"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    int	      =	"Wav2Vec2FeatureExtractor"
     _UpperCamelCase							:    Optional[Any]	      =	"AutoTokenizer"






     def __init__(	self       ,  a__       ,  a__						):
       super().__init__(a__       ,  a__						)
       _lowerCAmelCase		: str						    =  self.feature_extractor
       _lowerCAmelCase		: Dict						    =  False






     @classmethod
     def 					__A						(	cls       ,  a__       ,  **a__						):
       try:
         return super().from_pretrained(a__       ,  **a__						)
       except OSError:
         warnings.warn(
             F"Loading a tokenizer inside {cls.__name__} from a config that does not"
             """ include a `tokenizer_class` attribute is deprecated and will be """
             """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
             """ attribute to either your `config.json` or `tokenizer_config.json` """
             """file to suppress this warning: """       ,  a__       ,  )

         _lowerCAmelCase		: Optional[int]						    =  WavaVecaFeatureExtractor.from_pretrained(a__       ,  **a__						)
         _lowerCAmelCase		: Tuple						    =  WavaVecaCTCTokenizer.from_pretrained(a__       ,  **a__						)

         return cls(feature_extractor=a__       ,  tokenizer=a__						)






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

       if "raw_speech" in kwargs:
         warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""						)
         _lowerCAmelCase		: str						    =  kwargs.pop("""raw_speech"""						)
       else:
         _lowerCAmelCase		: Optional[int]						    =  kwargs.pop("""audio"""       ,  a__						)
       _lowerCAmelCase		: str						    =  kwargs.pop("""sampling_rate"""       ,  a__						)
       _lowerCAmelCase		: Any						    =  kwargs.pop("""text"""       ,  a__						)
       if len(a__						) > 0:
         _lowerCAmelCase		: List[Any]						    =  args[0]
         _lowerCAmelCase		: List[Any]						    =  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:
         _lowerCAmelCase		: Optional[Any]						    =  self.feature_extractor(a__       ,  *a__       ,  sampling_rate=a__       ,  **a__						)
       if text is not None:
         _lowerCAmelCase		: int						    =  self.tokenizer(a__       ,  **a__						)

       if text is None:
         return inputs
       elif audio is None:
         return encodings
       else:
         _lowerCAmelCase		: Optional[int]						    =  encodings["""input_ids"""]
         return inputs






     def 					__A						(	self       ,  *a__       ,  **a__						):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor.pad(*a__       ,  **a__						)

       _lowerCAmelCase		: List[Any]						    =  kwargs.pop("""input_features"""       ,  a__						)
       _lowerCAmelCase		: List[Any]						    =  kwargs.pop("""labels"""       ,  a__						)
       if len(a__						) > 0:
         _lowerCAmelCase		: Union[str, Any]						    =  args[0]
         _lowerCAmelCase		: Optional[Any]						    =  args[1:]

       if input_features is not None:
         _lowerCAmelCase		: Dict						    =  self.feature_extractor.pad(a__       ,  *a__       ,  **a__						)
       if labels is not None:
         _lowerCAmelCase		: Optional[Any]						    =  self.tokenizer.pad(a__       ,  **a__						)

       if labels is None:
         return input_features
       elif input_features is None:
         return labels
       else:
         _lowerCAmelCase		: List[Any]						    =  labels["""input_ids"""]
         return input_features






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






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






     @contextmanager
     def 					__A						(	self						):
       warnings.warn(
           """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
           """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
           """your audio inputs, or in a separate call."""						)
       _lowerCAmelCase		: str						    =  True
       _lowerCAmelCase		: str						    =  self.tokenizer
       yield
       _lowerCAmelCase		: Union[str, Any]						    =  self.feature_extractor
       _lowerCAmelCase		: Optional[Any]						    =  False





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




