


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowercase_           =	{
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_           =	[
           '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''Speech2Text2ForCausalLM''',
           '''Speech2Text2PreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
       from .processing_speech_to_text_a import SpeechaTextaProcessor
       from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speech_to_text_a import (
                  SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechaTextaForCausalLM,
                  SpeechaTextaPreTrainedModel,
              )

else:
       import sys

       lowercase_           =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""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





