
from random import randint
from tempfile import TemporaryFile

import numpy as np



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						0
					if start < end:
										lowercase							=						randint(lowerCAmelCase__ ,			lowerCAmelCase__		)
										lowercase							=						a[end]
										lowercase							=						a[pivot]
										lowercase							=						temp

										lowercase	,						lowercase							=						_in_place_partition(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
										count += _in_place_quick_sort(lowerCAmelCase__ ,			lowerCAmelCase__ ,			p - 1		)
										count += _in_place_quick_sort(lowerCAmelCase__ ,			p + 1 ,			lowerCAmelCase__		)
					return count



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						0
					lowercase							=						randint(lowerCAmelCase__ ,			lowerCAmelCase__		)
					lowercase							=						a[end]
					lowercase							=						a[pivot]
					lowercase							=						temp
					lowercase							=						start - 1
					for index in range(lowerCAmelCase__ ,			lowerCAmelCase__		):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															lowercase							=						new_pivot_index + 1
															lowercase							=						a[new_pivot_index]
															lowercase							=						a[index]
															lowercase							=						temp

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


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


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


outfile.seek(0)  # using the same array
lowercase__		:int   =    np.load(outfile)
lowercase__		:Dict   =    len(M) - 1
lowercase__		:int   =    _in_place_quick_sort(M, 0, r)

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








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


UpperCAmelCase__   	=							{
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							['''EfficientNetImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''EfficientNetForImageClassification''',
      '''EfficientNetModel''',
      '''EfficientNetPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_efficientnet import (
      EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      EfficientNetConfig,
      EfficientNetOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientnet import EfficientNetImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientnet import (
        EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientNetForImageClassification,
        EfficientNetModel,
        EfficientNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure)

