


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class     lowercase__  (					snake_case__		):
  A__=  'ClapFeatureExtractor'
  A__=  ('RobertaTokenizer', 'RobertaTokenizerFast')






  def __init__(					self      :					List[Any]		,						_lowercase      :					Any		,						_lowercase      :					int ):




         """simple docstring"""







         super().__init__(lowercase_		,						lowercase_ )






  def __call__(					self      :					Dict		,						_lowercase      :					Optional[int]=None		,						_lowercase      :					int=None		,						_lowercase      :					int=None		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     kwargs.pop("sampling_rate"		,						lowercase_ )

         if text is None and audios is None:
                raise ValueError("You have to specify either text or audios. Both cannot be none." )

         if text is not None:
                UpperCAmelCase__										=     self.tokenizer(lowercase_		,						return_tensors=lowercase_		,						**lowercase_ )

         if audios is not None:
                UpperCAmelCase__										=     self.feature_extractor(
                    lowercase_		,						sampling_rate=lowercase_		,						return_tensors=lowercase_		,						**lowercase_ )

         if text is not None and audios is not None:
                UpperCAmelCase__										=     audio_features.input_features
                return encoding
         elif text is not None:
                return encoding
         else:
                return BatchEncoding(data=dict(**lowercase_ )		,						tensor_type=lowercase_ )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						*_lowercase      :					Union[str, Any]		,						**_lowercase      :					List[Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Tuple		,						*_lowercase      :					int		,						**_lowercase      :					Union[str, Any] ):




         """simple docstring"""







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






  @property
  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer.model_input_names
         UpperCAmelCase__										=     self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )









from typing import TYPE_CHECKING

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


A      = {"configuration_mmbt": ["MMBTConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
    from .configuration_mmbt import MMBTConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
    import sys

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





