


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


snake_case				:    str       				=     logging.get_logger(__name__)

snake_case				:    Optional[int]       				=     {
    '''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  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'mobilenet_v1'




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

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

       a						:Optional[Any] 							=       num_channels
       a						:Any 							=       image_size
       a						:int 							=       depth_multiplier
       a						:Dict 							=       min_depth
       a						:Union[str, Any] 							=       hidden_act
       a						:List[str] 							=       tf_padding
       a						:Union[str, Any] 							=       classifier_dropout_prob
       a						:Optional[Any] 							=       initializer_range
       a						:str 							=       layer_norm_eps







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       version.parse('1.11'							)




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




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       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    ):
       return 1e-4











from math import pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

