")
            encoding["qformer_attention_mask"] = qformer_text_encoding.pop("attention_mask")

        if images is not None:
            image_encoding = self.image_processor(images, return_tensors=return_tensors)
            encoding.update(image_encoding)

        return encoding

    # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

    # overwrite to save the Q-Former tokenizer in a separate folder
    def save_pretrained(self, save_directory, **kwargs):
        if os.path.isfile(save_directory):
            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
        os.makedirs(save_directory, exist_ok=True)
        qformer_tokenizer_path = os.path.join(save_directory, "qformer_tokenizer")
        self.qformer_tokenizer.save_pretrained(qformer_tokenizer_path)

        # We modify the attributes so that only the tokenizer and image processor are saved in the main folder
        qformer_present = "qformer_tokenizer" in self.attributes
        if qformer_present:
            self.attributes.remove("qformer_tokenizer")

        outputs = super().save_pretrained(save_directory, **kwargs)

        if qformer_present:
            self.attributes += ["qformer_tokenizer"]
        return outputs

    # overwrite to load the Q-Former tokenizer from a separate folder
    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)

        # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
        if isinstance(processor, tuple):
            processor = processor[0]
        qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer")
        processor.qformer_tokenizer = qformer_tokenizer
        return processor