code: 
  
  
  
  import enum
  import inspect
  from typing import Iterable, List, Optional, Tuple, Union
  
  
  
  
  
  
  class 	lowercase		(				enum.Enum):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Dict	     =							"timm"
  	a__   :      Optional[int]	     =							"transformers"
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[Iterable[str]]    ,lowerCAmelCase_     :      Optional[Iterable[int]]    ,lowerCAmelCase_     :      Optional[Iterable[str]]       )   ->				Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if stage_names is None:
  												raise ValueError("""Stage_names must be set for transformers backbones"""       )
  
  						if out_features is not None:
  												if not isinstance(lowerCAmelCase_    ,(list,)       ):
  																		raise ValueError(F"""out_features must be a list {type(lowerCAmelCase_       )}"""       )
  												if any(feat not in stage_names for feat in out_features       ):
  																		raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""       )
  
  						if out_indices is not None:
  												if not isinstance(lowerCAmelCase_    ,(list, tuple)       ):
  																		raise ValueError(F"""out_indices must be a list or tuple, got {type(lowerCAmelCase_       )}"""       )
  												if any(idx >= len(lowerCAmelCase_       ) for idx in out_indices       ):
  																		raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""       )
  
  						if out_features is not None and out_indices is not None:
  												if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
  																		raise ValueError("""out_features and out_indices should have the same length if both are set"""       )
  												if out_features != [stage_names[idx] for idx in out_indices]:
  																		raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""       )
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[List[str]]    ,lowerCAmelCase_     :      Optional[Union[List[int], Tuple[int]]]    ,lowerCAmelCase_     :      List[str]    ,)   ->				Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if out_indices is None and out_features is None:
  												UpperCAmelCase_=     [len(lowerCAmelCase_       ) - 1]
  												UpperCAmelCase_=     [stage_names[-1]]
  						elif out_indices is None and out_features is not None:
  												UpperCAmelCase_=     [stage_names.index(lowerCAmelCase_       ) for layer in out_features]
  						elif out_features is None and out_indices is not None:
  												UpperCAmelCase_=     [stage_names[idx] for idx in out_indices]
  						return out_features, out_indices
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[List[str]]    ,lowerCAmelCase_     :      Optional[Union[List[int], Tuple[int]]]    ,lowerCAmelCase_     :      List[str]    ,)   ->				Tuple[List[str], List[int]]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						verify_out_features_out_indices(out_features=lowerCAmelCase_    ,out_indices=lowerCAmelCase_    ,stage_names=lowerCAmelCase_       )
  						UpperCAmelCase_,     UpperCAmelCase_=     _align_output_features_output_indices(
  						    out_features=lowerCAmelCase_    ,out_indices=lowerCAmelCase_    ,stage_names=lowerCAmelCase_       )
  						# Verify that the aligned out_features and out_indices are valid
  						verify_out_features_out_indices(out_features=lowerCAmelCase_    ,out_indices=lowerCAmelCase_    ,stage_names=lowerCAmelCase_       )
  						return output_features, output_indices
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Optional[BackboneType]	     =							None
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Optional[int]  )    ->				None:
  							if getattr(self		,						"""_backbone"""		,						__UpperCAmelCase  ) is None:
  													raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""  )
  
  							# These will diagree with the defaults for the transformers models e.g. for resnet50
  							# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
  							# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
  							UpperCAmelCase_=     [stage["""module"""] for stage in self._backbone.feature_info.info]
  							UpperCAmelCase_=     [stage["""num_chs"""] for stage in self._backbone.feature_info.info]
  							UpperCAmelCase_=     self._backbone.feature_info.out_indices
  							UpperCAmelCase_=     self._backbone.feature_info.module_name()
  
  							# We verify the out indices and out features are valid
  							verify_out_features_out_indices(
  							    out_features=__UpperCAmelCase		,						out_indices=__UpperCAmelCase		,						stage_names=self.stage_names  )
  							UpperCAmelCase_,     UpperCAmelCase_=     out_features, out_indices
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		int  )    ->				None:
  							UpperCAmelCase_=     getattr(__UpperCAmelCase		,						"""stage_names"""  )
  							UpperCAmelCase_=     getattr(__UpperCAmelCase		,						"""out_features"""		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     getattr(__UpperCAmelCase		,						"""out_indices"""		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     stage_names
  							UpperCAmelCase_,     UpperCAmelCase_=     get_aligned_output_features_output_indices(
  							    out_features=__UpperCAmelCase		,						out_indices=__UpperCAmelCase		,						stage_names=__UpperCAmelCase  )
  							# Number of channels for each stage. This is set in the transformer backbone model init
  							UpperCAmelCase_=     None
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		str  )    ->				None:
  							UpperCAmelCase_=     config
  
  							UpperCAmelCase_=     getattr(__UpperCAmelCase		,						"""use_timm_backbone"""		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS
  
  							if self.backbone_type == BackboneType.TIMM:
  													self._init_timm_backbone(__UpperCAmelCase  )
  							elif self.backbone_type == BackboneType.TRANSFORMERS:
  													self._init_transformers_backbone(__UpperCAmelCase  )
  							else:
  													raise ValueError(F"""backbone_type {self.backbone_type} not supported."""  )
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				List[str]:
  							return self._out_features
  
  
  
  
  	@out_features.setter
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		List[str]  )    ->				Any:
  							UpperCAmelCase_,     UpperCAmelCase_=     get_aligned_output_features_output_indices(
  							    out_features=__UpperCAmelCase		,						out_indices=__UpperCAmelCase		,						stage_names=self.stage_names  )
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				int:
  							return self._out_indices
  
  
  
  
  	@out_indices.setter
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple		,						__UpperCAmelCase			:		Union[Tuple[int], List[int]]  )    ->				str:
  							UpperCAmelCase_,     UpperCAmelCase_=     get_aligned_output_features_output_indices(
  							    out_features=__UpperCAmelCase		,						out_indices=__UpperCAmelCase		,						stage_names=self.stage_names  )
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				List[Any]:
  							# the current backbones will output the number of channels for each stage
  							# even if that stage is not in the out_features list.
  							return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names  )}
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				Tuple:
  							return [self.out_feature_channels[name] for name in self.out_features]
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						*__UpperCAmelCase			:		Dict		,						**__UpperCAmelCase			:		Any  )    ->				str:
  							UpperCAmelCase_=     dict(inspect.signature(self.forward  ).parameters  )
  							UpperCAmelCase_=     {k: v for k, v in kwargs.items() if k in signature}
  							return self(*__UpperCAmelCase		,						**__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[bool] = None		,						__UpperCAmelCase			:		Optional[bool] = None		,						__UpperCAmelCase			:		Optional[bool] = None		,						)    ->				List[Any]:
  							raise NotImplementedError("""This method should be implemented by the derived class."""  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				int:
  							UpperCAmelCase_=     super().to_dict()
  							UpperCAmelCase_=     output.pop("""_out_features"""  )
  							UpperCAmelCase_=     output.pop("""_out_indices"""  )
  							return output
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				int:
  							return self._out_features
  
  
  
  
  	@out_features.setter
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		List[str]  )    ->				Dict:
  							UpperCAmelCase_,     UpperCAmelCase_=     get_aligned_output_features_output_indices(
  							    out_features=__UpperCAmelCase		,						out_indices=__UpperCAmelCase		,						stage_names=self.stage_names  )
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Optional[int]:
  							return self._out_indices
  
  
  
  
  	@out_indices.setter
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple		,						__UpperCAmelCase			:		Union[Tuple[int], List[int]]  )    ->				Optional[int]:
  							UpperCAmelCase_,     UpperCAmelCase_=     get_aligned_output_features_output_indices(
  							    out_features=__UpperCAmelCase		,						out_indices=__UpperCAmelCase		,						stage_names=self.stage_names  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     super().to_dict()
  							UpperCAmelCase_=     output.pop("""_out_features"""  )
  							UpperCAmelCase_=     output.pop("""_out_indices"""  )
  							return output
  
  
  
  
  
style_context: 
  
  
  
  import copy
  from typing import Dict
  
  from ...generation.configuration_utils import GenerationConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Optional[int]	     =							"semantic"
  
  
  
  
  	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						eos_token_id=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						max_new_tokens=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     text_encoding_offset
  							UpperCAmelCase_=     text_pad_token
  							UpperCAmelCase_=     eos_token_id
  							UpperCAmelCase_=     semantic_infer_token
  							UpperCAmelCase_=     semantic_vocab_size
  							UpperCAmelCase_=     max_input_semantic_length
  							UpperCAmelCase_=     semantic_rate_hz
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"coarse_acoustics"
  
  
  
  
  	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     coarse_semantic_pad_token
  							UpperCAmelCase_=     coarse_rate_hz
  							UpperCAmelCase_=     n_coarse_codebooks
  							UpperCAmelCase_=     coarse_infer_token
  							UpperCAmelCase_=     max_coarse_input_length
  							UpperCAmelCase_=     max_coarse_history
  							UpperCAmelCase_=     sliding_window_len
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"fine_acoustics"
  
  
  
  
  	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
  							super().__init__(temperature=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     max_fine_history_length
  							UpperCAmelCase_=     max_fine_input_length
  							UpperCAmelCase_=     n_fine_codebooks
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"bark"
  	a__   :      List[str]	     =							True
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
  							if semantic_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )
  
  							if coarse_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )
  
  							if fine_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""  )
  
  							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )
  
  							UpperCAmelCase_=     sample_rate
  							UpperCAmelCase_=     codebook_size
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				Any:
  							return cls(
  							    semantic_config=semantic_config.to_dict()		,						coarse_acoustics_config=coarse_acoustics_config.to_dict()		,						fine_acoustics_config=fine_acoustics_config.to_dict()		,						**__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
  							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )
  
  							UpperCAmelCase_=     self.semantic_config.to_dict()
  							UpperCAmelCase_=     self.coarse_acoustics_config.to_dict()
  							UpperCAmelCase_=     self.fine_acoustics_config.to_dict()
  
  							UpperCAmelCase_=     self.__class__.model_type
  							return output
  
  
  
  
  