


from random import randint
from tempfile import TemporaryFile

import numpy as np



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    _lowercase			:				Tuple 						=						0
    if start < end:
        _lowercase			:				Optional[Any] 						=						randint(lowerCamelCase_				,					lowerCamelCase_					)
        _lowercase			:				Any 						=						a[end]
        _lowercase			:				int 						=						a[pivot]
        _lowercase			:				str 						=						temp

        _lowercase						,							_lowercase			:				Optional[Any] 						=						_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_					)       ->		str:
    _lowercase			:				Optional[int] 						=						0
    _lowercase			:				List[Any] 						=						randint(lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						a[end]
    _lowercase			:				str 						=						a[pivot]
    _lowercase			:				Any 						=						temp
    _lowercase			:				Tuple 						=						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			:				int 						=						new_pivot_index + 1
            _lowercase			:				Optional[int] 						=						a[new_pivot_index]
            _lowercase			:				Tuple 						=						a[index]
            _lowercase			:				str 						=						temp

    _lowercase			:				Any 						=						a[new_pivot_index + 1]
    _lowercase			:				Dict 						=						a[end]
    _lowercase			:				Optional[Any] 						=						temp
    return new_pivot_index + 1, count


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


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


outfile.seek(0)  # using the same array
SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					np.load(outfile)
SCREAMING_SNAKE_CASE			: Any				   =					len(M) - 1
SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					_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_torch_available, is_vision_available


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


