





"""simple docstring"""
import numpy as np

__UpperCamelCase			:		Tuple		    =   [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]







class 	a				:



 def __init__(			self			):







  """simple docstring"""






  lowerCAmelCase  							=      np.array(_snake_case			)



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase  							=      np.where(letter == self.SQUARE			)
  lowerCAmelCase  							=      np.concatenate([indexa + 1, indexa + 1]			)
  return indexes



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  return self.SQUARE[indexa - 1, indexa - 1]



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      message.lower()
  lowerCAmelCase  							=      message.replace('j'							,    'i'			)

  lowerCAmelCase  							=      ''
  for letter_index in range(len(_snake_case			)			):
   if message[letter_index] != " ":
    lowerCAmelCase  							=      self.letter_to_numbers(message[letter_index]			)
    lowerCAmelCase  							=      encoded_message + str(numbers[0]			) + str(numbers[1]			)
   elif message[letter_index] == " ":
    lowerCAmelCase  							=      encoded_message + ' '

  return encoded_message



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      message.replace(' '							,    '  '			)
  lowerCAmelCase  							=      ''
  for numbers_index in range(int(len(_snake_case			) / 2			)			):
   if message[numbers_index * 2] != " ":
    lowerCAmelCase  							=      message[numbers_index * 2]
    lowerCAmelCase  							=      message[numbers_index * 2 + 1]

    lowerCAmelCase  							=      self.numbers_to_letter(int(_snake_case			)							,    int(_snake_case			)			)
    lowerCAmelCase  							=      decoded_message + letter
   elif message[numbers_index * 2] == " ":
    lowerCAmelCase  							=      decoded_message + ' '

  return decoded_message












"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 	a				(					a__   ):
 snake_case__    	=    '''AutoFeatureExtractor'''
 snake_case__    	=    '''Speech2Text2Tokenizer'''



 def __init__(			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  super().__init__(_snake_case							,    _snake_case			)
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False



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







  """simple docstring"""






  if self._in_target_context_manager:
   return self.current_processor(*_snake_case							,    **_snake_case			)

  if "raw_speech" in kwargs:
   warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
   lowerCAmelCase  							=      kwargs.pop('raw_speech'			)
  else:
   lowerCAmelCase  							=      kwargs.pop('audio'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('sampling_rate'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('text'							,    _snake_case			)
  if len(_snake_case			) > 0:
   lowerCAmelCase  							=      args[0]
   lowerCAmelCase  							=      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:
   lowerCAmelCase  							=      self.feature_extractor(_snake_case							,    *_snake_case							,    sampling_rate=_snake_case							,    **_snake_case			)
  if text is not None:
   lowerCAmelCase  							=      self.tokenizer(_snake_case							,    **_snake_case			)

  if text is None:
   return inputs
  elif audio is None:
   return encodings
  else:
   lowerCAmelCase  							=      encodings['input_ids']
   return inputs



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.batch_decode(*_snake_case							,    **_snake_case			)



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.decode(*_snake_case							,    **_snake_case			)



 @contextmanager
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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.'			)
  lowerCAmelCase  							=      True
  lowerCAmelCase  							=      self.tokenizer
  yield
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False





