
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)

lowercase  : Union[str, Any]						   =							{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Any=    "mobilenet_v2"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case=8		,snake_case=6		,snake_case=32		,snake_case=True		,snake_case=True		,snake_case="relu6"		,snake_case=True		,snake_case=0.8		,snake_case=0.02		,snake_case=0.001		,snake_case=255		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           if depth_multiplier <= 0:
                  raise ValueError("""depth_multiplier must be greater than zero."""				)

           lowercase							:					Any	     =				num_channels
           lowercase							:					Optional[int]	     =				image_size
           lowercase							:					Optional[int]	     =				depth_multiplier
           lowercase							:					Any	     =				depth_divisible_by
           lowercase							:					int	     =				min_depth
           lowercase							:					Any	     =				expand_ratio
           lowercase							:					Any	     =				output_stride
           lowercase							:					Dict	     =				first_layer_is_expansion
           lowercase							:					Tuple	     =				finegrained_output
           lowercase							:					Tuple	     =				hidden_act
           lowercase							:					List[str]	     =				tf_padding
           lowercase							:					Optional[int]	     =				classifier_dropout_prob
           lowercase							:					Optional[Any]	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps
           lowercase							:					List[str]	     =				semantic_loss_ignore_index






class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[Any]=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.task == "image-classification":
                  return OrderedDict([("""logits""", {0: """batch"""})]				)
           else:
                  return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4


from collections.abc import Generator







def 						_snake_case(  )			->   Generator[int, None, None]:
       lowercase     ,      lowercase							:					List[str]	     =				0, 1
       while True:
              lowercase     ,      lowercase							:					Optional[int]	     =				b, a + b
              yield b







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = 1_000    )			->   int:
       lowercase							:					List[Any]	     =				1
       lowercase							:					Tuple	     =				fibonacci_generator()
       while len(str(next(SCREAMING_SNAKE_CASE__    )    )    ) < n:
              answer += 1
       return answer + 1


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