




'''simple docstring'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_snake_case : Dict  				=						logging.get_logger(__name__)

_snake_case : int  				=						{
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class      A       (						_a       ):
							lowercase_							     =							'dpt'







							def __init__(    self			:    List[Any]				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    str=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[Any]=30_72				,     lowerCAmelCase_			:    Any="gelu"				,     lowerCAmelCase_			:    Any=0.0				,     lowerCAmelCase_			:    Union[str, Any]=0.0				,     lowerCAmelCase_			:    List[str]=0.0_2				,     lowerCAmelCase_			:    Any=1e-12				,     lowerCAmelCase_			:    List[Any]=3_84				,     lowerCAmelCase_			:    Optional[Any]=16				,     lowerCAmelCase_			:    Optional[int]=3				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    Dict=[2, 5, 8, 11]				,     lowerCAmelCase_			:    Optional[Any]="project"				,     lowerCAmelCase_			:    int=[4, 2, 1, 0.5]				,     lowerCAmelCase_			:    Optional[Any]=[96, 1_92, 3_84, 7_68]				,     lowerCAmelCase_			:    List[Any]=2_56				,     lowerCAmelCase_			:    Optional[int]=-1				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    Optional[int]=True				,     lowerCAmelCase_			:    Any=0.4				,     lowerCAmelCase_			:    List[str]=2_55				,     lowerCAmelCase_			:    Any=0.1				,     lowerCAmelCase_			:    Tuple=[1, 10_24, 24, 24]				,     lowerCAmelCase_			:    Optional[int]=[0, 1]				,     lowerCAmelCase_			:    int=None				,     **lowerCAmelCase_			:    Dict				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )

														_a      	=							hidden_size
														_a      	=							is_hybrid

														if self.is_hybrid:
																					if backbone_config is None:
																												logger.info('''Initializing the config with a `BiT` backbone.'''   )
																												_a      	=							{
																												    '''global_padding''': '''same''',
																												    '''layer_type''': '''bottleneck''',
																												    '''depths''': [3, 4, 9],
																												    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
																												    '''embedding_dynamic_padding''': True,
																												}
																												_a      	=							BitConfig(**lowerCAmelCase_   )
																					elif isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ):
																												logger.info('''Initializing the config with a `BiT` backbone.'''   )
																												_a      	=							BitConfig(**lowerCAmelCase_   )
																					elif isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ):
																												_a      	=							backbone_config
																					else:
																												raise ValueError(
																												    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'   )

																					_a      	=							backbone_featmap_shape
																					_a      	=							neck_ignore_stages

																					if readout_type != "project":
																												raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''   )
														else:
																					_a      	=							None
																					_a      	=							None
																					_a      	=							[]

														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							intermediate_size
														_a      	=							hidden_act
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							image_size
														_a      	=							patch_size
														_a      	=							num_channels
														_a      	=							qkv_bias
														_a      	=							backbone_out_indices
														if readout_type not in ["ignore", "add", "project"]:
																					raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''   )
														_a      	=							readout_type
														_a      	=							reassemble_factors
														_a      	=							neck_hidden_sizes
														_a      	=							fusion_hidden_size
														_a      	=							head_in_index
														_a      	=							use_batch_norm_in_fusion_residual
														# auxiliary head attributes (semantic segmentation)
														_a      	=							use_auxiliary_head
														_a      	=							auxiliary_loss_weight
														_a      	=							semantic_loss_ignore_index
														_a      	=							semantic_classifier_dropout







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							copy.deepcopy(self.__dict__   )

														if output["backbone_config"] is not None:
																					_a      	=							self.backbone_config.to_dict()

														_a      	=							self.__class__.model_type
														return output



from ...processing_utils import ProcessorMixin



class    a_ (			_a    ):
			a	:			Optional[int]					   =							'''SpeechT5FeatureExtractor'''
			a	:			List[Any]					   =							'''SpeechT5Tokenizer'''






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )






			def __call__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""audio"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""audio_target"""				,			__UpperCamelCase  )
									_lowercase					       =					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:
															_lowercase					       =					self.feature_extractor(__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
									elif text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

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

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""input_values"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""input_ids"""				,			__UpperCamelCase  )
									_lowercase					       =					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:
															_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
									elif input_ids is not None:
															_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

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

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )