



"""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


_lowercase			:			Optional[Any]										=						logging.get_logger(__name__)

_lowercase			:			List[str]										=						{
    "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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'mobilenet_v2'



					def __init__(	self							:  str,  lowerCamelCase							:  Dict=3,  lowerCamelCase							:  str=224,  lowerCamelCase							:  Optional[Any]=1.0,  lowerCamelCase							:  Optional[Any]=8,  lowerCamelCase							:  Any=8,  lowerCamelCase							:  Tuple=6,  lowerCamelCase							:  Dict=32,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Optional[int]="relu6",  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Any=0.8,  lowerCamelCase							:  Union[str, Any]=0.02,  lowerCamelCase							:  List[Any]=0.001,  lowerCamelCase							:  str=255,  **lowerCamelCase							:  Any,  )-> Dict:
								super().__init__(**lowerCamelCase					)

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

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





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							version.parse('1.11'       )



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



					@property
					def 		snake_case	(	self							:  List[Any]					)-> 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 		snake_case	(	self							:  Tuple					)-> float:
								return 1E-4









"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			return int((input_a, input_a).count(0     ) != 0     )






def 					snake_case__       (		):
			"""simple docstring"""
			assert nand_gate(0							,		0     ) == 1
			assert nand_gate(0							,		1     ) == 1
			assert nand_gate(1							,		0     ) == 1
			assert nand_gate(1							,		1     ) == 0


if __name__ == "__main__":
		print(nand_gate(0, 0))
		print(nand_gate(0, 1))
		print(nand_gate(1, 0))
		print(nand_gate(1, 1))




