




from random import randint
from tempfile import TemporaryFile

import numpy as np







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

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

              A__					,				A__ 					=       _in_place_partition(lowercase_					,							lowercase_					,							lowercase_		)
              count += _in_place_quick_sort(lowercase_					,							lowercase_					,							p - 1		)
              count += _in_place_quick_sort(lowercase_					,							p + 1					,							lowercase_		)
       return count







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Any:



       """simple docstring"""

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

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


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


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

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





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_							:					int		 =   {
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''InformerForPrediction''',
			    '''InformerModel''',
			    '''InformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_informer import (
						    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InformerForPrediction,
						    InformerModel,
						    InformerPreTrainedModel,
						)

else:
			import sys

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





