


"""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 						snake_case_(				a__	):
     __UpperCamelCase     =    '''Wav2Vec2FeatureExtractor'''
     __UpperCamelCase     =    '''AutoTokenizer'''






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    int ):
           super().__init__(UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase    :		List[str]				=      self.feature_extractor
           lowerCAmelCase    :		List[str]				=      False






     @classmethod
     def    lowerCamelCase__   (		cls     :    Tuple    , UpperCamelCase_     :    Union[str, Any]    , **UpperCamelCase_     :    List[str] ):
           try:
                 return super().from_pretrained(UpperCamelCase_    , **UpperCamelCase_ )
           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: '''    , UpperCamelCase_    , )

                 lowerCAmelCase    :		int				=      WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_    , **UpperCamelCase_ )
                 lowerCAmelCase    :		Union[str, Any]				=      WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase_    , **UpperCamelCase_ )

                 return cls(feature_extractor=UpperCamelCase_    , tokenizer=UpperCamelCase_ )






     def __call__(		self     :    Any    , *UpperCamelCase_     :    int    , **UpperCamelCase_     :    Union[str, Any] ):
           # For backward compatibility
           if self._in_target_context_manager:
                 return self.current_processor(*UpperCamelCase_    , **UpperCamelCase_ )

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

           if text is None:
                 return inputs
           elif audio is None:
                 return encodings
           else:
                 lowerCAmelCase    :		List[Any]				=      encodings['''input_ids''']
                 return inputs






     def    lowerCamelCase__   (		self     :    str    , *UpperCamelCase_     :    List[str]    , **UpperCamelCase_     :    Tuple ):
           # For backward compatibility
           if self._in_target_context_manager:
                 return self.current_processor.pad(*UpperCamelCase_    , **UpperCamelCase_ )

           lowerCAmelCase    :		Tuple				=      kwargs.pop('''input_features'''    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      kwargs.pop('''labels'''    , UpperCamelCase_ )
           if len(UpperCamelCase_ ) > 0:
                 lowerCAmelCase    :		Union[str, Any]				=      args[0]
                 lowerCAmelCase    :		Optional[int]				=      args[1:]

           if input_features is not None:
                 lowerCAmelCase    :		Tuple				=      self.feature_extractor.pad(UpperCamelCase_    , *UpperCamelCase_    , **UpperCamelCase_ )
           if labels is not None:
                 lowerCAmelCase    :		Optional[Any]				=      self.tokenizer.pad(UpperCamelCase_    , **UpperCamelCase_ )

           if labels is None:
                 return input_features
           elif input_features is None:
                 return labels
           else:
                 lowerCAmelCase    :		int				=      labels['''input_ids''']
                 return input_features






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , *UpperCamelCase_     :    List[str]    , **UpperCamelCase_     :    Any ):
           return self.tokenizer.batch_decode(*UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any]    , *UpperCamelCase_     :    Dict    , **UpperCamelCase_     :    Union[str, Any] ):
           return self.tokenizer.decode(*UpperCamelCase_    , **UpperCamelCase_ )






     @contextmanager
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           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    :		Union[str, Any]				=      True
           lowerCAmelCase    :		List[str]				=      self.tokenizer
           yield
           lowerCAmelCase    :		Optional[Any]				=      self.feature_extractor
           lowerCAmelCase    :		Union[str, Any]				=      False









"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)