





'''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 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Optional[Any]			=  '''naver-clova-ix/donut-base-finetuned-docvqa'''
      _lowercase:     Tuple			=  (
          '''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:     List[Any]			=  '''document_qa'''
      _lowercase:     int			=  AutoProcessor
      _lowercase:     Optional[Any]			=  VisionEncoderDecoderModel

      _lowercase:     List[str]			=  ['''image''', '''text''']
      _lowercase:     Optional[int]			=  ['''text''']




      def __init__(     self     :					Any     ,     *__snake_case     :					Optional[int]     ,     **__snake_case     :					Dict    )							-> int:
            if not is_vision_available():
                  raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""    )

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




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"Image"     ,     __snake_case     :					str    )							-> Any:
            _lowerCAmelCase					=				"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
            _lowerCAmelCase					=				task_prompt.replace("""{user_input}"""     ,     __snake_case    )
            _lowerCAmelCase					=				self.pre_processor.tokenizer(
                __snake_case     ,     add_special_tokens=__snake_case     ,     return_tensors="""pt"""    ).input_ids
            _lowerCAmelCase					=				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     :					Optional[int]    )							-> 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=__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]    )							-> List[str]:
            _lowerCAmelCase					=				self.pre_processor.batch_decode(__snake_case    )[0]
            _lowerCAmelCase					=				sequence.replace(self.pre_processor.tokenizer.eos_token     ,     """"""    )
            _lowerCAmelCase					=				sequence.replace(self.pre_processor.tokenizer.pad_token     ,     """"""    )
            _lowerCAmelCase					=				re.sub(R"""<.*?>"""     ,     """"""     ,     __snake_case     ,     count=1    ).strip()  # remove first task start token
            _lowerCAmelCase					=				self.pre_processor.tokenajson(__snake_case    )

            return sequence["answer"]













'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if divisor % 5 == 0 or divisor % 2 == 0:
            return 0
      _lowerCAmelCase					=				1
      _lowerCAmelCase					=				1
      while repunit:
            _lowerCAmelCase					=				(10 * repunit + 1) % divisor
            repunit_index += 1
      return repunit_index
def   UpperCamelCase__ (						lowerCAmelCase = 1_00_00_00    ):





      """simple docstring"""


      _lowerCAmelCase					=				limit - 1
      if divisor % 2 == 0:
            divisor += 1
      while least_divisible_repunit(lowerCAmelCase    ) <= limit:
            divisor += 2
      return divisor


if __name__ == "__main__":
      print(F"""{solution() = }""")






