






from ...processing_utils import ProcessorMixin


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	'''SpeechT5FeatureExtractor'''
     UpperCamelCase_							:		str			=	'''SpeechT5Tokenizer'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[str]      ):
       super().__init__(UpperCAmelCase_					, UpperCAmelCase_      )


     def __call__(				self       :	List[Any]					, *UpperCAmelCase_       :	Optional[int]					, **UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      int							=    kwargs.pop("audio"					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    kwargs.pop("text"					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    kwargs.pop("text_target"					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    kwargs.pop("audio_target"					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    kwargs.pop("sampling_rate"					, UpperCAmelCase_      )

       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:
         SCREAMING_SNAKE_CASE       :      int							=    self.feature_extractor(UpperCAmelCase_					, *UpperCAmelCase_					, sampling_rate=UpperCAmelCase_					, **UpperCAmelCase_      )
       elif text is not None:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.tokenizer(UpperCAmelCase_					, **UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      Any							=    None

       if audio_target is not None:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.feature_extractor(audio_target=UpperCAmelCase_					, *UpperCAmelCase_					, sampling_rate=UpperCAmelCase_					, **UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    targets["input_values"]
       elif text_target is not None:
         SCREAMING_SNAKE_CASE       :      Tuple							=    self.tokenizer(UpperCAmelCase_					, **UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      List[Any]							=    targets["input_ids"]
       else:
         SCREAMING_SNAKE_CASE       :      Tuple							=    None

       if inputs is None:
         return targets

       if targets is not None:
         SCREAMING_SNAKE_CASE       :      Any							=    labels

         SCREAMING_SNAKE_CASE       :      Dict							=    targets.get("attention_mask"      )
         if decoder_attention_mask is not None:
           SCREAMING_SNAKE_CASE       :      Tuple							=    decoder_attention_mask

       return inputs


     def 				_A       (				self       :	Optional[Any]					, *UpperCAmelCase_       :	Any					, **UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    kwargs.pop("input_values"					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Dict							=    kwargs.pop("input_ids"					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    kwargs.pop("labels"					, UpperCAmelCase_      )

       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:
         SCREAMING_SNAKE_CASE       :      Dict							=    self.feature_extractor.pad(UpperCAmelCase_					, *UpperCAmelCase_					, **UpperCAmelCase_      )
       elif input_ids is not None:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.tokenizer.pad(UpperCAmelCase_					, **UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      Tuple							=    None

       if labels is not None:
         if "input_ids" in labels or (isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) and "input_ids" in labels[0]):
           SCREAMING_SNAKE_CASE       :      Dict							=    self.tokenizer.pad(UpperCAmelCase_					, **UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      int							=    targets["input_ids"]
         else:
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.feature_extractor.feature_size
           SCREAMING_SNAKE_CASE       :      Tuple							=    self.feature_extractor.num_mel_bins
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.feature_extractor.pad(UpperCAmelCase_					, *UpperCAmelCase_					, **UpperCAmelCase_      )
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    feature_size_hack
           SCREAMING_SNAKE_CASE       :      Tuple							=    targets["input_values"]
       else:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    None

       if inputs is None:
         return targets

       if targets is not None:
         SCREAMING_SNAKE_CASE       :      List[str]							=    labels

         SCREAMING_SNAKE_CASE       :      List[str]							=    targets.get("attention_mask"      )
         if decoder_attention_mask is not None:
           SCREAMING_SNAKE_CASE       :      str							=    decoder_attention_mask

       return inputs


     def 				_A       (				self       :	List[Any]					, *UpperCAmelCase_       :	Union[str, Any]					, **UpperCAmelCase_       :	List[str]      ):
       return self.tokenizer.batch_decode(*UpperCAmelCase_					, **UpperCAmelCase_      )






     def 				_A       (				self       :	Any					, *UpperCAmelCase_       :	Tuple					, **UpperCAmelCase_       :	List[str]      ):
       return self.tokenizer.decode(*UpperCAmelCase_					, **UpperCAmelCase_      )














from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

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






