



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Union[str, Any]       =						"AutoFeatureExtractor"
   A__      : List[str]       =						"Speech2Text2Tokenizer"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Dict							)	->      str:
       super().__init__(lowerCamelCase_  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					self.feature_extractor
       UpperCAmelCase_     :       Dict  		=					False


   def __call__(    self:     int  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     str							)	->      List[str]:
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor(*lowerCamelCase_  ,**lowerCamelCase_							)

       if "raw_speech" in kwargs:
           warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""							)
           UpperCAmelCase_     :       Any  		=					kwargs.pop("""raw_speech"""							)
       else:
           UpperCAmelCase_     :       Optional[int]  		=					kwargs.pop("""audio"""  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					kwargs.pop("""sampling_rate"""  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					kwargs.pop("""text"""  ,lowerCamelCase_							)
       if len(lowerCamelCase_							) > 0:
           UpperCAmelCase_     :       Union[str, Any]  		=					args[0]
           UpperCAmelCase_     :       Tuple  		=					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_     :       Dict  		=					self.feature_extractor(lowerCamelCase_  ,*lowerCamelCase_  ,sampling_rate=lowerCamelCase_  ,**lowerCamelCase_							)
       if text is not None:
           UpperCAmelCase_     :       int  		=					self.tokenizer(lowerCamelCase_  ,**lowerCamelCase_							)

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


   def       A__    (    self:     Optional[int]  ,*lowerCamelCase_:     List[str]  ,**lowerCamelCase_:     Union[str, Any]							)	->      Optional[Any]:
       return self.tokenizer.batch_decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     str  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     List[Any]							)	->      List[str]:
       return self.tokenizer.decode(*lowerCamelCase_  ,**lowerCamelCase_							)






   @contextmanager
   def       A__    (    self:     Dict							)	->      Optional[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_     :       int  		=					True
       UpperCAmelCase_     :       Tuple  		=					self.tokenizer
       yield
       UpperCAmelCase_     :       Tuple  		=					self.feature_extractor
       UpperCAmelCase_     :       Dict  		=					False

'''simple docstring'''
from __future__ import annotations

import math
import random







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[int]:
  lowercase				:							Union[str, Any]									=   d * e - 1
  lowercase				:							List[str]									=   0
  lowercase				:							Tuple									=   0
  while p == 0:
    lowercase				:							Optional[Any]									=   random.randint(2 ,   n - 1			)
    lowercase				:							Dict									=   k
    while True:
      if t % 2 == 0:
        lowercase				:							List[Any]									=   t // 2
        lowercase				:							Union[str, Any]									=   (g**t) % n
        lowercase				:							Optional[Any]									=   math.gcd(x - 1 ,   __UpperCAmelCase			)
        if x > 1 and y > 1:
          lowercase				:							Dict									=   y
          lowercase				:							Any									=   n // y
          break  # find the correct factors
      else:
        break  # t is not divisible by 2, break and choose another g
  return sorted([p, q]			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()