




"""simple docstring"""


from random import randint
from tempfile import TemporaryFile

import numpy as np


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      str	,     snake_case__							:      Union[str, Any]					):
  """simple docstring"""

  _snake_case							:	List[str]			=						0
  if start < end:
    _snake_case							:	Tuple			=						randint(snake_case__	,     snake_case__					)
    _snake_case							:	Tuple			=						a[end]
    _snake_case							:	Optional[Any]			=						a[pivot]
    _snake_case							:	Optional[Any]			=						temp

    _snake_case	,      _snake_case							:	Dict			=						_in_place_partition(snake_case__	,     snake_case__	,     snake_case__					)
    count += _in_place_quick_sort(snake_case__	,     snake_case__	,     p - 1					)
    count += _in_place_quick_sort(snake_case__	,     p + 1	,     snake_case__					)
  return count


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      Dict	,     snake_case__							:      int					):
  """simple docstring"""

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

  _snake_case							:	str			=						a[new_pivot_index + 1]
  _snake_case							:	Dict			=						a[end]
  _snake_case							:	Dict			=						temp
  return new_pivot_index + 1, count


A_      			=       TemporaryFile()
A_      			=       1_00  # 1000 elements are to be sorted


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


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


A_      			=       {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       ['''OwlViTFeatureExtractor''']
     A_      			=       ['''OwlViTImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       [
         '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''OwlViTModel''',
         '''OwlViTPreTrainedModel''',
         '''OwlViTTextModel''',
         '''OwlViTVisionModel''',
         '''OwlViTForObjectDetection''',
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

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


