

from random import randint
from tempfile import TemporaryFile

import numpy as np
def 						__lowerCamelCase	(       __snake_case					:		Dict,		__snake_case					:		Dict,		__snake_case					:		Union[str, Any]	)	->							str:






						"""simple docstring"""





						A__					:       List[str]     =0
						if start < end:
												A__					:       Any     =randint(__snake_case,		__snake_case	)
												A__					:       List[Any]     =a[end]
												A__					:       Union[str, Any]     =a[pivot]
												A__					:       Union[str, Any]     =temp

												A__					:       Optional[int]     =_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 						__lowerCamelCase	(       __snake_case					:		Tuple,		__snake_case					:		Union[str, Any],		__snake_case					:		int	)	->							Optional[Any]:






						"""simple docstring"""





						A__					:       List[str]     =0
						A__					:       Tuple     =randint(__snake_case,		__snake_case	)
						A__					:       Tuple     =a[end]
						A__					:       Dict     =a[pivot]
						A__					:       Union[str, Any]     =temp
						A__					:       str     =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
																		A__					:       Dict     =new_pivot_index + 1
																		A__					:       Dict     =a[new_pivot_index]
																		A__					:       Dict     =a[index]
																		A__					:       str     =temp

						A__					:       Optional[int]     =a[new_pivot_index + 1]
						A__					:       str     =a[end]
						A__					:       str     =temp
						return new_pivot_index + 1, count


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


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


outfile.seek(0)  # using the same array
__snake_case			:     str										=      np.load(outfile)
__snake_case			:     Optional[Any]										=      len(M) - 1
__snake_case			:     Optional[Any]										=      _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_speech_available, is_torch_available


__snake_case			:     Any										=      {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     Optional[int]										=      [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     List[Any]										=      ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

		__snake_case			:     Optional[Any]										=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

