




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






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

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        super().__init__(lowercase_      ,				lowercase_		)

      def __call__(   self      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     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:
          lowerCAmelCase__				:  Optional[int]		       =     self.tokenizer(lowercase_      ,				return_tensors=lowercase_      ,				**lowercase_		)

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

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

      def 	__magic_name__(   self      ,				*__UpperCAmelCase      ,				**__UpperCAmelCase		):
        return self.tokenizer.batch_decode(*lowercase_      ,				**lowercase_		)

      def 	__magic_name__(   self      ,				*__UpperCAmelCase      ,				**__UpperCAmelCase		):
        return self.tokenizer.decode(*lowercase_      ,				**lowercase_		)







      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.tokenizer.model_input_names
        lowerCAmelCase__				:  int		       =     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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_					    = {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = ["""LongformerTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LongformerForMaskedLM""",
         """LongformerForMultipleChoice""",
         """LongformerForQuestionAnswering""",
         """LongformerForSequenceClassification""",
         """LongformerForTokenClassification""",
         """LongformerModel""",
         """LongformerPreTrainedModel""",
         """LongformerSelfAttention""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFLongformerForMaskedLM""",
         """TFLongformerForMultipleChoice""",
         """TFLongformerForQuestionAnswering""",
         """TFLongformerForSequenceClassification""",
         """TFLongformerForTokenClassification""",
         """TFLongformerModel""",
         """TFLongformerPreTrainedModel""",
         """TFLongformerSelfAttention""",
     ]


if TYPE_CHECKING:
     from .configuration_longformer import (
         LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LongformerConfig,
         LongformerOnnxConfig,
     )
     from .tokenization_longformer import LongformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_longformer_fast import LongformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_longformer import (
              LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              LongformerForMaskedLM,
              LongformerForMultipleChoice,
              LongformerForQuestionAnswering,
              LongformerForSequenceClassification,
              LongformerForTokenClassification,
              LongformerModel,
              LongformerPreTrainedModel,
              LongformerSelfAttention,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_longformer import (
              TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLongformerForMaskedLM,
              TFLongformerForMultipleChoice,
              TFLongformerForQuestionAnswering,
              TFLongformerForSequenceClassification,
              TFLongformerForTokenClassification,
              TFLongformerModel,
              TFLongformerPreTrainedModel,
              TFLongformerSelfAttention,
          )

else:
     import sys

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

