"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__:Any			=	{
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Dict			=	[
       """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """GPTBigCodeForSequenceClassification""",
       """GPTBigCodeForTokenClassification""",
       """GPTBigCodeForCausalLM""",
       """GPTBigCodeModel""",
       """GPTBigCodePreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_bigcode import (
          GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTBigCodeForCausalLM,
          GPTBigCodeForSequenceClassification,
          GPTBigCodeForTokenClassification,
          GPTBigCodeModel,
          GPTBigCodePreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE__:Optional[int]			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""



from ...processing_utils import ProcessorMixin






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[str]           =		"""WhisperFeatureExtractor"""
 _snake_case   :			Any           =		"""WhisperTokenizer"""


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase  ,						lowerCamelCase		)
        __a     				=  self.feature_extractor
        __a     				=  False


 def 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=True		):
        return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase  ,						language=lowerCamelCase  ,						no_timestamps=lowerCamelCase		)


 def __call__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        # For backward compatibility
        if self._in_target_context_manager:
               return self.current_processor(*lowerCamelCase  ,						**lowerCamelCase		)

        __a     				=  kwargs.pop("audio"  ,						lowerCamelCase		)
        __a     				=  kwargs.pop("sampling_rate"  ,						lowerCamelCase		)
        __a     				=  kwargs.pop("text"  ,						lowerCamelCase		)
        if len(lowerCamelCase		) > 0:
               __a     				=  args[0]
               __a     				=  args[1:]

        if audio is None and text is None:
               raise ValueError("You need to specify either an `audio` or `text` input to process."		)

        if audio is not None:
               __a     				=  self.feature_extractor(lowerCamelCase  ,						*lowerCamelCase  ,						sampling_rate=lowerCamelCase  ,						**lowerCamelCase		)
        if text is not None:
               __a     				=  self.tokenizer(lowerCamelCase  ,						**lowerCamelCase		)

        if text is None:
               return inputs

        elif audio is None:
               return encodings
        else:
               __a     				=  encodings["input_ids"]
               return inputs


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.batch_decode(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.decode(*lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase="np"		):
        return self.tokenizer.get_prompt_ids(lowerCamelCase  ,						return_tensors=lowerCamelCase		)
