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   : 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
  import tempfile
  import unittest
  from typing import List
  
  from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
  from transformers.testing_utils import require_tokenizers
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  
  
  
  
  
  
  @require_tokenizers
  class     lowerCamelCase_			(  unittest.TestCase  ):
  	_lowercase		:      List[str]    				=							EsmTokenizer
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		super().setUp()
  		__A   : Dict								= tempfile.mkdtemp()
  		# fmt: off
  		__A   : List[str]								= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
  		# fmt: on
  		__A   : int								= os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]       )
  		with open(self.vocab_file				,			"""w"""				,			encoding="""utf-8"""       ) as vocab_writer:
  			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict				,			**__A	:    int       ):
  		return [self.get_tokenizer(**__A       )]
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			**__A	:    List[Any]       ):
  		return self.tokenizer_class.from_pretrained(self.tmpdirname				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : int								= tokenizer.tokenize("""LAGVS"""       )
  		self.assertListEqual(__A				,			["""L""", """A""", """G""", """V""", """S"""]       )
  		self.assertListEqual(tokenizer.convert_tokens_to_ids(__A       )				,			[4, 5, 6, 7, 8]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str       ):
  		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : int								= """LAGVS"""
  		self.assertListEqual(tokenizer.encode(__A       )				,			[0, 4, 5, 6, 7, 8, 2]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		__A   : str								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : Tuple								= ["""LAGVS""", """WCB"""]
  		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]
  
  		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		__A   : Dict								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : Any								= ["""LAGVS""", """WCB"""]
  		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]
  
  		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : Optional[int]								= self.get_tokenizers(fast=__A       )
  		for tokenizer in tokenizers:
  			with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
  				__A   : Any								= """<unk>"""
  				__A   : str								= """<mask>"""
  
  				__A   : Optional[Any]								= tokenizer.tokenize(__A       )
  				__A   : str								= tokenizer.tokenize(__A       )
  
  				self.assertEqual(len(__A       )				,			1       )
  				self.assertEqual(len(__A       )				,			1       )
  				self.assertEqual(token_a[0]				,			__A       )
  				self.assertEqual(token_a[0]				,			__A       )
  
  
  
  
  
  