
'''simple docstring'''


from random import randint
from tempfile import TemporaryFile

import numpy as np




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		0
					if start < end:
										lowerCamelCase_					:      Optional[Any]     =		randint(_lowerCamelCase			,       _lowerCamelCase				)
										lowerCamelCase_					:      Dict     =		a[end]
										lowerCamelCase_					:      int     =		a[pivot]
										lowerCamelCase_					:      List[str]     =		temp

										lowerCamelCase_					:      Any     =		_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 lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Dict:
					'''simple docstring'''

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

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


__lowercase							:   Optional[Any]					=     TemporaryFile()
__lowercase							:   int					=     100  # 1000 elements are to be sorted


__lowercase				,		__lowercase							:   int					=     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							:   Any					=     np.load(outfile)
__lowercase							:   int					=     len(M) - 1
__lowercase							:   int					=     _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


__lowercase							:   str					=     {
    '''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
    '''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
    '''processing_mctct''': ['''MCTCTProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   str					=     [
					    '''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MCTCTForCTC''',
					    '''MCTCTModel''',
					    '''MCTCTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
					from .feature_extraction_mctct import MCTCTFeatureExtractor
					from .processing_mctct import MCTCTProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


