


"""simple docstring"""







from random import randint
from tempfile import TemporaryFile

import numpy as np
def    a__     (							SCREAMING_SNAKE_CASE       :       Union[str, Any]							, SCREAMING_SNAKE_CASE       :       str							, SCREAMING_SNAKE_CASE       :       str							):


  '''simple docstring'''







  lowerCAmelCase	:	Union[str, Any]								=				0
  if start < end:
    lowerCAmelCase	:	Optional[Any]								=				randint(UpperCamelCase__							, UpperCamelCase__							)
    lowerCAmelCase	:	Any								=				a[end]
    lowerCAmelCase	:	Tuple								=				a[pivot]
    lowerCAmelCase	:	Optional[Any]								=				temp

    lowerCAmelCase		,    lowerCAmelCase	:	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    a__     (							SCREAMING_SNAKE_CASE       :       Optional[int]							, SCREAMING_SNAKE_CASE       :       Dict							, SCREAMING_SNAKE_CASE       :       Tuple							):


  '''simple docstring'''







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

  lowerCAmelCase	:	Optional[Any]								=				a[new_pivot_index + 1]
  lowerCAmelCase	:	Optional[Any]								=				a[end]
  lowerCAmelCase	:	Optional[Any]								=				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)









import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



