

'''simple docstring'''

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


SCREAMING_SNAKE_CASE							:				Any     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			{
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """mobilenet_v1"""



				def __init__(    self,     _lowercase=3,     _lowercase=224,     _lowercase=1.0,     _lowercase=8,     _lowercase="relu6",     _lowercase=True,     _lowercase=0.999,     _lowercase=0.02,     _lowercase=0.001,     **_lowercase,     )     ->							str:
											super().__init__(**_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				num_channels
											SCREAMING_SNAKE_CASE_				=				image_size
											SCREAMING_SNAKE_CASE_				=				depth_multiplier
											SCREAMING_SNAKE_CASE_				=				min_depth
											SCREAMING_SNAKE_CASE_				=				hidden_act
											SCREAMING_SNAKE_CASE_				=				tf_padding
											SCREAMING_SNAKE_CASE_				=				classifier_dropout_prob
											SCREAMING_SNAKE_CASE_				=				initializer_range
											SCREAMING_SNAKE_CASE_				=				layer_norm_eps






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     version.parse("""1.11"""		)



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											return OrderedDict([('pixel_values', {0: 'batch'})]       )



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





				@property
				def   a__					(    self       )     ->							float:
											return 1E-4








'''simple docstring'''

from math import factorial




def 							_UpperCamelCase				(   lowerCAmelCase__:					int = 100   )      -> int:
							return sum(map(lowerCAmelCase__		,str(factorial(lowerCAmelCase__   )   )   )   )


if __name__ == "__main__":
							print(solution(int(input("Enter the Number: ").strip())))





