



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
     import tensorflow as tf


a_    :  Any						  =		logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      [
         'no_inference',
         'no_cuda',
         'no_tpu',
         'no_speed',
         'no_memory',
         'no_env_print',
         'no_multi_process',
     ]







     def __init__(self					,      **__a     ):






           '''simple docstring'''



           for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                       lowerCamelCase             =		deprecated_arg[3:]
                       lowerCamelCase             =		not kwargs.pop(__a     )
                       logger.warning(
                           F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
                           F""" {positive_arg}={kwargs[positive_arg]}"""     )
           lowerCamelCase             =		kwargs.pop("tpu_name"					,      self.tpu_name     )
           lowerCamelCase             =		kwargs.pop("device_idx"					,      self.device_idx     )
           lowerCamelCase             =		kwargs.pop("eager_mode"					,      self.eager_mode     )
           lowerCamelCase             =		kwargs.pop("use_xla"					,      self.use_xla     )
           super().__init__(**__a     )

     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Name of TPU'}							,     )
     _A      =      field(
         default=0							,     metadata={'help': 'CPU / GPU device index. Defaults to 0.'}							,     )
     _A      =      field(default=UpperCAmelCase_							,     metadata={'help': 'Benchmark models in eager model.'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={
             'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
         }							,     )







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           lowerCamelCase             =		None
           if self.tpu:
                 try:
                       if self.tpu_name:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
                       else:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                       lowerCamelCase             =		None
           return tpu







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.is_tpu:
                 tf.config.experimental_connect_to_cluster(self._setup_tpu     )
                 tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

                 lowerCamelCase             =		tf.distribute.TPUStrategy(self._setup_tpu     )
           else:
                 # currently no multi gpu is allowed
                 if self.is_gpu:
                       # TODO: Currently only single GPU is supported
                       tf.config.set_visible_devices(self.gpu_list[self.device_idx]					,      "GPU"     )
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""     )
                 else:
                       tf.config.set_visible_devices([]					,      "GPU"     )  # disable GPU
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""     )

           return strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_tpu is not None







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return tf.config.list_physical_devices("GPU"     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.cuda:
                 return len(self.gpu_list     )
           return 0







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.n_gpu > 0




def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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

            return sequence[n]

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		0
      lowerCamelCase             =		2

      while digits < n:
            index += 1
            lowerCamelCase             =		len(str(fibonacci(UpperCAmelCase__			)			)			)

      return index

def      __lowercase(						UpperCAmelCase__ = 1000			):


      """simple docstring"""



      return fibonacci_digits_index(UpperCAmelCase__			)


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