






'''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_       (     __A					):




      """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      :		int  ,   *UpperCAmelCase      :		str  ,   **UpperCAmelCase      :		Optional[int]		)	->	str:


       '''simple docstring'''

       if not is_vision_available():
        raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''		)

       super().__init__(*UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		"Image"  ,   UpperCAmelCase      :		str		)	->	int:


       '''simple docstring'''

       lowercase       : int						='''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
       lowercase       : Tuple						=task_prompt.replace('''{user_input}'''  ,   UpperCAmelCase		)
       lowercase       : str						=self.pre_processor.tokenizer(
           UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase  ,   return_tensors='''pt'''		).input_ids
       lowercase       : str						=self.pre_processor(UpperCAmelCase  ,   return_tensors='''pt'''		).pixel_values

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






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       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 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		List[Any]		)	->	str:


       '''simple docstring'''

       lowercase       : List[Any]						=self.pre_processor.batch_decode(UpperCAmelCase		)[0]
       lowercase       : Dict						=sequence.replace(self.pre_processor.tokenizer.eos_token  ,   ''''''		)
       lowercase       : List[str]						=sequence.replace(self.pre_processor.tokenizer.pad_token  ,   ''''''		)
       lowercase       : Union[str, Any]						=re.sub(R'''<.*?>'''  ,   ''''''  ,   UpperCAmelCase  ,   count=1		).strip()  # remove first task start token
       lowercase       : Optional[Any]						=self.pre_processor.tokenajson(UpperCAmelCase		)

       return sequence["answer"]











'''simple docstring'''



def 				lowercase_					(     __A			:							int      )							->   int:



 """simple docstring"""




 if n == 1 or not isinstance(__A       ,   __A      ):
  return 0
 elif n == 2:
  return 1
 else:
  lowercase       : Tuple						=[0, 1]
  for i in range(2       ,   n + 1      ):
   sequence.append(sequence[i - 1] + sequence[i - 2]      )

  return sequence[n]


def 				lowercase_					(     __A			:							int      )							->   int:



 """simple docstring"""




 lowercase       : List[str]						=0
 lowercase       : str						=2

 while digits < n:
  index += 1
  lowercase       : int						=len(str(fibonacci(__A      )      )      )

 return index


def 				lowercase_					(     __A			:							int = 1_0_0_0      )							->   int:



 """simple docstring"""




 return fibonacci_digits_index(__A      )


if __name__ == "__main__":
      print(solution(int(str(input()).strip())))



