
'''simple docstring'''
from collections.abc import Generator







def 		A_(     ):
							UpperCamelCase			,	UpperCamelCase   			=     0, 1
							while True:
														UpperCamelCase			,	UpperCamelCase   			=     b, a + b
														yield b







def 		A_(     A						:		int = 1000):
							UpperCamelCase   			=     1
							UpperCamelCase   			=     fibonacci_generator()
							while len(str(next(A))) < n:
														answer += 1
							return answer + 1


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








"""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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    '''openai/whisper-base'''
   __UpperCAmelCase					:			Any				    =    (
       '''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
       '''transcribed text.'''
   )
   __UpperCAmelCase					:			Tuple				    =    '''transcriber'''
   __UpperCAmelCase					:			Union[str, Any]				    =    WhisperProcessor
   __UpperCAmelCase					:			str				    =    WhisperForConditionalGeneration

   __UpperCAmelCase					:			int				    =    ['''audio''']
   __UpperCAmelCase					:			Optional[int]				    =    ['''text''']







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       return self.pre_processor(UpperCamelCase__		,      return_tensors="pt"		).input_features







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       return self.model.generate(inputs=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       return self.pre_processor.batch_decode(UpperCamelCase__		,      skip_special_tokens=UpperCamelCase__		)[0]





