



from typing import TYPE_CHECKING

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


__lowerCamelCase         =			{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase         =			[
        'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BioGptForCausalLM',
        'BioGptForTokenClassification',
        'BioGptForSequenceClassification',
        'BioGptModel',
        'BioGptPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
    from .tokenization_biogpt import BioGptTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_biogpt import (
            BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BioGptForCausalLM,
            BioGptForSequenceClassification,
            BioGptForTokenClassification,
            BioGptModel,
            BioGptPreTrainedModel,
        )


else:
    import sys

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





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


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""ClapFeatureExtractor"""
       _lowerCamelCase			    =				("""RobertaTokenizer""", """RobertaTokenizerFast""")



       def __init__(     self     ,lowercase     ,lowercase):

             """simple docstring"""





             super().__init__(lowercase     ,lowercase)



       def __call__(     self     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		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_  :	List[str]				      =		self.tokenizer(lowercase     ,return_tensors=lowercase     ,**lowercase)

             if audios is not None:
                   UpperCAmelCase_  :	str				      =		self.feature_extractor(
                       lowercase     ,sampling_rate=lowercase     ,return_tensors=lowercase     ,**lowercase)

             if text is not None and audios is not None:
                   UpperCAmelCase_  :	Optional[int]				      =		audio_features.input_features
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**lowercase)     ,tensor_type=lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





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



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.tokenizer.model_input_names
             UpperCAmelCase_  :	str				      =		self.feature_extractor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
