






'''simple docstring'''







from ...processing_utils import ProcessorMixin

class _lowercase			(       _lowercase	):
							a          = """SpeechT5FeatureExtractor"""
							a          = """SpeechT5Tokenizer"""




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str  ):
														super().__init__(UpperCamelCase__    ,							UpperCamelCase__  )




							def __call__(							self:       Tuple    ,							*UpperCamelCase__:       Dict    ,							**UpperCamelCase__:       List[Any]  ):
														lowerCamelCase__      :			str 						= kwargs.pop("""audio"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= kwargs.pop("""text"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= kwargs.pop("""text_target"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Union[str, Any] 						= kwargs.pop("""audio_target"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			int 						= 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:
																					lowerCamelCase__      :			Tuple 						= self.feature_extractor(UpperCamelCase__    ,							*UpperCamelCase__    ,							sampling_rate=UpperCamelCase__    ,							**UpperCamelCase__  )
														elif text is not None:
																					lowerCamelCase__      :			Any 						= self.tokenizer(UpperCamelCase__    ,							**UpperCamelCase__  )
														else:
																					lowerCamelCase__      :			List[Any] 						= None

														if audio_target is not None:
																					lowerCamelCase__      :			Optional[Any] 						= self.feature_extractor(audio_target=UpperCamelCase__    ,							*UpperCamelCase__    ,							sampling_rate=UpperCamelCase__    ,							**UpperCamelCase__  )
																					lowerCamelCase__      :			Optional[Any] 						= targets["""input_values"""]
														elif text_target is not None:
																					lowerCamelCase__      :			Dict 						= self.tokenizer(UpperCamelCase__    ,							**UpperCamelCase__  )
																					lowerCamelCase__      :			List[Any] 						= targets["""input_ids"""]
														else:
																					lowerCamelCase__      :			Optional[Any] 						= None

														if inputs is None:
																					return targets

														if targets is not None:
																					lowerCamelCase__      :			str 						= labels

																					lowerCamelCase__      :			Any 						= targets.get("""attention_mask"""  )
																					if decoder_attention_mask is not None:
																												lowerCamelCase__      :			int 						= decoder_attention_mask

														return inputs




							def 	lowerCamelCase_       (							self:       List[Any]    ,							*UpperCamelCase__:       Dict    ,							**UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Union[str, Any] 						= kwargs.pop("""input_values"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= kwargs.pop("""input_ids"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Union[str, Any] 						= 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:
																					lowerCamelCase__      :			int 						= self.feature_extractor.pad(UpperCamelCase__    ,							*UpperCamelCase__    ,							**UpperCamelCase__  )
														elif input_ids is not None:
																					lowerCamelCase__      :			Union[str, Any] 						= self.tokenizer.pad(UpperCamelCase__    ,							**UpperCamelCase__  )
														else:
																					lowerCamelCase__      :			List[Any] 						= None

														if labels is not None:
																					if "input_ids" in labels or (isinstance(UpperCamelCase__    ,							UpperCamelCase__  ) and "input_ids" in labels[0]):
																												lowerCamelCase__      :			Dict 						= self.tokenizer.pad(UpperCamelCase__    ,							**UpperCamelCase__  )
																												lowerCamelCase__      :			Optional[int] 						= targets["""input_ids"""]
																					else:
																												lowerCamelCase__      :			int 						= self.feature_extractor.feature_size
																												lowerCamelCase__      :			Union[str, Any] 						= self.feature_extractor.num_mel_bins
																												lowerCamelCase__      :			Optional[Any] 						= self.feature_extractor.pad(UpperCamelCase__    ,							*UpperCamelCase__    ,							**UpperCamelCase__  )
																												lowerCamelCase__      :			List[Any] 						= feature_size_hack
																												lowerCamelCase__      :			List[str] 						= targets["""input_values"""]
														else:
																					lowerCamelCase__      :			List[str] 						= None

														if inputs is None:
																					return targets

														if targets is not None:
																					lowerCamelCase__      :			str 						= labels

																					lowerCamelCase__      :			Optional[Any] 						= targets.get("""attention_mask"""  )
																					if decoder_attention_mask is not None:
																												lowerCamelCase__      :			List[Any] 						= decoder_attention_mask

														return inputs




							def 	lowerCamelCase_       (							self:       List[Any]    ,							*UpperCamelCase__:       Tuple    ,							**UpperCamelCase__:       Tuple  ):
														return self.tokenizer.batch_decode(*UpperCamelCase__    ,							**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str    ,							*UpperCamelCase__:       int    ,							**UpperCamelCase__:       Tuple  ):
														return self.tokenizer.decode(*UpperCamelCase__    ,							**UpperCamelCase__  )









'''simple docstring'''







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


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Optional[int]					  ={
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """rwkv"""
							a          = {"""max_position_embeddings""": """context_length"""}




							def __init__(							self:       Tuple    ,							UpperCamelCase__:       Optional[Any]=50_277    ,							UpperCamelCase__:       Union[str, Any]=1_024    ,							UpperCamelCase__:       Tuple=4_096    ,							UpperCamelCase__:       List[Any]=32    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       int=1e-5    ,							UpperCamelCase__:       Any=0    ,							UpperCamelCase__:       str=0    ,							UpperCamelCase__:       Union[str, Any]=6    ,							UpperCamelCase__:       Optional[int]=False    ,							UpperCamelCase__:       Dict=True    ,							**UpperCamelCase__:       Dict    ,							):
														lowerCamelCase__      :			Dict 						= vocab_size
														lowerCamelCase__      :			Optional[Any] 						= context_length
														lowerCamelCase__      :			Optional[Any] 						= hidden_size
														lowerCamelCase__      :			Any 						= num_hidden_layers
														lowerCamelCase__      :			int 						= attention_hidden_size if attention_hidden_size is not None else hidden_size
														lowerCamelCase__      :			Union[str, Any] 						= intermediate_size if intermediate_size is not None else 4 * hidden_size
														lowerCamelCase__      :			List[str] 						= layer_norm_epsilon
														lowerCamelCase__      :			int 						= rescale_every
														lowerCamelCase__      :			Optional[int] 						= use_cache

														lowerCamelCase__      :			Dict 						= bos_token_id
														lowerCamelCase__      :			Any 						= eos_token_id

														super().__init__(
														    tie_word_embeddings=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

