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													=		logging.get_logger(__name__)


UpperCamelCase													=		{
    '''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													=		'''
    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 						snake_case_ (	__A ):
   __A			:	Optional[int]        =       "bark_module"
   __A			:	Dict        =       ["past_key_values"]

   __A			:	Dict        =       {
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
       "vocab_size": "input_vocab_size",
       "window_size": "block_size",
   }






   def __init__(   self       :  Any			,		lowercase_       :  List[str]=10_24			,		lowercase_       :  Union[str, Any]=1_00_48			,		lowercase_       :  str=1_00_48			,		lowercase_       :  str=12			,		lowercase_       :  Union[str, Any]=12			,		lowercase_       :  Dict=7_68			,		lowercase_       :  Optional[int]=0.0			,		lowercase_       :  Tuple=True			,		lowercase_       :  List[str]=0.02			,		lowercase_       :  Tuple=True			,		**lowercase_       :  Optional[Any]			,		)      ->		List[str]:
       lowercase__				:     Any					      =       block_size
       lowercase__				:     Union[str, Any]					      =       input_vocab_size
       lowercase__				:     Tuple					      =       output_vocab_size
       lowercase__				:     List[Any]					      =       num_layers
       lowercase__				:     List[str]					      =       num_heads
       lowercase__				:     List[str]					      =       hidden_size
       lowercase__				:     Optional[Any]					      =       dropout
       lowercase__				:     int					      =       bias
       lowercase__				:     Union[str, Any]					      =       use_cache
       lowercase__				:     Tuple					      =       initializer_range

       super().__init__(**lowercase_						)







   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  Union[str, os.PathLike]			,		lowercase_       :  Optional[Union[str, os.PathLike]] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  Optional[Union[str, bool]] = None			,		lowercase_       :  str = "main"			,		**lowercase_       :  Union[str, Any]			,		)      ->		"PretrainedConfig":
       lowercase__				:     Union[str, Any]					      =       cache_dir
       lowercase__				:     Union[str, Any]					      =       force_download
       lowercase__				:     List[str]					      =       local_files_only
       lowercase__				:     Any					      =       revision

       cls._set_token_in_kwargs(lowercase_			,		lowercase_						)

       lowercase__      ,     lowercase__				:     Dict					      =       cls.get_config_dict(lowercase_			,		**lowercase_						)

       # get the config dict if we are loading from Bark
       if config_dict.get("model_type"						) == "bark":
           lowercase__				:     List[Any]					      =       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(lowercase_			,		**lowercase_						)



@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 						snake_case_ (	__A ):
   __A			:	Tuple        =       "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 						snake_case_ (	__A ):
   __A			:	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 						snake_case_ (	__A ):
   __A			:	str        =       "fine_acoustics"






   def __init__(   self       :  Any			,		lowercase_       :  List[Any]=True			,		lowercase_       :  Tuple=8			,		lowercase_       :  str=1			,		**lowercase_       :  Optional[int]						)      ->		Any:
       lowercase__				:     Any					      =       n_codes_total
       lowercase__				:     List[str]					      =       n_codes_given

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



class 						snake_case_ (	__A ):
   __A			:	Union[str, Any]        =       "bark"
   __A			:	str        =       True






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

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

       if fine_acoustics_config is None:
           lowercase__				:     List[str]					      =       {}
           logger.info("fine_acoustics_config is None. initializing the fine model with default values."						)

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

       lowercase__				:     Optional[Any]					      =       BarkSemanticConfig(**lowercase_						)
       lowercase__				:     int					      =       BarkCoarseConfig(**lowercase_						)
       lowercase__				:     Optional[int]					      =       BarkFineConfig(**lowercase_						)
       lowercase__				:     Tuple					      =       AutoConfig.for_model(**lowercase_						)

       lowercase__				:     List[Any]					      =       initializer_range

       super().__init__(**lowercase_						)






   @classmethod
   def    __UpperCamelCase  (   cls       :  List[Any]			,		lowercase_       :  BarkSemanticConfig			,		lowercase_       :  BarkCoarseConfig			,		lowercase_       :  BarkFineConfig			,		lowercase_       :  AutoConfig			,		**lowercase_       :  Tuple			,		)      ->		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()			,		**lowercase_			,		)







   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Dict:
       lowercase__				:     Optional[Any]					      =       copy.deepcopy(self.__dict__						)

       lowercase__				:     str					      =       self.semantic_config.to_dict()
       lowercase__				:     Union[str, Any]					      =       self.coarse_acoustics_config.to_dict()
       lowercase__				:     Dict					      =       self.fine_acoustics_config.to_dict()
       lowercase__				:     Tuple					      =       self.codec_config.to_dict()

       lowercase__				:     Tuple					      =       self.__class__.model_type
       return output




from typing import Callable

def     lowercase_       (    _lowerCamelCase							: Callable):

    def wrapper(_lowerCamelCase							: str):
        lowercase__				:     Tuple					      =       function_with_docstring.__doc__
        return target_function

    return wrapper



