





'''simple docstring'''

import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
      from PIL import Image


class    a_	(							snake_case_			):

    '''simple docstring'''
    UpperCamelCase					       =					"""naver-clova-ix/donut-base-finetuned-docvqa"""
    UpperCamelCase					       =					(
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    UpperCamelCase					       =					"""document_qa"""
    UpperCamelCase					       =					AutoProcessor
    UpperCamelCase					       =					VisionEncoderDecoderModel

    UpperCamelCase					       =					["""image""", """text"""]
    UpperCamelCase					       =					["""text"""]


    def __init__(						self					,						*A					,						**A			)						->  int:
        if not is_vision_available():
            raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""			)

        super().__init__(*UpperCamelCase_					,						**UpperCamelCase_			)


    def      snake_case_(						self					,						A					,						A			)						->  List[str]:
        _SCREAMING_SNAKE_CASE   							=     '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
        _SCREAMING_SNAKE_CASE   							=     task_prompt.replace("""{user_input}"""					,						UpperCamelCase_			)
        _SCREAMING_SNAKE_CASE   							=     self.pre_processor.tokenizer(
            UpperCamelCase_					,						add_special_tokens=UpperCamelCase_					,						return_tensors="""pt"""			).input_ids
        _SCREAMING_SNAKE_CASE   							=     self.pre_processor(UpperCamelCase_					,						return_tensors="""pt"""			).pixel_values

        return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}


    def      snake_case_(						self					,						A			)						->  List[Any]:
        return self.model.generate(
            inputs["""pixel_values"""].to(self.device			)					,						decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device			)					,						max_length=self.model.decoder.config.max_position_embeddings					,						early_stopping=UpperCamelCase_					,						pad_token_id=self.pre_processor.tokenizer.pad_token_id					,						eos_token_id=self.pre_processor.tokenizer.eos_token_id					,						use_cache=UpperCamelCase_					,						num_beams=1					,						bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]					,						return_dict_in_generate=UpperCamelCase_					,						).sequences





    def      snake_case_(						self					,						A			)						->  str:
        _SCREAMING_SNAKE_CASE   							=     self.pre_processor.batch_decode(UpperCamelCase_			)[0]
        _SCREAMING_SNAKE_CASE   							=     sequence.replace(self.pre_processor.tokenizer.eos_token					,						""""""			)
        _SCREAMING_SNAKE_CASE   							=     sequence.replace(self.pre_processor.tokenizer.pad_token					,						""""""			)
        _SCREAMING_SNAKE_CASE   							=     re.sub(R"""<.*?>"""					,						""""""					,						UpperCamelCase_					,						count=1			).strip()  # remove first task start token
        _SCREAMING_SNAKE_CASE   							=     self.pre_processor.tokenajson(UpperCamelCase_			)

        return sequence["answer"]





"""simple docstring"""



import warnings
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 				a			(					lowercase      ):
      UpperCamelCase							:   Tuple           =     ["""image_processor""", """tokenizer"""]
      UpperCamelCase							:   List[str]           =     """LayoutLMv2ImageProcessor"""
      UpperCamelCase							:   Optional[int]           =     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'     ,	UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Tuple     							=					kwargs.pop('feature_extractor'      )

          UpperCAmelCase__	:      Optional[Any]     							=					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.'      )
          if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.'      )

          super().__init__(UpperCamelCase_     ,	UpperCamelCase_      )



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  'You cannot provide bounding boxes '
                  'if you initialized the image processor with apply_ocr set to True.'      )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'      )

          # first, apply the image processor
          UpperCAmelCase__	:      List[Any]     							=					self.image_processor(images=UpperCamelCase_     ,	return_tensors=UpperCamelCase_      )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
                  UpperCAmelCase__	:      Any     							=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              UpperCAmelCase__	:      Union[str, Any]     							=					features['words']

          UpperCAmelCase__	:      List[Any]     							=					self.tokenizer(
              text=text if text is not None else features['words']     ,	text_pair=text_pair if text_pair is not None else None     ,	boxes=boxes if boxes is not None else features['boxes']     ,	word_labels=UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=UpperCamelCase_     ,	stride=UpperCamelCase_     ,	pad_to_multiple_of=UpperCamelCase_     ,	return_token_type_ids=UpperCamelCase_     ,	return_attention_mask=UpperCamelCase_     ,	return_overflowing_tokens=UpperCamelCase_     ,	return_special_tokens_mask=UpperCamelCase_     ,	return_offsets_mapping=UpperCamelCase_     ,	return_length=UpperCamelCase_     ,	verbose=UpperCamelCase_     ,	return_tensors=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          # add pixel values
          UpperCAmelCase__	:      List[Any]     							=					features.pop('pixel_values'      )
          if return_overflowing_tokens is True:
              UpperCAmelCase__	:      int     							=					self.get_overflowing_images(UpperCamelCase_     ,	encoded_inputs['overflow_to_sample_mapping']      )
          UpperCAmelCase__	:      Dict     							=					images

          return encoded_inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase__	:      Dict     							=					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]      )

          if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
              raise ValueError(
                  'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                  F''' {len(UpperCamelCase_      )} and {len(UpperCamelCase_      )}'''      )

          return images_with_overflow



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      @property
      def  __snake_case							(   self      ):
          return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor_class



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor

