





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

a		:				Union[str, Any]  			=    {
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         ="""convnextv2"""






							def __init__(     self   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=1E-12   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=224   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								super().__init__(**lowerCAmelCase__   )

								a   :	List[Any]			=       num_channels
								a   :	str			=       patch_size
								a   :	Union[str, Any]			=       num_stages
								a   :	List[Any]			=       [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
								a   :	str			=       [3, 3, 9, 3] if depths is None else depths
								a   :	int			=       hidden_act
								a   :	str			=       initializer_range
								a   :	List[str]			=       layer_norm_eps
								a   :	Optional[int]			=       drop_path_rate
								a   :	Dict			=       image_size
								a   :	Any			=       ["stem"] + [f"""stage{idx}""" for idx in range(1   ,      len(self.depths   ) + 1   )]
								a, a   :	Optional[Any]			=       get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase__   ,      out_indices=lowerCAmelCase__   ,      stage_names=self.stage_names   )









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






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

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


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


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

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







print(z)


