
'''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__ :			str											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Optional[Any]											=		{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Optional[int]   =  'data2vec-vision'





	def __init__(       self      :							Optional[Any]   ,				__magic_name__      :							Optional[int]=768   ,				__magic_name__      :							Any=12   ,				__magic_name__      :							int=12   ,				__magic_name__      :							str=3072   ,				__magic_name__      :							Optional[Any]="gelu"   ,				__magic_name__      :							Optional[int]=0.0   ,				__magic_name__      :							Optional[int]=0.0   ,				__magic_name__      :							List[Any]=0.02   ,				__magic_name__      :							Any=1E-12   ,				__magic_name__      :							Dict=224   ,				__magic_name__      :							Dict=16   ,				__magic_name__      :							List[Any]=3   ,				__magic_name__      :							Tuple=False   ,				__magic_name__      :							Optional[int]=False   ,				__magic_name__      :							Any=False   ,				__magic_name__      :							Optional[Any]=False   ,				__magic_name__      :							List[Any]=0.1   ,				__magic_name__      :							int=0.1   ,				__magic_name__      :							Tuple=True   ,				__magic_name__      :							Dict=[3, 5, 7, 11]   ,				__magic_name__      :							int=[1, 2, 3, 6]   ,				__magic_name__      :							Union[str, Any]=True   ,				__magic_name__      :							int=0.4   ,				__magic_name__      :							int=256   ,				__magic_name__      :							Union[str, Any]=1   ,				__magic_name__      :							Union[str, Any]=False   ,				__magic_name__      :							Optional[int]=255   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)

				lowerCAmelCase__										=				hidden_size
				lowerCAmelCase__										=				num_hidden_layers
				lowerCAmelCase__										=				num_attention_heads
				lowerCAmelCase__										=				intermediate_size
				lowerCAmelCase__										=				hidden_act
				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				initializer_range
				lowerCAmelCase__										=				layer_norm_eps

				lowerCAmelCase__										=				image_size
				lowerCAmelCase__										=				patch_size
				lowerCAmelCase__										=				num_channels
				lowerCAmelCase__										=				use_mask_token
				lowerCAmelCase__										=				use_absolute_position_embeddings
				lowerCAmelCase__										=				use_relative_position_bias
				lowerCAmelCase__										=				use_shared_relative_position_bias
				lowerCAmelCase__										=				layer_scale_init_value
				lowerCAmelCase__										=				drop_path_rate
				lowerCAmelCase__										=				use_mean_pooling
				# decode head attributes (semantic segmentation)
				lowerCAmelCase__										=				out_indices
				lowerCAmelCase__										=				pool_scales
				# auxiliary head attributes (semantic segmentation)
				lowerCAmelCase__										=				use_auxiliary_head
				lowerCAmelCase__										=				auxiliary_loss_weight
				lowerCAmelCase__										=				auxiliary_channels
				lowerCAmelCase__										=				auxiliary_num_convs
				lowerCAmelCase__										=				auxiliary_concat_input
				lowerCAmelCase__										=				semantic_loss_ignore_index






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Any   =  version.parse('1.11'     )





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]				)







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				return 1E-4








from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int						,__a :     Any      )     ->	int:

					"""simple docstring"""

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

										_a ,   _a      :       Optional[int]   =						_in_place_partition(__a						,__a						,__a      )
										count += _in_place_quick_sort(__a						,__a						,p - 1      )
										count += _in_place_quick_sort(__a						,p + 1						,__a      )
					return count



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Tuple						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

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

					_a      :       Union[str, Any]   =						a[new_pivot_index + 1]
					_a      :       Tuple   =						a[end]
					_a      :       Any   =						temp
					return new_pivot_index + 1, count


a__												=						TemporaryFile()
a__												=						100  # 1000 elements are to be sorted


a__						,     a__												=						0, 1  # mean and standard deviation
a__												=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a__												=						np.load(outfile)
a__												=						len(M) - 1
a__												=						_in_place_quick_sort(M, 0, r)

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







print(z)



