



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_								=     logging.get_logger(__name__)

lowercase_								=     {
    """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__):
				UpperCamelCase__				:					Dict     		="""mobilenet_v1"""




				def __init__(						self					:						Optional[int],					__lowercase					:						Tuple=3,					__lowercase					:						Optional[int]=224,					__lowercase					:						str=1.0,					__lowercase					:						List[Any]=8,					__lowercase					:						List[str]="relu6",					__lowercase					:						Optional[int]=True,					__lowercase					:						Optional[int]=0.999,					__lowercase					:						Any=0.02,					__lowercase					:						int=0.001,					**__lowercase					:						Optional[Any],					):
							super().__init__(**__lowercase						)

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

							lowercase__					  =				num_channels
							lowercase__					  =				image_size
							lowercase__					  =				depth_multiplier
							lowercase__					  =				min_depth
							lowercase__					  =				hidden_act
							lowercase__					  =				tf_padding
							lowercase__					  =				classifier_dropout_prob
							lowercase__					  =				initializer_range
							lowercase__					  =				layer_norm_eps
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Dict     		=version.parse("""1.11""")




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




				@property
				def 							A__      (						self					:						Optional[Any]						):
							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					:						Optional[Any]						):
							return 1e-4









from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def __lowerCAmelCase (     ):
			lowercase__					  =				HfArgumentParser(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			lowercase__					  =				TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_  )
			try:
						lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowercase__					  =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowercase__					  =				" ".join(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[:-1]  )
						lowercase__					  =				""
						lowercase__					  =				eval(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[-1]  )
						lowercase__					  =				[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in TensorFlowBenchmark.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]  )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE_  )
						if len(SCREAMING_SNAKE_CASE_  ) > 0:
									lowercase__					  =				full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_  )
						raise ValueError(SCREAMING_SNAKE_CASE_  )
			benchmark.run()


if __name__ == "__main__":
							main()




