"""simple docstring"""




from ...processing_utils import ProcessorMixin
class 			UpperCamelCase__(	UpperCamelCase_       ):
						lowerCAmelCase__     :  Optional[int]  							=						'SpeechT5FeatureExtractor'
						lowerCAmelCase__     :  Dict  							=						'SpeechT5Tokenizer'





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[int]:
													super().__init__(lowercase_						,lowercase_   )





						def __call__(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Any:
													A__		   =							kwargs.pop('audio'						,lowercase_   )
													A__		   =							kwargs.pop('text'						,lowercase_   )
													A__		   =							kwargs.pop('text_target'						,lowercase_   )
													A__		   =							kwargs.pop('audio_target'						,lowercase_   )
													A__		   =							kwargs.pop('sampling_rate'						,lowercase_   )

													if audio is not None and text is not None:
																				raise ValueError(
																				    'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'   )
													if audio_target is not None and text_target is not None:
																				raise ValueError(
																				    'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'   )
													if audio is None and audio_target is None and text is None and text_target is None:
																				raise ValueError(
																				    'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'   )

													if audio is not None:
																				A__		   =							self.feature_extractor(lowercase_						,*lowercase_						,sampling_rate=lowercase_						,**lowercase_   )
													elif text is not None:
																				A__		   =							self.tokenizer(lowercase_						,**lowercase_   )
													else:
																				A__		   =							None

													if audio_target is not None:
																				A__		   =							self.feature_extractor(audio_target=lowercase_						,*lowercase_						,sampling_rate=lowercase_						,**lowercase_   )
																				A__		   =							targets['input_values']
													elif text_target is not None:
																				A__		   =							self.tokenizer(lowercase_						,**lowercase_   )
																				A__		   =							targets['input_ids']
													else:
																				A__		   =							None

													if inputs is None:
																				return targets

													if targets is not None:
																				A__		   =							labels

																				A__		   =							targets.get('attention_mask'   )
																				if decoder_attention_mask is not None:
																											A__		   =							decoder_attention_mask

													return inputs





						def  snake_case__		(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> List[str]:
													A__		   =							kwargs.pop('input_values'						,lowercase_   )
													A__		   =							kwargs.pop('input_ids'						,lowercase_   )
													A__		   =							kwargs.pop('labels'						,lowercase_   )

													if input_values is not None and input_ids is not None:
																				raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'   )
													if input_values is None and input_ids is None and labels is None:
																				raise ValueError(
																				    'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'   )

													if input_values is not None:
																				A__		   =							self.feature_extractor.pad(lowercase_						,*lowercase_						,**lowercase_   )
													elif input_ids is not None:
																				A__		   =							self.tokenizer.pad(lowercase_						,**lowercase_   )
													else:
																				A__		   =							None

													if labels is not None:
																				if "input_ids" in labels or (isinstance(lowercase_						,lowercase_   ) and "input_ids" in labels[0]):
																											A__		   =							self.tokenizer.pad(lowercase_						,**lowercase_   )
																											A__		   =							targets['input_ids']
																				else:
																											A__		   =							self.feature_extractor.feature_size
																											A__		   =							self.feature_extractor.num_mel_bins
																											A__		   =							self.feature_extractor.pad(lowercase_						,*lowercase_						,**lowercase_   )
																											A__		   =							feature_size_hack
																											A__		   =							targets['input_values']
													else:
																				A__		   =							None

													if inputs is None:
																				return targets

													if targets is not None:
																				A__		   =							labels

																				A__		   =							targets.get('attention_mask'   )
																				if decoder_attention_mask is not None:
																											A__		   =							decoder_attention_mask

													return inputs





						def  snake_case__		(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Tuple:
													return self.tokenizer.batch_decode(*lowercase_						,**lowercase_   )





						def  snake_case__		(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Tuple:
													return self.tokenizer.decode(*lowercase_						,**lowercase_   )









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


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      ='transfo-xl'
      UpperCamelCase__							:  int      =['mems']
      UpperCamelCase__							:  List[str]      ={
          'n_token': 'vocab_size',
          'hidden_size': 'd_model',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(					self						:     Tuple			,   lowercase_						:     List[Any]=26_7735			,   lowercase_						:     List[Any]=[2_0000, 4_0000, 20_0000]			,   lowercase_						:     Tuple=1024			,   lowercase_						:     Optional[int]=1024			,   lowercase_						:     str=16			,   lowercase_						:     int=64			,   lowercase_						:     Any=4096			,   lowercase_						:     List[Any]=4			,   lowercase_						:     List[Any]=False			,   lowercase_						:     List[str]=18			,   lowercase_						:     Tuple=1600			,   lowercase_						:     List[str]=1000			,   lowercase_						:     Any=True			,   lowercase_						:     str=True			,   lowercase_						:     Optional[int]=0			,   lowercase_						:     List[Any]=-1			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Any=0.1			,   lowercase_						:     Tuple=0.0			,   lowercase_						:     str=True			,   lowercase_						:     Union[str, Any]="normal"			,   lowercase_						:     Dict=0.01			,   lowercase_						:     Optional[Any]=0.01			,   lowercase_						:     List[Any]=0.02			,   lowercase_						:     List[Any]=1E-5			,   lowercase_						:     Tuple=0			,   **lowercase_						:     Tuple			,   )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=vocab_size
             _lowerCamelCase       :		Optional[Any]		=[]
             self.cutoffs.extend(lowercase_ )
             if proj_share_all_but_first:
                    _lowerCamelCase       :		List[str]		=[False] + [True] * len(self.cutoffs )
             else:
                    _lowerCamelCase       :		str		=[False] + [False] * len(self.cutoffs )
             _lowerCamelCase       :		str		=d_model
             _lowerCamelCase       :		str		=d_embed
             _lowerCamelCase       :		str		=d_head
             _lowerCamelCase       :		Dict		=d_inner
             _lowerCamelCase       :		Union[str, Any]		=div_val
             _lowerCamelCase       :		Dict		=pre_lnorm
             _lowerCamelCase       :		Tuple		=n_layer
             _lowerCamelCase       :		Optional[Any]		=n_head
             _lowerCamelCase       :		List[Any]		=mem_len
             _lowerCamelCase       :		List[Any]		=same_length
             _lowerCamelCase       :		Optional[int]		=attn_type
             _lowerCamelCase       :		List[str]		=clamp_len
             _lowerCamelCase       :		int		=sample_softmax
             _lowerCamelCase       :		List[Any]		=adaptive
             _lowerCamelCase       :		List[str]		=dropout
             _lowerCamelCase       :		Any		=dropatt
             _lowerCamelCase       :		Dict		=untie_r
             _lowerCamelCase       :		List[Any]		=init
             _lowerCamelCase       :		List[str]		=init_range
             _lowerCamelCase       :		int		=proj_init_std
             _lowerCamelCase       :		Optional[int]		=init_std
             _lowerCamelCase       :		Tuple		=layer_norm_epsilon
             super().__init__(eos_token_id=lowercase_			,   **lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
             return -1






      @max_position_embeddings.setter
      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     int )					->						List[str]:
             """simple docstring"""




             raise NotImplementedError(
                 F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


