


from random import randint
from tempfile import TemporaryFile

import numpy as np


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				int		     =					0
  if start < end:
    A_      :				Dict		     =					randint(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    A_      :				Optional[int]		     =					a[end]
    A_      :				Dict		     =					a[pivot]
    A_      :				int		     =					temp

    A_     ,       A_      :				Optional[int]		     =					_in_place_partition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    count += _in_place_quick_sort(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			p - 1       )
    count += _in_place_quick_sort(SCREAMING_SNAKE_CASE ,			p + 1 ,			SCREAMING_SNAKE_CASE       )
  return count


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

  A_      :				Any		     =					a[new_pivot_index + 1]
  A_      :				Optional[Any]		     =					a[end]
  A_      :				Union[str, Any]		     =					temp
  return new_pivot_index + 1, count


UpperCamelCase								=	TemporaryFile()
UpperCamelCase								=	100  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
UpperCamelCase								=	np.load(outfile)
UpperCamelCase								=	len(M) - 1
UpperCamelCase								=	_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

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


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """NezhaForNextSentencePrediction""",
         """NezhaForMaskedLM""",
         """NezhaForPreTraining""",
         """NezhaForMultipleChoice""",
         """NezhaForQuestionAnswering""",
         """NezhaForSequenceClassification""",
         """NezhaForTokenClassification""",
         """NezhaModel""",
         """NezhaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nezha import (
              NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
              NezhaModel,
              NezhaPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




