




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  :				Tuple						=				logging.get_logger(__name__)


_lowerCAmelCase  :				Tuple						=				{
    "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  :				List[str]						=				"\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      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""bark_module"""
      UpperCAmelCase_            =						["""past_key_values"""]

      UpperCAmelCase_            =						{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
          """vocab_size""": """input_vocab_size""",
          """window_size""": """block_size""",
      }
      def __init__(     self				:Optional[int]	, lowerCamelCase				:Union[str, Any]=1024	, lowerCamelCase				:Optional[Any]=1_0048	, lowerCamelCase				:Any=1_0048	, lowerCamelCase				:int=12	, lowerCamelCase				:Union[str, Any]=12	, lowerCamelCase				:str=768	, lowerCamelCase				:str=0.0	, lowerCamelCase				:Any=True	, lowerCamelCase				:Any=0.02	, lowerCamelCase				:Dict=True	, **lowerCamelCase				:Optional[Any]	, )       ->     Dict:
       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__(**lowerCamelCase )







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

       cls._set_token_in_kwargs(lowerCamelCase	, lowerCamelCase )

       UpperCAmelCase__	,	UpperCAmelCase__								=  cls.get_config_dict(lowerCamelCase	, **lowerCamelCase )

       # 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(lowerCamelCase	, **lowerCamelCase )



@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      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""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      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""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      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""fine_acoustics"""
      def __init__(     self				:Optional[int]	, lowerCamelCase				:Optional[int]=True	, lowerCamelCase				:Optional[Any]=8	, lowerCamelCase				:Tuple=1	, **lowerCamelCase				:List[str] )       ->     Tuple:
       UpperCAmelCase__								=  n_codes_total
       UpperCAmelCase__								=  n_codes_given

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



class      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""bark"""
      UpperCAmelCase_            =						True
      def __init__(     self				:Optional[int]	, lowerCamelCase				:Dict = None	, lowerCamelCase				:Dict = None	, lowerCamelCase				:Dict = None	, lowerCamelCase				:Dict = None	, lowerCamelCase				:int=0.02	, **lowerCamelCase				:Optional[int]	, )       ->     Optional[int]:
       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(**lowerCamelCase )
       UpperCAmelCase__								=  BarkCoarseConfig(**lowerCamelCase )
       UpperCAmelCase__								=  BarkFineConfig(**lowerCamelCase )
       UpperCAmelCase__								=  AutoConfig.for_model(**lowerCamelCase )

       UpperCAmelCase__								=  initializer_range

       super().__init__(**lowerCamelCase )
      @classmethod
      def 		UpperCAmelCase_  (     cls				:Dict	, lowerCamelCase				:BarkSemanticConfig	, lowerCamelCase				:BarkCoarseConfig	, lowerCamelCase				:BarkFineConfig	, lowerCamelCase				:AutoConfig	, **lowerCamelCase				:int	, )       ->     Optional[int]:
       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				:List[Any] )       ->     Dict:
       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











from __future__ import annotations



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 assert (
     c % greatest_common_divisor(_lowerCAmelCase  ,							_lowerCAmelCase		) == 0
 )  # greatest_common_divisor(a,b) function implemented below
 ((UpperCAmelCase__)	,	(UpperCAmelCase__)	,	(UpperCAmelCase__))								=  extended_gcd(_lowerCAmelCase  ,							_lowerCAmelCase		)  # extended_gcd(a,b) function implemented below
 UpperCAmelCase__								=  c / d
 return (r * x, r * y)



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int = 2		):




 """simple docstring"""







 ((UpperCAmelCase__)	,	(UpperCAmelCase__))								=  diophantine(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)  # Initial value
 UpperCAmelCase__								=  greatest_common_divisor(_lowerCAmelCase  ,							_lowerCAmelCase		)
 UpperCAmelCase__								=  a // d
 UpperCAmelCase__								=  b // d

 for i in range(_lowerCAmelCase		):
  UpperCAmelCase__								=  xa + i * q
  UpperCAmelCase__								=  ya - i * p
  print(_lowerCAmelCase  ,							_lowerCAmelCase		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 if a < b:
  UpperCAmelCase__	,	UpperCAmelCase__								=  b, a

 while a % b != 0:
  UpperCAmelCase__	,	UpperCAmelCase__								=  b, a % b

 return b



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 assert a >= 0 and b >= 0

 if b == 0:
  UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__								=  a, 1, 0
 else:
  ((UpperCAmelCase__)	,	(UpperCAmelCase__)	,	(UpperCAmelCase__))								=  extended_gcd(_lowerCAmelCase  ,							a % b		)
  UpperCAmelCase__								=  q
  UpperCAmelCase__								=  p - q * (a // b)

 assert a % d == 0 and b % d == 0
 assert d == a * x + b * y

 return (d, x, y)


if __name__ == "__main__":
  from doctest import testmod

  testmod(name="diophantine", verbose=True)
  testmod(name="diophantine_all_soln", verbose=True)
  testmod(name="extended_gcd", verbose=True)
  testmod(name="greatest_common_divisor", verbose=True)





