


'''simple docstring'''



from random import randint
from tempfile import TemporaryFile

import numpy as np


def 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      Optional[Any]				,       A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     0
						if start < end:
												UpperCAmelCase					    =     randint(lowerCAmelCase__				,       lowerCAmelCase__					)
												UpperCAmelCase					    =     a[end]
												UpperCAmelCase					    =     a[pivot]
												UpperCAmelCase					    =     temp

												UpperCAmelCase    ,    UpperCAmelCase					    =     _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 				lowerCamelCase__				(	A       :      Union[str, Any]				,       A       :      Optional[Any]				,       A       :      List[Any]					):




						'''simple docstring'''






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

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


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


_lowercase      :      Dict            =					0, 1  # mean and standard deviation
_lowercase      :      Tuple            =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
_lowercase      :      Optional[int]            =					np.load(outfile)
_lowercase      :      List[Any]            =					len(M) - 1
_lowercase      :      str            =					_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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase      :      Union[str, Any]            =					{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[Any]            =					["""PoolFormerFeatureExtractor"""]
	_lowercase      :      Any            =					["""PoolFormerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Tuple            =					[
	    """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """PoolFormerForImageClassification""",
	    """PoolFormerModel""",
	    """PoolFormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_poolformer import (
	    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    PoolFormerConfig,
	    PoolFormerOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_poolformer import PoolFormerFeatureExtractor
		from .image_processing_poolformer import PoolFormerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_poolformer import (
		    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PoolFormerForImageClassification,
		    PoolFormerModel,
		    PoolFormerPreTrainedModel,
		)


else:
	import sys

	_lowercase      :      Dict            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





