



from ...processing_utils import ProcessorMixin



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	"""SpeechT5FeatureExtractor"""
							lowerCamelCase__         =	"""SpeechT5Tokenizer"""







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														super().__init__(__a,    __a)







							def __call__(     self,    *__a,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					kwargs.pop("audio",    __a)
														_lowerCAmelCase  :							Any				     =					kwargs.pop("text",    __a)
														_lowerCAmelCase  :							Any				     =					kwargs.pop("text_target",    __a)
														_lowerCAmelCase  :							Optional[int]				     =					kwargs.pop("audio_target",    __a)
														_lowerCAmelCase  :							Tuple				     =					kwargs.pop("sampling_rate",    __a)

														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  :							str				     =					self.feature_extractor(__a,    *__a,    sampling_rate=__a,    **__a)
														elif text is not None:
																					_lowerCAmelCase  :							int				     =					self.tokenizer(__a,    **__a)
														else:
																					_lowerCAmelCase  :							List[Any]				     =					None

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

														if inputs is None:
																					return targets

														if targets is not None:
																					_lowerCAmelCase  :							str				     =					labels

																					_lowerCAmelCase  :							Union[str, Any]				     =					targets.get("attention_mask")
																					if decoder_attention_mask is not None:
																												_lowerCAmelCase  :							Optional[Any]				     =					decoder_attention_mask

														return inputs







							def      snake_case__   (     self,    *__a,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					kwargs.pop("input_values",    __a)
														_lowerCAmelCase  :							int				     =					kwargs.pop("input_ids",    __a)
														_lowerCAmelCase  :							Optional[Any]				     =					kwargs.pop("labels",    __a)

														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  :							str				     =					self.feature_extractor.pad(__a,    *__a,    **__a)
														elif input_ids is not None:
																					_lowerCAmelCase  :							int				     =					self.tokenizer.pad(__a,    **__a)
														else:
																					_lowerCAmelCase  :							List[str]				     =					None

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

														if inputs is None:
																					return targets

														if targets is not None:
																					_lowerCAmelCase  :							int				     =					labels

																					_lowerCAmelCase  :							List[str]				     =					targets.get("attention_mask")
																					if decoder_attention_mask is not None:
																												_lowerCAmelCase  :							Union[str, Any]				     =					decoder_attention_mask

														return inputs







							def      snake_case__   (     self,    *__a,    **__a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    *__a,    **__a):

														'''simple docstring'''

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






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'upernet'







							def __init__(     self,    __a=None,    __a=512,    __a=0.02,    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=384,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														if backbone_config is None:
																					logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
																					_lowerCAmelCase  :							List[str]				     =					CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
														elif isinstance(__a,    __a):
																					_lowerCAmelCase  :							List[Any]				     =					backbone_config.get("model_type")
																					_lowerCAmelCase  :							Dict				     =					CONFIG_MAPPING[backbone_model_type]
																					_lowerCAmelCase  :							Optional[Any]				     =					config_class.from_dict(__a)

														_lowerCAmelCase  :							Tuple				     =					backbone_config
														_lowerCAmelCase  :							List[Any]				     =					hidden_size
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							str				     =					pool_scales
														_lowerCAmelCase  :							List[str]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Dict				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							Tuple				     =					auxiliary_in_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_channels
														_lowerCAmelCase  :							str				     =					auxiliary_num_convs
														_lowerCAmelCase  :							Union[str, Any]				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					loss_ignore_index







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							List[Any]				     =					self.backbone_config.to_dict()
														_lowerCAmelCase  :							Optional[Any]				     =					self.__class__.model_type
														return output
