



'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
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__(      lowerCamelCase__			):
    lowercase__				=					"""naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__				=					(
        """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."""
    )
    lowercase__				=					"""document_qa"""
    lowercase__				=					AutoProcessor
    lowercase__				=					VisionEncoderDecoderModel

    lowercase__				=					["""image""", """text"""]
    lowercase__				=					["""text"""]







    def __init__(   self				:						List[Any]		,    *__snake_case				:						str		,    **__snake_case				:						List[str]			):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'			)

      super().__init__(*__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						"Image"		,    __snake_case				:						str			):
      a		:      Dict       =  '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:      Optional[Any]       =  task_prompt.replace('{user_input}'		,    __snake_case			)
      a		:      List[str]       =  self.pre_processor.tokenizer(
          __snake_case		,    add_special_tokens=__snake_case		,    return_tensors='pt'			).input_ids
      a		:      Union[str, Any]       =  self.pre_processor(__snake_case		,    return_tensors='pt'			).pixel_values

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







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						Tuple			):
      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=__snake_case		,    pad_token_id=self.pre_processor.tokenizer.pad_token_id		,    eos_token_id=self.pre_processor.tokenizer.eos_token_id		,    use_cache=__snake_case		,    num_beams=1		,    bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,    return_dict_in_generate=__snake_case		,    ).sequences







    def     lowercase_ (   self				:						str		,    __snake_case				:						List[str]			):
      a		:      str       =  self.pre_processor.batch_decode(__snake_case			)[0]
      a		:      Union[str, Any]       =  sequence.replace(self.pre_processor.tokenizer.eos_token		,    ''			)
      a		:      int       =  sequence.replace(self.pre_processor.tokenizer.pad_token		,    ''			)
      a		:      Any       =  re.sub(r'<.*?>'		,    ''		,    __snake_case		,    count=1			).strip()  # remove first task start token
      a		:      Optional[Any]       =  self.pre_processor.tokenajson(__snake_case			)

      return sequence["answer"]




'''simple docstring'''






class 							a__:







    def __init__(   self				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Any		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  name
      a		:      Dict       =  value
      a		:      List[str]       =  weight







    def __repr__(   self				:						int			):
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







    def     lowercase_ (   self				:						Optional[int]			):
      return self.value







    def     lowercase_ (   self				:						List[str]			):
      return self.name







    def     lowercase_ (   self				:						int			):
      return self.weight







    def     lowercase_ (   self				:						List[str]			):
      return self.value / self.weight
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  []
  for i in range(len(_A       )       ):
    menu.append(Things(name[i]				,    value[i]				,    weight[i]       )       )
  return menu
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  sorted(_A				,    key=_A				,    reverse=_A       )
  a		:      Optional[int]       =  []
  a     ,			a		:      str       =  0.0, 0.0
  for i in range(len(_A       )       ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]       )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)
def    lowerCamelCase__ (		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()