'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 	__A				(			UpperCamelCase__     ):
    a__	:						Optional[int]	       =  """Speech2TextFeatureExtractor"""
    a__	:						Optional[int]	       =  """Speech2TextTokenizer"""



    def __init__(self   :   str       ,      __a   :   Optional[Any]       ,      __a   :   Dict			):
         super().__init__(__a       ,      __a			)
         UpperCAmelCase_	   =       self.feature_extractor
         UpperCAmelCase_	   =       False



    def __call__(self   :   str       ,      *__a   :   Any       ,      **__a   :   Union[str, Any]			):
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor(*__a       ,      **__a			)

         if "raw_speech" in kwargs:
              warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."			)
              UpperCAmelCase_	   =       kwargs.pop("raw_speech"			)
         else:
              UpperCAmelCase_	   =       kwargs.pop("audio"       ,      __a			)
         UpperCAmelCase_	   =       kwargs.pop("sampling_rate"       ,      __a			)
         UpperCAmelCase_	   =       kwargs.pop("text"       ,      __a			)
         if len(__a			) > 0:
              UpperCAmelCase_	   =       args[0]
              UpperCAmelCase_	   =       args[1:]

         if audio is None and text is None:
              raise ValueError("You need to specify either an `audio` or `text` input to process."			)

         if audio is not None:
              UpperCAmelCase_	   =       self.feature_extractor(__a       ,      *__a       ,      sampling_rate=__a       ,      **__a			)
         if text is not None:
              UpperCAmelCase_	   =       self.tokenizer(__a       ,      **__a			)

         if text is None:
              return inputs
         elif audio is None:
              return encodings
         else:
              UpperCAmelCase_	   =       encodings["input_ids"]
              return inputs



    def  _lowercase						(self   :   Any       ,      *__a   :   Dict       ,      **__a   :   Tuple			):
         return self.tokenizer.batch_decode(*__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      *__a   :   Tuple       ,      **__a   :   int			):
         return self.tokenizer.decode(*__a       ,      **__a			)






    @contextmanager
    def  _lowercase						(self   :   List[Any]			):
         warnings.warn(
             "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
             "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
             "your audio inputs, or in a separate call."			)
         UpperCAmelCase_	   =       True
         UpperCAmelCase_	   =       self.tokenizer
         yield
         UpperCAmelCase_	   =       self.feature_extractor
         UpperCAmelCase_	   =       False





'''simple docstring'''





from random import randint
from tempfile import TemporaryFile

import numpy as np





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     if start < end:
          UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       a[end]
          UpperCAmelCase_	   =       a[pivot]
          UpperCAmelCase_	   =       temp

          UpperCAmelCase_   ,			UpperCAmelCase_	   =       _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_    ( snake_case_	:			List[Any] ,   snake_case_	:			Dict ,   snake_case_	:			List[str]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       a[end]
     UpperCAmelCase_	   =       a[pivot]
     UpperCAmelCase_	   =       temp
     UpperCAmelCase_	   =       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
               UpperCAmelCase_	   =       new_pivot_index + 1
               UpperCAmelCase_	   =       a[new_pivot_index]
               UpperCAmelCase_	   =       a[index]
               UpperCAmelCase_	   =       temp

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


SCREAMING_SNAKE_CASE_:					List[str]  =TemporaryFile()
SCREAMING_SNAKE_CASE_:					int  =1_00  # 1000 elements are to be sorted


SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_:					str  =0, 1  # mean and standard deviation
SCREAMING_SNAKE_CASE_:					List[str]  =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_:					str  =np.load(outfile)
SCREAMING_SNAKE_CASE_:					List[Any]  =len(M) - 1
SCREAMING_SNAKE_CASE_:					Dict  =_in_place_quick_sort(M, 0, r)

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






print(z)




