





from random import randint
from tempfile import TemporaryFile

import numpy as np




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Tuple	=		0
       if start < end:
              _lowerCamelCase							:     int	=		randint(lowercase_ ,    lowercase_      )
              _lowerCamelCase							:     Optional[int]	=		a[end]
              _lowerCamelCase							:     Optional[Any]	=		a[pivot]
              _lowerCamelCase							:     Optional[Any]	=		temp

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

       _lowerCamelCase							:     Dict	=		a[new_pivot_index + 1]
       _lowerCamelCase							:     Tuple	=		a[end]
       _lowerCamelCase							:     Tuple	=		temp
       return new_pivot_index + 1, count


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


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


outfile.seek(0)  # using the same array
_lowerCamelCase			       =      np.load(outfile)
_lowerCamelCase			       =      len(M) - 1
_lowerCamelCase			       =      _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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

