



'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Dict:
		_a	: List[str]								=				0
		if start < end:
				_a	: Any								=				randint(snake_case__		,     snake_case__    )
				_a	: Dict								=				a[end]
				_a	: Optional[int]								=				a[pivot]
				_a	: int								=				temp

				_a	: List[Any]								=				_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 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Union[str, Any]:
		_a	: str								=				0
		_a	: Any								=				randint(snake_case__		,     snake_case__    )
		_a	: List[str]								=				a[end]
		_a	: str								=				a[pivot]
		_a	: List[Any]								=				temp
		_a	: 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
						_a	: List[Any]								=				new_pivot_index + 1
						_a	: int								=				a[new_pivot_index]
						_a	: Tuple								=				a[index]
						_a	: List[Any]								=				temp

		_a	: int								=				a[new_pivot_index + 1]
		_a	: Dict								=				a[end]
		_a	: int								=				temp
		return new_pivot_index + 1, count


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


__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)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase     :							Tuple       						=     {
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase     :							int       						=     [
          'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LukeForEntityClassification',
          'LukeForEntityPairClassification',
          'LukeForEntitySpanClassification',
          'LukeForMultipleChoice',
          'LukeForQuestionAnswering',
          'LukeForSequenceClassification',
          'LukeForTokenClassification',
          'LukeForMaskedLM',
          'LukeModel',
          'LukePreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
      from .tokenization_luke import LukeTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_luke import (
                LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
                LukeForEntityClassification,
                LukeForEntityPairClassification,
                LukeForEntitySpanClassification,
                LukeForMaskedLM,
                LukeForMultipleChoice,
                LukeForQuestionAnswering,
                LukeForSequenceClassification,
                LukeForTokenClassification,
                LukeModel,
                LukePreTrainedModel,
            )

else:
      import sys

      lowercase     :							Dict       						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




