


from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """pegasus"""
     _lowerCamelCase   =      ["""past_key_values"""]
     _lowerCamelCase   =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


     def __init__(	self  ,       __A=50265  ,       __A=1024  ,       __A=12  ,       __A=4096  ,       __A=16  ,       __A=12  ,       __A=4096  ,       __A=16  ,       __A=0.0  ,       __A=0.0  ,       __A=True  ,       __A=True  ,       __A="gelu"  ,       __A=1024  ,       __A=0.1  ,       __A=0.0  ,       __A=0.0  ,       __A=0.02  ,       __A=0  ,       __A=False  ,       __A=0  ,       __A=1  ,       __A=1  ,       **__A  ,       ):
        __a					=  vocab_size
        __a					=  max_position_embeddings
        __a					=  d_model
        __a					=  encoder_ffn_dim
        __a					=  encoder_layers
        __a					=  encoder_attention_heads
        __a					=  decoder_ffn_dim
        __a					=  decoder_layers
        __a					=  decoder_attention_heads
        __a					=  dropout
        __a					=  attention_dropout
        __a					=  activation_dropout
        __a					=  activation_function
        __a					=  init_std
        __a					=  encoder_layerdrop
        __a					=  decoder_layerdrop
        __a					=  use_cache
        __a					=  encoder_layers
        __a					=  scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=__A  ,       eos_token_id=__A  ,       is_encoder_decoder=__A  ,       decoder_start_token_id=__A  ,       forced_eos_token_id=__A  ,       **__A  ,       )


     @property
     def 	snake_case_  (	self    ):
        return self.encoder_attention_heads


     @property
     def 	snake_case_  (	self    ):
        return self.d_model









'''simple docstring'''






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      if n == 1 or not isinstance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		):
            return 0
      elif n == 2:
            return 1
      else:
            snake_case__       :   int	      =    [0, 1]
            for i in range(2				,      n + 1		):
                  sequence.append(sequence[i - 1] + sequence[i - 2]		)

            return sequence[n]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Optional[int]	      =    0
      snake_case__       :   Dict	      =    2

      while digits < n:
            index += 1
            snake_case__       :   Optional[int]	      =    len(str(fibonacci(__SCREAMING_SNAKE_CASE		)		)		)

      return index






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE = 1000		)    ->   int:
      return fibonacci_digits_index(__SCREAMING_SNAKE_CASE		)


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