





"""simple docstring"""



from collections.abc import Generator







def 		__lowercase					(       )       ->Generator[int, None, None]:



    '''simple docstring'''







    __A			,	__A   :						Optional[int]									=					0, 1
    while True:
        __A			,	__A   :						str									=					b, a + b
        yield b







def 		__lowercase					(       snake_case_  :  int = 1000						)       ->int:



    '''simple docstring'''







    __A   :						Optional[Any]									=					1
    __A   :						Optional[int]									=					fibonacci_generator()
    while len(str(next(snake_case_						)						)						) < n:
        answer += 1
    return answer + 1


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











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_			=      logging.get_logger(__name__)

a_			=      {
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""dinat"""

 _lowerCamelCase				=					{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(   self ,						__lowerCamelCase=4 ,						__lowerCamelCase=3 ,						__lowerCamelCase=64 ,						__lowerCamelCase=[3, 4, 6, 5] ,						__lowerCamelCase=[2, 4, 8, 16] ,						__lowerCamelCase=7 ,						__lowerCamelCase=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] ,						__lowerCamelCase=3.0 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     __A   :						Dict									=					patch_size
     __A   :						Union[str, Any]									=					num_channels
     __A   :						str									=					embed_dim
     __A   :						Optional[Any]									=					depths
     __A   :						int									=					len(__lowerCamelCase  )
     __A   :						Union[str, Any]									=					num_heads
     __A   :						Tuple									=					kernel_size
     __A   :						Optional[int]									=					dilations
     __A   :						Tuple									=					mlp_ratio
     __A   :						Optional[int]									=					qkv_bias
     __A   :						int									=					hidden_dropout_prob
     __A   :						Dict									=					attention_probs_dropout_prob
     __A   :						int									=					drop_path_rate
     __A   :						Dict									=					hidden_act
     __A   :						Any									=					layer_norm_eps
     __A   :						Tuple									=					initializer_range
     # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     __A   :						str									=					int(embed_dim * 2 ** (len(__lowerCamelCase  ) - 1)  )
     __A   :						List[Any]									=					layer_scale_init_value
     __A   :						Any									=					['''stem'''] + [F"""stage{idx}""" for idx in range(1 ,						len(__lowerCamelCase  ) + 1  )]
     __A			,	__A   :						Union[str, Any]									=					get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase ,						out_indices=__lowerCamelCase ,						stage_names=self.stage_names  )




