




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 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	Dict		   =       "bark_module"
      UpperCamelCase_    :	Tuple		   =       ["past_key_values"]

      UpperCamelCase_    :	Optional[int]		   =       {
          "num_attention_heads": "num_heads",
          "num_hidden_layers": "num_layers",
          "vocab_size": "input_vocab_size",
          "window_size": "block_size",
      }



      def __init__(    self   :       Any    ,      A__   :       Tuple=1024    ,      A__   :       Tuple=10048    ,      A__   :       Union[str, Any]=10048    ,      A__   :       List[Any]=12    ,      A__   :       Any=12    ,      A__   :       List[Any]=768    ,      A__   :       Any=0.0    ,      A__   :       Union[str, Any]=True    ,      A__   :       int=0.02    ,      A__   :       List[str]=True    ,      **A__   :       int    ,      )					->  Union[str, Any]:
        _snake_case		=							block_size
        _snake_case		=							input_vocab_size
        _snake_case		=							output_vocab_size
        _snake_case		=							num_layers
        _snake_case		=							num_heads
        _snake_case		=							hidden_size
        _snake_case		=							dropout
        _snake_case		=							bias
        _snake_case		=							use_cache
        _snake_case		=							initializer_range

        super().__init__(**__lowerCamelCase	)







      @classmethod
      def 		UpperCamelCase_	(    cls   :       Tuple    ,      A__   :       Any    ,      A__   :       Optional[int] = None    ,      A__   :       Optional[Any] = False    ,      A__   :       Any = False    ,      A__   :       str = None    ,      A__   :       Union[str, Any] = "main"    ,      **A__   :       Dict    ,      )					->  "PretrainedConfig":
        _snake_case		=							cache_dir
        _snake_case		=							force_download
        _snake_case		=							local_files_only
        _snake_case		=							revision

        cls._set_token_in_kwargs(__lowerCamelCase    ,      __lowerCamelCase	)

        _snake_case		=							cls.get_config_dict(__lowerCamelCase    ,      **__lowerCamelCase	)

        # get the config dict if we are loading from Bark
        if config_dict.get('''model_type'''	) == "bark":
          _snake_case		=							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 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	Dict		   =       "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 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	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 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	Any		   =       "fine_acoustics"



      def __init__(    self   :       Union[str, Any]    ,      A__   :       int=True    ,      A__   :       Union[str, Any]=8    ,      A__   :       Any=1    ,      **A__   :       List[str]	)					->  Optional[Any]:
        _snake_case		=							n_codes_total
        _snake_case		=							n_codes_given

        super().__init__(tie_word_embeddings=__lowerCamelCase    ,      **__lowerCamelCase	)



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	Union[str, Any]		   =       "bark"
      UpperCamelCase_    :	List[str]		   =       True



      def __init__(    self   :       int    ,      A__   :       List[str] = None    ,      A__   :       List[str] = None    ,      A__   :       List[Any] = None    ,      A__   :       Optional[Any] = None    ,      A__   :       Optional[int]=0.02    ,      **A__   :       Optional[Any]    ,      )					->  Any:
        if semantic_config is None:
          _snake_case		=							{}
          logger.info('''semantic_config is None. initializing the semantic model with default values.'''	)

        if coarse_acoustics_config is None:
          _snake_case		=							{}
          logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''	)

        if fine_acoustics_config is None:
          _snake_case		=							{}
          logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''	)

        if codec_config is None:
          _snake_case		=							{}
          logger.info('''codec_config is None. initializing the codec model with default values.'''	)

        _snake_case		=							BarkSemanticConfig(**__lowerCamelCase	)
        _snake_case		=							BarkCoarseConfig(**__lowerCamelCase	)
        _snake_case		=							BarkFineConfig(**__lowerCamelCase	)
        _snake_case		=							AutoConfig.for_model(**__lowerCamelCase	)

        _snake_case		=							initializer_range

        super().__init__(**__lowerCamelCase	)



      @classmethod
      def 		UpperCamelCase_	(    cls   :       Any    ,      A__   :       Tuple    ,      A__   :       int    ,      A__   :       List[Any]    ,      A__   :       Any    ,      **A__   :       int    ,      )					->  Tuple:
        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   :       Optional[Any]	)					->  Any:
        _snake_case		=							copy.deepcopy(self.__dict__	)

        _snake_case		=							self.semantic_config.to_dict()
        _snake_case		=							self.coarse_acoustics_config.to_dict()
        _snake_case		=							self.fine_acoustics_config.to_dict()
        _snake_case		=							self.codec_config.to_dict()

        _snake_case		=							self.__class__.model_type
        return output




import numpy as np



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  _snake_case		=							np.exp(_UpperCamelCase  )

  # Add up the all the exponentials
  _snake_case		=							np.sum(_UpperCamelCase  )

  # Divide every exponent by the sum of all exponents
  _snake_case		=							exponent_vector / sum_of_exponents

  return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

