






from random import randint
from tempfile import TemporaryFile

import numpy as np






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     0
				if start < end:
								lowercase__  					=     randint(lowerCamelCase_						,  lowerCamelCase_							)
								lowercase__  					=     a[end]
								lowercase__  					=     a[pivot]
								lowercase__  					=     temp

								lowercase__					,		lowercase__  					=     _in_place_partition(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)
								count += _in_place_quick_sort(lowerCamelCase_						,  lowerCamelCase_						,  p - 1							)
								count += _in_place_quick_sort(lowerCamelCase_						,  p + 1						,  lowerCamelCase_							)
				return count






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     0
				lowercase__  					=     randint(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     a[end]
				lowercase__  					=     a[pivot]
				lowercase__  					=     temp
				lowercase__  					=     start - 1
				for index in range(lowerCamelCase_						,  lowerCamelCase_							):
								count += 1
								if a[index] < a[end]:  # check if current val is less than pivot value
												lowercase__  					=     new_pivot_index + 1
												lowercase__  					=     a[new_pivot_index]
												lowercase__  					=     a[index]
												lowercase__  					=     temp

				lowercase__  					=     a[new_pivot_index + 1]
				lowercase__  					=     a[end]
				lowercase__  					=     temp
				return new_pivot_index + 1, count


A__      :     Dict       =					TemporaryFile()
A__      :     Union[str, Any]       =					1_00  # 1000 elements are to be sorted


A__  ,  A__      :     Optional[Any]       =					0, 1  # mean and standard deviation
A__      :     Optional[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__      :     List[str]       =					np.load(outfile)
A__      :     Any       =					len(M) - 1
A__      :     Any       =					_in_place_quick_sort(M, 0, r)

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




print(z)












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


A__      :     Tuple       =					logging.get_logger(__name__)
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			"""upernet"""




				def __init__(	self		:       Dict,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       str=512,    lowerCamelCase		:       Optional[Any]=0.02,    lowerCamelCase		:       Optional[Any]=[1, 2, 3, 6],    lowerCamelCase		:       Optional[int]=True,    lowerCamelCase		:       Tuple=0.4,    lowerCamelCase		:       Optional[int]=384,    lowerCamelCase		:       Optional[int]=256,    lowerCamelCase		:       Dict=1,    lowerCamelCase		:       str=False,    lowerCamelCase		:       List[str]=255,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase    )

								if backbone_config is None:
												logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''    )
												lowercase__  					=     CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
								elif isinstance(lowerCamelCase,    lowerCamelCase    ):
												lowercase__  					=     backbone_config.get('''model_type'''    )
												lowercase__  					=     CONFIG_MAPPING[backbone_model_type]
												lowercase__  					=     config_class.from_dict(lowerCamelCase    )

								lowercase__  					=     backbone_config
								lowercase__  					=     hidden_size
								lowercase__  					=     initializer_range
								lowercase__  					=     pool_scales
								lowercase__  					=     use_auxiliary_head
								lowercase__  					=     auxiliary_loss_weight
								lowercase__  					=     auxiliary_in_channels
								lowercase__  					=     auxiliary_channels
								lowercase__  					=     auxiliary_num_convs
								lowercase__  					=     auxiliary_concat_input
								lowercase__  					=     loss_ignore_index




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     copy.deepcopy(self.__dict__    )
								lowercase__  					=     self.backbone_config.to_dict()
								lowercase__  					=     self.__class__.model_type
								return output




