




'''simple docstring'''



from random import randint
from tempfile import TemporaryFile

import numpy as np




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Optional[int]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
      if start < end:
            SCREAMING_SNAKE_CASE   :      Dict    					=						randint(__UpperCamelCase      ,__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						a[end]
            SCREAMING_SNAKE_CASE   :      Dict    					=						a[pivot]
            SCREAMING_SNAKE_CASE   :      str    					=						temp

            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						_in_place_partition(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
            count += _in_place_quick_sort(__UpperCamelCase      ,__UpperCamelCase      ,p - 1    )
            count += _in_place_quick_sort(__UpperCamelCase      ,p + 1      ,__UpperCamelCase    )
      return count




def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






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

      SCREAMING_SNAKE_CASE   :      List[Any]    					=						a[new_pivot_index + 1]
      SCREAMING_SNAKE_CASE   :      Tuple    					=						a[end]
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						temp
      return new_pivot_index + 1, count


UpperCamelCase_    	=				TemporaryFile()
UpperCamelCase_    	=				1_0_0  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
UpperCamelCase_    	=				np.load(outfile)
UpperCamelCase_    	=				len(M) - 1
UpperCamelCase_    	=				_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 Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )


