code: 
  
  import copy
  import os
  from typing import Dict, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import add_start_docstrings, logging
  from ..auto import AutoConfig
  
  
  UpperCAmelCase_       :				Union[str, Any]		   =      logging.get_logger(__name__)
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      {
      '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
      '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
  }
  
  UpperCAmelCase_       :				Dict		   =      '''
      This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
      according to the specified arguments, defining the model architecture. Instantiating a configuration with the
      defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
      architecture.
  
      Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
      documentation from [`PretrainedConfig`] for more information.
  
      Args:
          block_size (`int`, *optional*, defaults to 1024):
              The maximum sequence length that this model might ever be used with. Typically set this to something large
              just in case (e.g., 512 or 1024 or 2048).
          input_vocab_size (`int`, *optional*, defaults to 10_048):
              Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
              `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
              regards to the chosen sub-model.
          output_vocab_size (`int`, *optional*, defaults to 10_048):
              Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
              by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
              with regards to the chosen sub-model.
          num_layers (`int`, *optional*, defaults to 12):
              Number of hidden layers in the given sub-model.
          num_heads (`int`, *optional*, defaults to 12):
              Number of attention heads for each attention layer in the Transformer architecture.
          hidden_size (`int`, *optional*, defaults to 768):
              Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
          dropout (`float`, *optional*, defaults to 0.0):
              The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
          bias (`bool`, *optional*, defaults to `True`):
              Whether or not to use bias in the linear layers and layer norm layers.
          initializer_range (`float`, *optional*, defaults to 0.02):
              The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
          use_cache (`bool`, *optional*, defaults to `True`):
              Whether or not the model should return the last key/values attentions (not used by all models).
  '''
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      List[Any]    				=							'''bark_module'''
  	_lowercase		:      Tuple    				=							['''past_key_values''']
  
  	_lowercase		:      List[Any]    				=							{
  	    '''num_attention_heads''': '''num_heads''',
  	    '''num_hidden_layers''': '''num_layers''',
  	    '''vocab_size''': '''input_vocab_size''',
  	    '''window_size''': '''block_size''',
  	}
  
  
  
  
  	def __init__(    self	:    Optional[Any]				,			__A	:    Any=1024				,			__A	:    Optional[Any]=1_0048				,			__A	:    Optional[int]=1_0048				,			__A	:    Tuple=12				,			__A	:    List[str]=12				,			__A	:    Optional[Any]=768				,			__A	:    Optional[int]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Optional[int]=0.0_2				,			__A	:    Tuple=True				,			**__A	:    Tuple				,			):
  		__A   : Dict								= block_size
  		__A   : Union[str, Any]								= input_vocab_size
  		__A   : Any								= output_vocab_size
  		__A   : Optional[int]								= num_layers
  		__A   : Optional[Any]								= num_heads
  		__A   : int								= hidden_size
  		__A   : List[Any]								= dropout
  		__A   : Optional[Any]								= bias
  		__A   : Any								= use_cache
  		__A   : Optional[Any]								= initializer_range
  
  		super().__init__(**__A       )
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    Dict				,			__A	:    Union[str, os.PathLike]				,			__A	:    Optional[Union[str, os.PathLike]] = None				,			__A	:    bool = False				,			__A	:    bool = False				,			__A	:    Optional[Union[str, bool]] = None				,			__A	:    str = "main"				,			**__A	:    Any				,			):
  		__A   : str								= cache_dir
  		__A   : Dict								= force_download
  		__A   : str								= local_files_only
  		__A   : List[str]								= revision
  
  		cls._set_token_in_kwargs(__A				,			__A       )
  
  		__A						,							__A   : Tuple								= cls.get_config_dict(__A				,			**__A       )
  
  		# get the config dict if we are loading from Bark
  		if config_dict.get("""model_type"""       ) == "bark":
  			__A   : Optional[int]								= config_dict[F"""{cls.model_type}_config"""]
  
  		if "model_type" in config_dict and hasattr(cls				,			"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
  			logger.warning(
  			    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
  			    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )
  
  		return cls.from_dict(__A				,			**__A       )
  
  
  
  
  
  
  @add_start_docstrings(
      BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkSemanticConfig'''	,			model='''BarkSemanticModel'''  )	,			'''
      Example:
  
      ```python
      >>> from transformers import BarkSemanticConfig, BarkSemanticModel
  
      >>> # Initializing a Bark sub-module style configuration
      >>> configuration = BarkSemanticConfig()
  
      >>> # Initializing a model (with random weights) from the suno/bark style configuration
      >>> model = BarkSemanticModel(configuration)
  
      >>> # Accessing the model configuration
      >>> configuration = model.config
      ```'''	,			)
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Any    				=							'''semantic'''
  
  
  
  
  
  
  @add_start_docstrings(
      BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkCoarseConfig'''	,			model='''BarkCoarseModel'''  )	,			'''
      Example:
  
      ```python
      >>> from transformers import BarkCoarseConfig, BarkCoarseModel
  
      >>> # Initializing a Bark sub-module style configuration
      >>> configuration = BarkCoarseConfig()
  
      >>> # Initializing a model (with random weights) from the suno/bark style configuration
      >>> model = BarkCoarseModel(configuration)
  
      >>> # Accessing the model configuration
      >>> configuration = model.config
      ```'''	,			)
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      int    				=							'''coarse_acoustics'''
  
  
  
  
  
  
  @add_start_docstrings(
      BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkFineConfig'''	,			model='''BarkFineModel'''  )	,			'''
          n_codes_total (`int`, *optional*, defaults to 8):
              The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
          n_codes_given (`int`, *optional*, defaults to 1):
              The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
              sub-models.
      Example:
  
      ```python
      >>> from transformers import BarkFineConfig, BarkFineModel
  
      >>> # Initializing a Bark sub-module style configuration
      >>> configuration = BarkFineConfig()
  
      >>> # Initializing a model (with random weights) from the suno/bark style configuration
      >>> model = BarkFineModel(configuration)
  
      >>> # Accessing the model configuration
      >>> configuration = model.config
      ```'''	,			)
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      str    				=							'''fine_acoustics'''
  
  
  
  
  	def __init__(    self	:    Any				,			__A	:    Tuple=True				,			__A	:    List[Any]=8				,			__A	:    int=1				,			**__A	:    List[Any]       ):
  		__A   : int								= n_codes_total
  		__A   : List[str]								= n_codes_given
  
  		super().__init__(tie_word_embeddings=__A				,			**__A       )
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      str    				=							'''bark'''
  	_lowercase		:      Union[str, Any]    				=							True
  
  
  
  
  	def __init__(    self	:    List[str]				,			__A	:    Dict = None				,			__A	:    Dict = None				,			__A	:    Dict = None				,			__A	:    Dict = None				,			__A	:    Optional[Any]=0.0_2				,			**__A	:    Union[str, Any]				,			):
  		if semantic_config is None:
  			__A   : List[Any]								= {}
  			logger.info("""semantic_config is None. initializing the semantic model with default values."""       )
  
  		if coarse_acoustics_config is None:
  			__A   : Union[str, Any]								= {}
  			logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""       )
  
  		if fine_acoustics_config is None:
  			__A   : List[Any]								= {}
  			logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""       )
  
  		if codec_config is None:
  			__A   : List[Any]								= {}
  			logger.info("""codec_config is None. initializing the codec model with default values."""       )
  
  		__A   : Dict								= BarkSemanticConfig(**__A       )
  		__A   : Optional[Any]								= BarkCoarseConfig(**__A       )
  		__A   : str								= BarkFineConfig(**__A       )
  		__A   : int								= AutoConfig.for_model(**__A       )
  
  		__A   : List[str]								= initializer_range
  
  		super().__init__(**__A       )
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    Tuple				,			__A	:    BarkSemanticConfig				,			__A	:    BarkCoarseConfig				,			__A	:    BarkFineConfig				,			__A	:    AutoConfig				,			**__A	:    Optional[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()				,			codec_config=codec_config.to_dict()				,			**__A				,			)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
  		__A   : str								= copy.deepcopy(self.__dict__       )
  
  		__A   : List[Any]								= self.semantic_config.to_dict()
  		__A   : Dict								= self.coarse_acoustics_config.to_dict()
  		__A   : List[str]								= self.fine_acoustics_config.to_dict()
  		__A   : List[Any]								= self.codec_config.to_dict()
  
  		__A   : Union[str, Any]								= self.__class__.model_type
  		return output
  
  
  
  
  
  
style_context: 
  
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  
  
  
  
  
  
  class     lowerCamelCase_			(  tf.keras.layers.Layer  ):
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    List				,			__A	:    bool				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    str = "longest"				,			__A	:    bool = True				,			__A	:    int = 512				,			__A	:    int = None				,			__A	:    bool = True				,			__A	:    bool = True				,			__A	:    bool = True				,			**__A	:    Any				,			):
  		super().__init__()
  		if use_fast_bert_tokenizer:
  			__A   : Optional[int]								= FastBertTokenizer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case_nfd_strip_accents=__A				,			**__A       )
  		else:
  			__A   : Optional[Any]								= tf.lookup.StaticVocabularyTable(
  			    tf.lookup.KeyValueTensorInitializer(
  			        keys=__A				,			key_dtype=tf.string				,			values=tf.range(tf.size(__A				,			out_type=tf.intaa       )				,			dtype=tf.intaa       )				,			value_dtype=tf.intaa				,			)				,			num_oov_buckets=1				,			)
  			__A   : Any								= BertTokenizerLayer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case=__A				,			**__A       )
  
  		__A   : Union[str, Any]								= vocab_list
  		__A   : List[str]								= do_lower_case
  		__A   : List[Any]								= cls_token_id or vocab_list.index("""[CLS]"""       )
  		__A   : Tuple								= sep_token_id or vocab_list.index("""[SEP]"""       )
  		__A   : str								= pad_token_id or vocab_list.index("""[PAD]"""       )
  		__A   : Optional[int]								= ShrinkLongestTrimmer(max_length - 3				,			axis=1       )  # Allow room for special tokens
  		__A   : Union[str, Any]								= max_length
  		__A   : List[Any]								= padding
  		__A   : Optional[int]								= truncation
  		__A   : Tuple								= pad_to_multiple_of
  		__A   : Optional[Any]								= return_token_type_ids
  		__A   : int								= return_attention_mask
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[Any]				,			__A	:    "PreTrainedTokenizerBase"				,			**__A	:    List[str]       ):  # noqa: F821
  		__A   : Any								= kwargs.pop("""do_lower_case"""				,			__A       )
  		__A   : Dict								= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
  		__A   : List[str]								= kwargs.pop("""cls_token_id"""				,			__A       )
  		__A   : str								= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
  		__A   : str								= kwargs.pop("""sep_token_id"""				,			__A       )
  		__A   : Dict								= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
  		__A   : Tuple								= kwargs.pop("""pad_token_id"""				,			__A       )
  		__A   : List[Any]								= tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
  		__A   : Union[str, Any]								= tokenizer.get_vocab()
  		__A   : Any								= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,			key=lambda __A       : x[1]       )
  		__A   : int								= [entry[0] for entry in vocab]
  		return cls(
  		    vocab_list=__A				,			do_lower_case=__A				,			cls_token_id=__A				,			sep_token_id=__A				,			pad_token_id=__A				,			**__A				,			)
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[str]				,			__A	:    Union[str, os.PathLike]				,			*__A	:    Tuple				,			**__A	:    Optional[int]       ):
  		try:
  			__A   : int								= BertTokenizer.from_pretrained(__A				,			*__A				,			**__A       )
  		except:  # noqa: E722
  			from .tokenization_bert_fast import BertTokenizerFast
  
  			__A   : Optional[int]								= BertTokenizerFast.from_pretrained(__A				,			*__A				,			**__A       )
  		return cls.from_tokenizer(__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Optional[Any]       ):
  		if self.do_lower_case:
  			__A   : Tuple								= case_fold_utfa(__A       )
  		__A   : Optional[int]								= self.tf_tokenizer.tokenize(__A       )
  		return tokens.merge_dims(1				,			-1       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    Optional[Any]				,			__A	:    Dict=None				,			__A	:    List[Any]=None				,			__A	:    str=None				,			__A	:    Union[str, Any]=None				,			__A	:    Union[str, Any]=None				,			__A	:    int=None				,			__A	:    Any=None				,			):
  		if padding is None:
  			__A   : List[Any]								= self.padding
  		if padding not in ("longest", "max_length"):
  			raise ValueError("""Padding must be either 'longest' or 'max_length'!"""       )
  		if max_length is not None and text_pair is not None:
  			# Because we have to instantiate a Trimmer to do it properly
  			raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""       )
  		if max_length is None:
  			__A   : Any								= self.max_length
  		if truncation is None:
  			__A   : int								= self.truncation
  		if pad_to_multiple_of is None:
  			__A   : List[str]								= self.pad_to_multiple_of
  		if return_token_type_ids is None:
  			__A   : int								= self.return_token_type_ids
  		if return_attention_mask is None:
  			__A   : Any								= self.return_attention_mask
  		if not isinstance(__A				,			tf.Tensor       ):
  			__A   : str								= tf.convert_to_tensor(__A       )
  		if text_pair is not None and not isinstance(__A				,			tf.Tensor       ):
  			__A   : Optional[Any]								= tf.convert_to_tensor(__A       )
  		if text_pair is not None:
  			if text.shape.rank > 1:
  				raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""       )
  			if text_pair.shape.rank > 1:
  				raise ValueError("""text_pair should not be multidimensional!"""       )
  		if text.shape.rank == 2:
  			__A						,							__A   : Optional[int]								= text[:, 0], text[:, 1]
  		__A   : List[Any]								= self.unpaired_tokenize(__A       )
  		if text_pair is None:  # Unpaired text
  			if truncation:
  				__A   : Union[str, Any]								= text[:, : max_length - 2]  # Allow room for special tokens
  			__A						,							__A   : List[Any]								= combine_segments(
  			    (text,)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		else:  # Paired text
  			__A   : Tuple								= self.unpaired_tokenize(__A       )
  			if truncation:
  				__A						,							__A   : Optional[int]								= self.paired_trimmer.trim([text, text_pair]       )
  			__A						,							__A   : int								= combine_segments(
  			    (text, text_pair)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		if padding == "longest":
  			__A   : str								= input_ids.bounding_shape(axis=1       )
  			if pad_to_multiple_of is not None:
  				# No ceiling division in tensorflow, so we negate floordiv instead
  				__A   : Optional[Any]								= pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,			__A       ))
  		else:
  			__A   : Dict								= max_length
  
  		__A						,							__A   : str								= pad_model_inputs(__A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  		__A   : List[str]								= {"""input_ids""": input_ids}
  		if return_attention_mask:
  			__A   : Any								= attention_mask
  		if return_token_type_ids:
  			__A						,							__A   : Tuple								= pad_model_inputs(
  			    __A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  			__A   : List[str]								= token_type_ids
  		return output
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return {
  		    "vocab_list": self.vocab_list,
  		    "do_lower_case": self.do_lower_case,
  		    "cls_token_id": self.cls_token_id,
  		    "sep_token_id": self.sep_token_id,
  		    "pad_token_id": self.pad_token_id,
  		}
  
  
  
  
  
  