






"""simple docstring"""



import copy

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


A__       :				Any 					=					logging.get_logger(__name__)







class    lowercase__			(							UpperCAmelCase__   ):
			_UpperCAmelCase		:Dict   			= "encoder-decoder"
			_UpperCAmelCase		:Optional[int]   			= True


			def __init__(      self     :  List[str]   ,       **snake_case__     :  Union[str, Any]					):
										super().__init__(**UpperCAmelCase__					)
										assert (
										    "encoder" in kwargs and "decoder" in kwargs
										), "Config has to be initialized with encoder and decoder config"
										lowerCamelCase_       :							Union[str, Any]									=kwargs.pop("encoder"					)
										lowerCamelCase_       :							str									=encoder_config.pop("model_type"					)
										lowerCamelCase_       :							Tuple									=kwargs.pop("decoder"					)
										lowerCamelCase_       :							Optional[int]									=decoder_config.pop("model_type"					)

										from ..auto.configuration_auto import AutoConfig

										lowerCamelCase_       :							int									=AutoConfig.for_model(UpperCAmelCase__   ,       **UpperCAmelCase__					)
										lowerCamelCase_       :							Optional[Any]									=AutoConfig.for_model(UpperCAmelCase__   ,       **UpperCAmelCase__					)
										lowerCamelCase_       :							Optional[Any]									=True


			@classmethod
			def 	UpperCAmelCase__						(      cls     :  Union[str, Any]   ,       snake_case__     :  PretrainedConfig   ,       snake_case__     :  PretrainedConfig   ,       **snake_case__     :  Union[str, Any]					):
										logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"					)
										lowerCamelCase_       :							int									=True
										lowerCamelCase_       :							Optional[int]									=True

										return cls(encoder=encoder_config.to_dict()   ,       decoder=decoder_config.to_dict()   ,       **UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  str					):
										lowerCamelCase_       :							List[Any]									=copy.deepcopy(self.__dict__					)
										lowerCamelCase_       :							List[Any]									=self.encoder.to_dict()
										lowerCamelCase_       :							Optional[Any]									=self.decoder.to_dict()
										lowerCamelCase_       :							Union[str, Any]									=self.__class__.model_type
										return output









from ...processing_utils import ProcessorMixin


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''SpeechT5FeatureExtractor'''
    UpperCAmelCase__							  =      '''SpeechT5Tokenizer'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__					, UpperCAmelCase__)


    def __call__(			self      :	Dict					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__       =    kwargs.pop('''audio'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''audio_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''sampling_rate'''					, UpperCAmelCase__)

           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(UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
           elif text is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if audio_target is not None:
                  A__       =    self.feature_extractor(audio_target=UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_values''']
           elif text_target is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
                  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        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    kwargs.pop('''input_values'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''input_ids'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''labels'''					, UpperCAmelCase__)

           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(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
           elif input_ids is not None:
                  A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if labels is not None:
                  if "input_ids" in labels or (isinstance(UpperCAmelCase__					, UpperCAmelCase__) and "input_ids" in labels[0]):
                         A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    targets['''input_ids''']
                  else:
                         A__       =    self.feature_extractor.feature_size
                         A__       =    self.feature_extractor.num_mel_bins
                         A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
                         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        SCREAMING_SNAKE_CASE							(			self      :	Any					, *UpperCAmelCase__      :	Dict					, **UpperCAmelCase__      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

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





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Union[str, Any])						->Dict:





           '''simple docstring'''

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


