processing_pix2struct.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
# limitations under the License.
"""
Processor class for Pix2Struct.
"""

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class Pix2StructProcessor(ProcessorMixin):
    r"""
    Constructs a PIX2STRUCT processor which wraps a BERT tokenizer and PIX2STRUCT image processor into a single
    processor.

    [`Pix2StructProcessor`] offers all the functionalities of [`Pix2StructImageProcessor`] and [`T5TokenizerFast`]. See
    the docstring of [`~Pix2StructProcessor.__call__`] and [`~Pix2StructProcessor.decode`] for more information.

    Args:
        image_processor (`Pix2StructImageProcessor`):
            An instance of [`Pix2StructImageProcessor`]. The image processor is a required input.
        tokenizer (Union[`T5TokenizerFast`, `T5Tokenizer`]):
            An instance of ['T5TokenizerFast`] or ['T5Tokenizer`]. The tokenizer is a required input.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "Pix2StructImageProcessor"
    tokenizer_class = ("T5Tokenizer", "T5TokenizerFast")

    def __init__(self, image_processor, tokenizer):
        tokenizer.return_token_type_ids = False
        super().__init__(image_processor, tokenizer)

    def __call__(
        self,
        images=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,
        max_patches: Optional[int] = 2048,
        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,
    ) -> BatchEncoding:
        """
        This method uses [`Pix2StructImageProcessor.preprocess`] method to prepare image(s) for the model, and
        [`T5TokenizerFast.__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 either images or text.")

        # Get only text
        if images is None and not self.image_processor.is_vqa:
            self.current_processor = self.tokenizer
            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=return_tensors,
                **kwargs,
            )
            return text_encoding

        if not self.image_processor.is_vqa:
            # add pixel_values
            encoding_image_processor = self.image_processor(
                images, return_tensors=return_tensors, max_patches=max_patches, **kwargs
            )
        else:
            # add pixel_values and bbox
            encoding_image_processor = self.image_processor(
                images, return_tensors=return_tensors, max_patches=max_patches, header_text=text, **kwargs
            )

        if text is not None and not self.image_processor.is_vqa:
            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=return_tensors,
                **kwargs,
            )

            if "attention_mask" in text_encoding:
                text_encoding["decoder_attention_mask"] = text_encoding.pop("attention_mask")
            if "input_ids" in text_encoding:
                text_encoding["decoder_input_ids"] = text_encoding.pop("input_ids")
        else:
            text_encoding = None

        if text_encoding is not None:
            encoding_image_processor.update(text_encoding)

        return encoding_image_processor

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Pix2StructTokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
        Please refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Pix2StructTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @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 list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))