


'''simple docstring'''






from random import randint
from tempfile import TemporaryFile

import numpy as np







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       Dict							,   A__ :       Optional[int] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       0
  if start < end:
    lowerCAmelCase_      :				int      =       randint(A__							,   A__ )
    lowerCAmelCase_      :				Any      =       a[end]
    lowerCAmelCase_      :				Optional[Any]      =       a[pivot]
    lowerCAmelCase_      :				int      =       temp

    lowerCAmelCase_,							lowerCAmelCase_      :				int      =       _in_place_partition(A__							,   A__							,   A__ )
    count += _in_place_quick_sort(A__							,   A__							,   p - 1 )
    count += _in_place_quick_sort(A__							,   p + 1							,   A__ )
  return count







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       str							,   A__ :       int ):
  '''simple docstring'''





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

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


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


__A							,			__A :  List[str]    							=				0, 1  # mean and standard deviation
__A :  Optional[int]    							=				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
__A :  List[Any]    							=				np.load(outfile)
__A :  str    							=				len(M) - 1
__A :  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_torch_available


__A :  List[str]    							=				{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BigBirdPegasusForCausalLM",
      "BigBirdPegasusForConditionalGeneration",
      "BigBirdPegasusForQuestionAnswering",
      "BigBirdPegasusForSequenceClassification",
      "BigBirdPegasusModel",
      "BigBirdPegasusPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BigBirdPegasusConfig,
      BigBirdPegasusOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        BigBirdPegasusForCausalLM,
        BigBirdPegasusForConditionalGeneration,
        BigBirdPegasusForQuestionAnswering,
        BigBirdPegasusForSequenceClassification,
        BigBirdPegasusModel,
        BigBirdPegasusPreTrainedModel,
    )


else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



