
"""simple docstring"""







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

		return sequence[n]
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Dict									=      0
	__lowerCAmelCase			:						Tuple									=      2

	while digits < n:
		index += 1
		__lowerCAmelCase			:						List[str]									=      len(str(fibonacci(_UpperCamelCase		)		)		)

	return index
def     __lowerCAmelCase				(_UpperCamelCase = 1000		):
	return fibonacci_digits_index(_UpperCamelCase		)


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




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase    :List[str]     =					logging.get_logger(__name__)

_lowerCAmelCase    :Any     =					{
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''falcon'''
     a__									=['''past_key_values''']





     def __init__(					self       ,	A=6_5_0_2_4       ,	A=4_5_4_4       ,	A=3_2       ,	A=7_1       ,	A=1E-5       ,	A=0.02       ,	A=True       ,	A=0.0       ,	A=0.0       ,	A=None       ,	A=False       ,	A=False       ,	A=True       ,	A=True       ,	A=False       ,	A=1_1       ,	A=1_1       ,	**A       ,	)							->					Any:
         _UpperCAmelCase							:  int											=    vocab_size
         # Backward compatibility with n_embed kwarg
         _UpperCAmelCase							:  Optional[Any]											=    kwargs.pop('''n_embed'''       ,	A		)
         _UpperCAmelCase							:  int											=    hidden_size if n_embed is None else n_embed
         _UpperCAmelCase							:  List[str]											=    num_hidden_layers
         _UpperCAmelCase							:  Tuple											=    num_attention_heads
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_epsilon
         _UpperCAmelCase							:  Tuple											=    initializer_range
         _UpperCAmelCase							:  Optional[int]											=    use_cache
         _UpperCAmelCase							:  Any											=    hidden_dropout
         _UpperCAmelCase							:  Dict											=    attention_dropout

         _UpperCAmelCase							:  Any											=    bos_token_id
         _UpperCAmelCase							:  List[Any]											=    eos_token_id
         _UpperCAmelCase							:  Tuple											=    num_attention_heads if num_kv_heads is None else num_kv_heads
         _UpperCAmelCase							:  Dict											=    alibi
         _UpperCAmelCase							:  Optional[int]											=    new_decoder_architecture
         _UpperCAmelCase							:  str											=    multi_query  # Ignored when new_decoder_architecture is True
         _UpperCAmelCase							:  Optional[int]											=    parallel_attn
         _UpperCAmelCase							:  Optional[int]											=    bias

         super().__init__(bos_token_id=A       ,	eos_token_id=A       ,	**A		)





     @property
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return self.hidden_size // self.num_attention_heads





     @property
     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         return not self.alibi


