"""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__:int		       =       logging.get_logger(__name__)

SCREAMING_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__	(    __SCREAMING_SNAKE_CASE    ):
 _snake_case    : Optional[int]				   =		'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__(**_a		)

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

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






class       snake_case__	(    __SCREAMING_SNAKE_CASE    ):
 _snake_case    : Tuple				   =		version.parse("""1.11"""    )


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


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




"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			set()

		# Replace all the whitespace in our sentence
		__a           =			input_str.replace(''' '''    ,					''''''			)
		for alpha in input_str:
				if "a" <= alpha.lower() <= "z":
						frequency.add(alpha.lower()			)
		return len(lowerCAmelCase__			) == 26

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			[False] * 26
		for char in input_str:
				if char.islower():
						__a           =			True
				elif char.isupper():
						__a           =			True
		return all(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def        lowercase							(		) ->      None:
		from timeit import timeit

		__a           =			'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
		print(timeit('''is_pangram()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_faster()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_fastest()'''    ,					setup=lowerCAmelCase__			)			)
		# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
		# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()






