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
  
  
  __A  							=  logging.get_logger(__name__)
  
  
  __A  							=  {
      "suno/bark-small": "https://huggingface.co/suno/bark-small/resolve/main/config.json",
      "suno/bark": "https://huggingface.co/suno/bark/resolve/main/config.json",
  }
  
  __A  							=  "\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n"
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Optional[Any]   =   'bark_module'
      lowerCamelCase  :       Tuple   =   ['past_key_values']
  
      lowerCamelCase  :       Dict   =   {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
          'vocab_size': 'input_vocab_size',
          'window_size': 'block_size',
      }
  
  
  
      def __init__(							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=1024   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=10048   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=10048   ,    __SCREAMING_SNAKE_CASE			:    Tuple=12   ,    __SCREAMING_SNAKE_CASE			:    Any=12   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=768   ,    __SCREAMING_SNAKE_CASE			:    Tuple=0.0   ,    __SCREAMING_SNAKE_CASE			:    str=True   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.02   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=True   ,    **__SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    )			->       Optional[Any]:
             __UpperCAmelCase    							=block_size
             __UpperCAmelCase    							=input_vocab_size
             __UpperCAmelCase    							=output_vocab_size
             __UpperCAmelCase    							=num_layers
             __UpperCAmelCase    							=num_heads
             __UpperCAmelCase    							=hidden_size
             __UpperCAmelCase    							=dropout
             __UpperCAmelCase    							=bias
             __UpperCAmelCase    							=use_cache
             __UpperCAmelCase    							=initializer_range
  
             super().__init__(**__SCREAMING_SNAKE_CASE			)
  
  
  
      @classmethod
      def _a       (							cls			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, os.PathLike]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Union[str, os.PathLike]] = None   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[Union[str, bool]] = None   ,    __SCREAMING_SNAKE_CASE			:    str = "main"   ,    **__SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    )			->       "PretrainedConfig":
             __UpperCAmelCase    							=cache_dir
             __UpperCAmelCase    							=force_download
             __UpperCAmelCase    							=local_files_only
             __UpperCAmelCase    							=revision
  
             cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase							,   __UpperCAmelCase    							=cls.get_config_dict(__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
             # get the config dict if we are loading from Bark
             if config_dict.get("""model_type"""			) == "bark":
                    __UpperCAmelCase    							=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(__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
  
  
  
  @add_start_docstrings(
      BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkSemanticConfig'				,				model='BarkSemanticModel'							)				,				'\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'				,				)
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Any   =   'semantic'
  
  
  
  
  @add_start_docstrings(
      BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkCoarseConfig'				,				model='BarkCoarseModel'							)				,				'\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'				,				)
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   'coarse_acoustics'
  
  
  
  
  @add_start_docstrings(
      BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkFineConfig'				,				model='BarkFineModel'							)				,				'\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'				,				)
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   'fine_acoustics'
  
  
  
      def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=True   ,    __SCREAMING_SNAKE_CASE			:    int=8   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=1   ,    **__SCREAMING_SNAKE_CASE			:    int			)			->       List[Any]:
             __UpperCAmelCase    							=n_codes_total
             __UpperCAmelCase    							=n_codes_given
  
             super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			)
  
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Tuple   =   'bark'
      lowerCamelCase  :       Dict   =   True
  
  
  
      def __init__(							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Dict = None   ,    __SCREAMING_SNAKE_CASE			:    Dict = None   ,    __SCREAMING_SNAKE_CASE			:    Dict = None   ,    __SCREAMING_SNAKE_CASE			:    Dict = None   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=0.02   ,    **__SCREAMING_SNAKE_CASE			:    str   ,    )			->       Dict:
             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."""			)
  
             if codec_config is None:
                    __UpperCAmelCase    							={}
                    logger.info("""codec_config is None. initializing the codec model with default values."""			)
  
             __UpperCAmelCase    							=BarkSemanticConfig(**__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=BarkCoarseConfig(**__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=BarkFineConfig(**__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=AutoConfig.for_model(**__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=initializer_range
  
             super().__init__(**__SCREAMING_SNAKE_CASE			)
  
  
  
      @classmethod
      def _a       (							cls			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    BarkSemanticConfig   ,    __SCREAMING_SNAKE_CASE			:    BarkCoarseConfig   ,    __SCREAMING_SNAKE_CASE			:    BarkFineConfig   ,    __SCREAMING_SNAKE_CASE			:    AutoConfig   ,    **__SCREAMING_SNAKE_CASE			:    int   ,    )			->       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()   ,    **__SCREAMING_SNAKE_CASE   ,    )
  
  
  
      def _a       (							self			:    Optional[int]			)			->       int:
             __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.codec_config.to_dict()
  
             __UpperCAmelCase    							=self.__class__.model_type
             return output
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from collections import defaultdict
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int = 5		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=defaultdict(A_		)
         __UpperCAmelCase    							=0
  
         while True:
                __UpperCAmelCase    							=get_digits(A_		)
                freqs[digits].append(A_		)
  
                if len(freqs[digits]		) == max_base:
                       __UpperCAmelCase    							=freqs[digits][0] ** 3
                       return base
  
                num += 1
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return "".join(sorted(str(num**3		)		)		)
  
  
  if __name__ == "__main__":
     print(F"""{solution() = }""")
  
  
  
  
  