



import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available







def    _snake_case (       *__snake_case    )     ->     Optional[int]:






      '''simple docstring'''







      if not isinstance(__snake_case					,  __snake_case    ):
            UpperCAmelCase_  :	Any				      =		list(__snake_case    )
      for i in range(len(__snake_case    )    ):
            UpperCAmelCase_  :	List[str]				      =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects







def    _snake_case (       __snake_case    )     ->     bool:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		[
          "CUDA out of memory.",  # CUDA OOM
          "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.",  # CUDNN SNAFU
          "DefaultCPUAllocator: can't allocate memory",  # CPU OOM
      ]
      if isinstance(__snake_case					,  __snake_case    ) and len(exception.args    ) == 1:
            return any(err in exception.args[0] for err in _statements    )
      return False







def    _snake_case (       __snake_case = None					,  __snake_case = 1_2_8    )     ->     Tuple:






      '''simple docstring'''







      if function is None:
            return functools.partial(__snake_case					,  starting_batch_size=__snake_case    )

      UpperCAmelCase_  :	Dict				      =		starting_batch_size

      def decorator(*__snake_case					,  **__snake_case    ):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            UpperCAmelCase_  :	str				      =		list(inspect.signature(__snake_case    ).parameters.keys()    )
            # Guard against user error
            if len(__snake_case    ) < (len(__snake_case    ) + 1):
                  UpperCAmelCase_  :	Optional[int]				      =		", ".join([F"""{arg}={value}""" for arg, value in zip(params[1:]					,  args[1:]    )]    )
                  raise TypeError(
                      F"""Batch size was passed into `{function.__name__}` as the first argument when called."""
                      F"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`"""    )
            while True:
                  if batch_size == 0:
                        raise RuntimeError("No executable batch size found, reached zero."    )
                  try:
                        return function(__snake_case					,  *__snake_case					,  **__snake_case    )
                  except Exception as e:
                        if should_reduce_batch_size(__snake_case    ):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator





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))
