


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case				:    Optional[int]       				=     {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Optional[int]       				=     ['''BlenderbotTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Dict       				=     [
         '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BlenderbotForCausalLM''',
         '''BlenderbotForConditionalGeneration''',
         '''BlenderbotModel''',
         '''BlenderbotPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Tuple       				=     [
         '''TFBlenderbotForConditionalGeneration''',
         '''TFBlenderbotModel''',
         '''TFBlenderbotPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    str       				=     [
         '''FlaxBlenderbotForConditionalGeneration''',
         '''FlaxBlenderbotModel''',
         '''FlaxBlenderbotPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     snake_case				:    Optional[Any]       				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









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







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

       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:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





