






"""simple docstring"""







from random import randint
from tempfile import TemporaryFile

import numpy as np





def 		lowercase__(A   ,						A   ,						A		)							->int:




						"""simple docstring"""



						lowercase__							: List[Any]=			0
						if start < end:
												lowercase__							: int=			randint(snake_case__   ,						snake_case__		)
												lowercase__							: Dict=			a[end]
												lowercase__							: Tuple=			a[pivot]
												lowercase__							: Tuple=			temp

												lowercase__							: Optional[Any]=			_in_place_partition(snake_case__   ,						snake_case__   ,						snake_case__		)
												count += _in_place_quick_sort(snake_case__   ,						snake_case__   ,						p - 1		)
												count += _in_place_quick_sort(snake_case__   ,						p + 1   ,						snake_case__		)
						return count





def 		lowercase__(A   ,						A   ,						A		)							->List[str]:




						"""simple docstring"""



						lowercase__							: Optional[int]=			0
						lowercase__							: Optional[Any]=			randint(snake_case__   ,						snake_case__		)
						lowercase__							: Any=			a[end]
						lowercase__							: Any=			a[pivot]
						lowercase__							: List[Any]=			temp
						lowercase__							: Tuple=			start - 1
						for index in range(snake_case__   ,						snake_case__		):
												count += 1
												if a[index] < a[end]:  # check if current val is less than pivot value
																		lowercase__							: List[Any]=			new_pivot_index + 1
																		lowercase__							: Optional[Any]=			a[new_pivot_index]
																		lowercase__							: int=			a[index]
																		lowercase__							: int=			temp

						lowercase__							: List[str]=			a[new_pivot_index + 1]
						lowercase__							: List[Any]=			a[end]
						lowercase__							: Dict=			temp
						return new_pivot_index + 1, count


a					:							List[str]    =						TemporaryFile()
a					:							Any    =						100  # 1000 elements are to be sorted


a					:							Optional[int]    =						0, 1  # mean and standard deviation
a					:							List[str]    =						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
a					:							Dict    =						np.load(outfile)
a					:							Tuple    =						len(M) - 1
a					:							int    =						_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)







print(z)










"""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


UpperCamelCase__				:Union[str, Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:Union[str, Any]			   =	{
    """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      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"mobilenet_v2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=2_24					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="relu6"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.8					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0_0_1					,				SCREAMING_SNAKE_CASE__=2_55					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

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

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






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							version.parse("1.11"		)
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



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

					"""simple docstring"""



					if self.task == "image-classification":
								return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
								return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )





		@property
		def   _UpperCamelCase( self   )      ->	float:

					"""simple docstring"""



					return 1E-4

