


'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				int				):







   '''simple docstring'''




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

      return sequence[n]





def lowerCamelCase__     (		__lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  int				       =0
   _UpperCAmelCase  :  Any				       =2

   while digits < n:
      index += 1
      _UpperCAmelCase  :  int				       =len(str(fibonacci(__lowerCamelCase				)				)				)

   return index





def lowerCamelCase__     (		__lowerCamelCase			:				int = 1_0_0_0				):







   '''simple docstring'''




   return fibonacci_digits_index(__lowerCamelCase				)


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





'''simple docstring'''






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase				=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase							)
class    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				*snake_case    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          super().__init__(*snake_case    ,				**snake_case)
          requires_backends(self    ,				'vision')
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)






       def      lowerCAmelCase						(     self    ,				snake_case=None)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ={}
          if top_k is not None:
             _UpperCAmelCase  :  Optional[int]				       =top_k
          return {}, {}, postprocess_params






       def __call__(     self    ,				snake_case    ,				**snake_case)		->    List[str]:




          '''simple docstring'''
          return super().__call__(snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =load_image(snake_case)
          _UpperCAmelCase  :  Tuple				       =self.image_processor(images=snake_case    ,				return_tensors=self.framework)
          return model_inputs






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self.model(**snake_case)
          return model_outputs






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=5)		->    Any:




          '''simple docstring'''
          if top_k > self.model.config.num_labels:
             _UpperCAmelCase  :  Optional[Any]				       =self.model.config.num_labels

          if self.framework == "pt":
             _UpperCAmelCase  :  List[Any]				       =model_outputs.logits.softmax(-1)[0]
             _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =probs.topk(snake_case)
          elif self.framework == "tf":
             _UpperCAmelCase  :  int				       =stable_softmax(model_outputs.logits    ,				axis=-1)[0]
             _UpperCAmelCase  :  List[Any]				       =tf.math.top_k(snake_case    ,				k=snake_case)
             _UpperCAmelCase       ,			_UpperCAmelCase  :  List[str]				       =topk.values.numpy(), topk.indices.numpy()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

          _UpperCAmelCase  :  int				       =scores.tolist()
          _UpperCAmelCase  :  Tuple				       =ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case    ,				snake_case)]

