import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class 		a__						(					lowercase__					):
					A	       =							'MCTCTFeatureExtractor'
					A	       =							'AutoTokenizer'

					def __init__(    self      :       Tuple,_A      :       int,_A      :       Dict    ):







							"""simple docstring"""






							super().__init__(__lowercase,__lowercase    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.feature_extractor
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    False

					def __call__(    self      :       int,*_A      :       Tuple,**_A      :       Optional[int]    ):







							"""simple docstring"""






							if self._in_target_context_manager:
									return self.current_processor(*__lowercase,**__lowercase    )

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

							if text is None:
									return inputs
							elif audio is None:
									return encodings
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    encodings["""input_ids"""]
									return inputs

					def 				__UpperCamelCase   (    self      :       Optional[Any],*_A      :       List[Any],**_A      :       int    ):







							"""simple docstring"""






							return self.tokenizer.batch_decode(*__lowercase,**__lowercase    )

					def 				__UpperCamelCase   (    self      :       Optional[int],*_A      :       Optional[Any],**_A      :       List[str]    ):







							"""simple docstring"""






							if self._in_target_context_manager:
									return self.current_processor.pad(*__lowercase,**__lowercase    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    kwargs.pop("input_features",__lowercase    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    kwargs.pop("labels",__lowercase    )
							if len(__lowercase    ) > 0:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args[0]
									SCREAMING_SNAKE_CASE_  :		str				      =    args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE_  :		Any				      =    self.feature_extractor.pad(__lowercase,*__lowercase,**__lowercase    )
							if labels is not None:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.tokenizer.pad(__lowercase,**__lowercase    )

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

					def 				__UpperCamelCase   (    self      :       Any,*_A      :       Union[str, Any],**_A      :       Dict    ):







							"""simple docstring"""






							return self.tokenizer.decode(*__lowercase,**__lowercase    )






					@contextmanager
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							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."    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    True
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.tokenizer
							yield
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.feature_extractor
							SCREAMING_SNAKE_CASE_  :		int				      =    False








from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


