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
  
  
  _lowerCAmelCase     :			Optional[int]    =logging.get_logger(__name__)
  
  
  _lowerCAmelCase     :			List[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""",
  }
  
  _lowerCAmelCase     :			str    ="""
      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 __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      "bark_module"
  					__magic_name__     			=      ["past_key_values"]
  
  					__magic_name__     			=      {
  					    "num_attention_heads": "num_heads",
  					    "num_hidden_layers": "num_layers",
  					    "vocab_size": "input_vocab_size",
  					    "window_size": "block_size",
  					}
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__=1_0_2_4							,			lowerCamelCase__=1_0_0_4_8							,			lowerCamelCase__=1_0_0_4_8							,			lowerCamelCase__=1_2							,			lowerCamelCase__=1_2							,			lowerCamelCase__=7_6_8							,			lowerCamelCase__=0.0							,			lowerCamelCase__=True							,			lowerCamelCase__=0.02							,			lowerCamelCase__=True							,			**lowerCamelCase__							,			):
  						UpperCAmelCase__:  Union[str, Any]		       =   block_size
  						UpperCAmelCase__:  Tuple		       =   input_vocab_size
  						UpperCAmelCase__:  List[Any]		       =   output_vocab_size
  						UpperCAmelCase__:  Union[str, Any]		       =   num_layers
  						UpperCAmelCase__:  Any		       =   num_heads
  						UpperCAmelCase__:  List[Any]		       =   hidden_size
  						UpperCAmelCase__:  List[str]		       =   dropout
  						UpperCAmelCase__:  Tuple		       =   bias
  						UpperCAmelCase__:  List[str]		       =   use_cache
  						UpperCAmelCase__:  Optional[Any]		       =   initializer_range
  
  						super().__init__(**lowerCamelCase__	)
  
  
  
  
  
  					@classmethod
  					def       _UpperCAmelCase						(							cls							,			lowerCamelCase__							,			lowerCamelCase__ = None							,			lowerCamelCase__ = False							,			lowerCamelCase__ = False							,			lowerCamelCase__ = None							,			lowerCamelCase__ = "main"							,			**lowerCamelCase__							,			):
  						UpperCAmelCase__:  List[str]		       =   cache_dir
  						UpperCAmelCase__:  str		       =   force_download
  						UpperCAmelCase__:  List[str]		       =   local_files_only
  						UpperCAmelCase__:  List[str]		       =   revision
  
  						cls._set_token_in_kwargs(lowerCamelCase__							,			lowerCamelCase__	)
  
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   cls.get_config_dict(lowerCamelCase__							,			**lowerCamelCase__	)
  
  						# get the config dict if we are loading from Bark
  						if config_dict.get("model_type"	) == "bark":
  							UpperCAmelCase__:  Dict		       =   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(lowerCamelCase__							,			**lowerCamelCase__	)
  
  @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 __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      "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 __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      "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 __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      "fine_acoustics"
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__=True							,			lowerCamelCase__=8							,			lowerCamelCase__=1							,			**lowerCamelCase__	):
  						UpperCAmelCase__:  List[str]		       =   n_codes_total
  						UpperCAmelCase__:  Dict		       =   n_codes_given
  
  						super().__init__(tie_word_embeddings=lowerCamelCase__							,			**lowerCamelCase__	)
  
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      "bark"
  					__magic_name__     			=      True
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__ = None							,			lowerCamelCase__ = None							,			lowerCamelCase__ = None							,			lowerCamelCase__ = None							,			lowerCamelCase__=0.02							,			**lowerCamelCase__							,			):
  						if semantic_config is None:
  							UpperCAmelCase__:  Union[str, Any]		       =   {}
  							logger.info("semantic_config is None. initializing the semantic model with default values."	)
  
  						if coarse_acoustics_config is None:
  							UpperCAmelCase__:  str		       =   {}
  							logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."	)
  
  						if fine_acoustics_config is None:
  							UpperCAmelCase__:  Tuple		       =   {}
  							logger.info("fine_acoustics_config is None. initializing the fine model with default values."	)
  
  						if codec_config is None:
  							UpperCAmelCase__:  int		       =   {}
  							logger.info("codec_config is None. initializing the codec model with default values."	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   BarkSemanticConfig(**lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   BarkCoarseConfig(**lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   BarkFineConfig(**lowerCamelCase__	)
  						UpperCAmelCase__:  Union[str, Any]		       =   AutoConfig.for_model(**lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[Any]		       =   initializer_range
  
  						super().__init__(**lowerCamelCase__	)
  
  
  
  
  
  					@classmethod
  					def       _UpperCAmelCase						(							cls							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			**lowerCamelCase__							,			):
  						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()							,			**lowerCamelCase__							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   copy.deepcopy(self.__dict__	)
  
  						UpperCAmelCase__:  List[str]		       =   self.semantic_config.to_dict()
  						UpperCAmelCase__:  Optional[Any]		       =   self.coarse_acoustics_config.to_dict()
  						UpperCAmelCase__:  Any		       =   self.fine_acoustics_config.to_dict()
  						UpperCAmelCase__:  List[str]		       =   self.codec_config.to_dict()
  
  						UpperCAmelCase__:  Union[str, Any]		       =   self.__class__.model_type
  						return output
style_context: 
  
  import numpy as np
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)
  
  	# Add up the all the exponentials
  	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)
  
  	# Divide every exponent by the sum of all exponents
  	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents
  
  	return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))