





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

  return sequence[n]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   int:
 __lowerCAmelCase					:	List[Any]      =       0
 __lowerCAmelCase					:	Optional[int]      =       2

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

 return index






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int = 1_000   )      ->   int:
 return fibonacci_digits_index(SCREAMING_SNAKE_CASE   )


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






import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class        snake_case_    (		unittest.TestCase						):






  def __init__(							self			:					List[Any]       ,  _snake_case			:					List[Any]       ,  _snake_case			:					str=13       ,  _snake_case			:					int=30       ,  _snake_case			:					str=2       ,  _snake_case			:					int=3       ,  _snake_case			:					Optional[Any]=True       ,  _snake_case			:					str=True       ,  _snake_case			:					Optional[int]=32       ,  _snake_case			:					Dict=5       ,  _snake_case			:					Optional[int]=4       ,  _snake_case			:					List[Any]=37       ,  _snake_case			:					Union[str, Any]="gelu"       ,  _snake_case			:					str=0.1       ,  _snake_case			:					str=0.1       ,  _snake_case			:					str=10       ,  _snake_case			:					Any=0.02       ,  )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       parent
   __lowerCAmelCase					:	Any      =       batch_size
   __lowerCAmelCase					:	int      =       image_size
   __lowerCAmelCase					:	int      =       patch_size
   __lowerCAmelCase					:	List[Any]      =       num_channels
   __lowerCAmelCase					:	str      =       is_training
   __lowerCAmelCase					:	str      =       use_labels
   __lowerCAmelCase					:	List[str]      =       hidden_size
   __lowerCAmelCase					:	Dict      =       num_hidden_layers
   __lowerCAmelCase					:	List[str]      =       num_attention_heads
   __lowerCAmelCase					:	Any      =       intermediate_size
   __lowerCAmelCase					:	List[str]      =       hidden_act
   __lowerCAmelCase					:	List[str]      =       hidden_dropout_prob
   __lowerCAmelCase					:	Any      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	Tuple      =       type_sequence_label_size
   __lowerCAmelCase					:	Union[str, Any]      =       initializer_range

   # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   __lowerCAmelCase					:	Tuple      =       (image_size // patch_size) ** 2
   __lowerCAmelCase					:	Optional[Any]      =       num_patches + 1






  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

   __lowerCAmelCase					:	Tuple      =       ViTConfig(
       image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  hidden_size=self.hidden_size       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  is_decoder=_snake_case       ,  initializer_range=self.initializer_range       ,  )

   return config, pixel_values






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  _snake_case			:					List[str]       ,  _snake_case			:					List[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       FlaxViTModel(config=_snake_case      )
   __lowerCAmelCase					:	int      =       model(_snake_case      )
   # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
   __lowerCAmelCase					:	Dict      =       (self.image_size, self.image_size)
   __lowerCAmelCase					:	Any      =       (self.patch_size, self.patch_size)
   __lowerCAmelCase					:	Any      =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
   self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, num_patches + 1, self.hidden_size)      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					str       ,  _snake_case			:					List[Any]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.type_sequence_label_size
   __lowerCAmelCase					:	Tuple      =       FlaxViTForImageClassification(config=_snake_case      )
   __lowerCAmelCase					:	List[str]      =       model(_snake_case      )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)      )

   # test greyscale images
   __lowerCAmelCase					:	str      =       1
   __lowerCAmelCase					:	Any      =       FlaxViTForImageClassification(_snake_case      )

   __lowerCAmelCase					:	int      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
   __lowerCAmelCase					:	Dict      =       model(_snake_case      )






  def 				UpperCAmelCase__						(							self			:					str      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.prepare_config_and_inputs()
   (
       (
       __lowerCAmelCase
   ) ,					(
       __lowerCAmelCase
   ) ,					
   )					:	Union[str, Any]      =       config_and_inputs
   __lowerCAmelCase					:	str      =       {"""pixel_values""": pixel_values}
   return config, inputs_dict






@require_flax
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






  def 				UpperCAmelCase__						(							self			:					str      )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       FlaxViTModelTester(self      )
   __lowerCAmelCase					:	Optional[Any]      =       ConfigTester(self       ,  config_class=_snake_case       ,  has_text_modality=_snake_case       ,  hidden_size=37      )






  def 				UpperCAmelCase__						(							self			:					Any      )->Dict:






   '''simple docstring'''

   self.config_tester.run_common_tests()






  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_snake_case      )






  def 				UpperCAmelCase__						(							self			:					int      )->int:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowerCAmelCase					:	int      =       model_class(_snake_case      )
    __lowerCAmelCase					:	Optional[int]      =       inspect.signature(model.__call__      )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowerCAmelCase					:	Tuple      =       [*signature.parameters.keys()]

    __lowerCAmelCase					:	str      =       ["""pixel_values"""]
    self.assertListEqual(arg_names[:1]       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					str      )->str:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__      ):
     __lowerCAmelCase					:	Optional[int]      =       self._prepare_for_class(_snake_case       ,  _snake_case      )
     __lowerCAmelCase					:	List[str]      =       model_class(_snake_case      )

     @jax.jit
     def model_jitted(_snake_case			:					Dict       ,  **_snake_case			:					Union[str, Any]      ):
      return model(pixel_values=_snake_case       ,  **_snake_case      )

     with self.subTest("""JIT Enabled"""      ):
      __lowerCAmelCase					:	List[Any]      =       model_jitted(**_snake_case      ).to_tuple()

     with self.subTest("""JIT Disabled"""      ):
      with jax.disable_jit():
       __lowerCAmelCase					:	str      =       model_jitted(**_snake_case      ).to_tuple()

     self.assertEqual(len(_snake_case      )       ,  len(_snake_case      )      )
     for jitted_output, output in zip(_snake_case       ,  _snake_case      ):
      self.assertEqual(jitted_output.shape       ,  output.shape      )






  @slow
  def 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   for model_class_name in self.all_model_classes:
    __lowerCAmelCase					:	List[str]      =       model_class_name.from_pretrained("""google/vit-base-patch16-224"""      )
    __lowerCAmelCase					:	List[str]      =       model(np.ones((1, 3, 224, 224)      )      )
    self.assertIsNotNone(_snake_case      )