'''simple docstring'''


from ...processing_utils import ProcessorMixin





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''SpeechT5FeatureExtractor'''
  A_    							=						'''SpeechT5Tokenizer'''




  def __init__(		self      :       Optional[Any]   ,				lowercase_      :       List[str]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    super().__init__(lowercase_   ,				lowercase_      )




  def __call__(		self      :       str   ,				*lowercase_      :       Optional[Any]   ,				**lowercase_      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       kwargs.pop("""audio"""   ,				lowercase_      )
    lowercase_     =       kwargs.pop("""text"""   ,				lowercase_      )
    lowercase_     =       kwargs.pop("""text_target"""   ,				lowercase_      )
    lowercase_     =       kwargs.pop("""audio_target"""   ,				lowercase_      )
    lowercase_     =       kwargs.pop("""sampling_rate"""   ,				lowercase_      )

    if audio is not None and text is not None:
      raise ValueError(
          """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""      )
    if audio_target is not None and text_target is not None:
      raise ValueError(
          """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""      )
    if audio is None and audio_target is None and text is None and text_target is None:
      raise ValueError(
          """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""      )

    if audio is not None:
      lowercase_     =       self.feature_extractor(lowercase_   ,				*lowercase_   ,				sampling_rate=lowercase_   ,				**lowercase_      )
    elif text is not None:
      lowercase_     =       self.tokenizer(lowercase_   ,				**lowercase_      )
    else:
      lowercase_     =       None

    if audio_target is not None:
      lowercase_     =       self.feature_extractor(audio_target=lowercase_   ,				*lowercase_   ,				sampling_rate=lowercase_   ,				**lowercase_      )
      lowercase_     =       targets["""input_values"""]
    elif text_target is not None:
      lowercase_     =       self.tokenizer(lowercase_   ,				**lowercase_      )
      lowercase_     =       targets["""input_ids"""]
    else:
      lowercase_     =       None

    if inputs is None:
      return targets

    if targets is not None:
      lowercase_     =       labels

      lowercase_     =       targets.get("""attention_mask"""      )
      if decoder_attention_mask is not None:
        lowercase_     =       decoder_attention_mask

    return inputs




  def 		lowerCamelCase__  (		self      :       Tuple   ,				*lowercase_      :       Optional[Any]   ,				**lowercase_      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       kwargs.pop("""input_values"""   ,				lowercase_      )
    lowercase_     =       kwargs.pop("""input_ids"""   ,				lowercase_      )
    lowercase_     =       kwargs.pop("""labels"""   ,				lowercase_      )

    if input_values is not None and input_ids is not None:
      raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""      )
    if input_values is None and input_ids is None and labels is None:
      raise ValueError(
          """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""      )

    if input_values is not None:
      lowercase_     =       self.feature_extractor.pad(lowercase_   ,				*lowercase_   ,				**lowercase_      )
    elif input_ids is not None:
      lowercase_     =       self.tokenizer.pad(lowercase_   ,				**lowercase_      )
    else:
      lowercase_     =       None

    if labels is not None:
      if "input_ids" in labels or (isinstance(lowercase_   ,				lowercase_      ) and "input_ids" in labels[0]):
        lowercase_     =       self.tokenizer.pad(lowercase_   ,				**lowercase_      )
        lowercase_     =       targets["""input_ids"""]
      else:
        lowercase_     =       self.feature_extractor.feature_size
        lowercase_     =       self.feature_extractor.num_mel_bins
        lowercase_     =       self.feature_extractor.pad(lowercase_   ,				*lowercase_   ,				**lowercase_      )
        lowercase_     =       feature_size_hack
        lowercase_     =       targets["""input_values"""]
    else:
      lowercase_     =       None

    if inputs is None:
      return targets

    if targets is not None:
      lowercase_     =       labels

      lowercase_     =       targets.get("""attention_mask"""      )
      if decoder_attention_mask is not None:
        lowercase_     =       decoder_attention_mask

    return inputs




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				*lowercase_      :       int   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


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





  def 		lowerCamelCase__  (		self      :       List[str]   ,				*lowercase_      :       Optional[Any]   ,				**lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


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


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						  =		{
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GitForCausalLM""",
           """GitModel""",
           """GitPreTrainedModel""",
           """GitVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
       from .processing_git import GitProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_git import (
                  GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GitForCausalLM,
                  GitModel,
                  GitPreTrainedModel,
                  GitVisionModel,
              )

else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

