



'''simple docstring'''



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__		: Optional[int]												=  logging.get_logger(__name__)


A__		: List[str]												=  {
    """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__		: 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  ):





    """simple docstring"""






    lowerCamelCase  : Dict												=      "bark_module"
    lowerCamelCase  : Optional[int]												=      ["past_key_values"]

    lowerCamelCase  : str												=      {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
        "vocab_size": "input_vocab_size",
        "window_size": "block_size",
    }







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=10_24      ,					SCREAMING_SNAKE_CASE_=1_00_48      ,					SCREAMING_SNAKE_CASE_=1_00_48      ,					SCREAMING_SNAKE_CASE_=12      ,					SCREAMING_SNAKE_CASE_=12      ,					SCREAMING_SNAKE_CASE_=7_68      ,					SCREAMING_SNAKE_CASE_=0.0      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=0.0_2      ,					SCREAMING_SNAKE_CASE_=True      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Optional[Any]:
         __lowerCamelCase       :     Tuple        =		block_size
         __lowerCamelCase       :     Optional[Any]        =		input_vocab_size
         __lowerCamelCase       :     str        =		output_vocab_size
         __lowerCamelCase       :     str        =		num_layers
         __lowerCamelCase       :     Optional[Any]        =		num_heads
         __lowerCamelCase       :     str        =		hidden_size
         __lowerCamelCase       :     Optional[int]        =		dropout
         __lowerCamelCase       :     Optional[int]        =		bias
         __lowerCamelCase       :     Any        =		use_cache
         __lowerCamelCase       :     Optional[int]        =		initializer_range

         super().__init__(**_lowerCamelCase     )







    @classmethod
    def 				lowercase_    (							cls      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = False      ,					SCREAMING_SNAKE_CASE_ = False      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = "main"      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					int:
         __lowerCamelCase       :     Optional[Any]        =		cache_dir
         __lowerCamelCase       :     Tuple        =		force_download
         __lowerCamelCase       :     List[Any]        =		local_files_only
         __lowerCamelCase       :     Tuple        =		revision

         cls._set_token_in_kwargs(_lowerCamelCase      ,					_lowerCamelCase     )

         __lowerCamelCase    ,	__lowerCamelCase       :     List[str]        =		cls.get_config_dict(_lowerCamelCase      ,					**_lowerCamelCase     )

         # get the config dict if we are loading from Bark
         if config_dict.get('model_type'     ) == "bark":
              __lowerCamelCase       :     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_  (_lowerCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : List[str]												=      "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_  (_lowerCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Optional[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        UpperCAmelCase_  (_lowerCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Any												=      "fine_acoustics"







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=8      ,					SCREAMING_SNAKE_CASE_=1      ,					**SCREAMING_SNAKE_CASE_     )    ->					str:
         __lowerCamelCase       :     int        =		n_codes_total
         __lowerCamelCase       :     List[str]        =		n_codes_given

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







class        UpperCAmelCase_  (_lowerCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Dict												=      "bark"
    lowerCamelCase  : int												=      True







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_=0.0_2      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Dict:
         if semantic_config is None:
              __lowerCamelCase       :     List[str]        =		{}
              logger.info('semantic_config is None. initializing the semantic model with default values.'     )

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

         if fine_acoustics_config is None:
              __lowerCamelCase       :     Optional[Any]        =		{}
              logger.info('fine_acoustics_config is None. initializing the fine model with default values.'     )

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

         __lowerCamelCase       :     Optional[int]        =		BarkSemanticConfig(**_lowerCamelCase     )
         __lowerCamelCase       :     Optional[Any]        =		BarkCoarseConfig(**_lowerCamelCase     )
         __lowerCamelCase       :     Union[str, Any]        =		BarkFineConfig(**_lowerCamelCase     )
         __lowerCamelCase       :     Dict        =		AutoConfig.for_model(**_lowerCamelCase     )

         __lowerCamelCase       :     Any        =		initializer_range

         super().__init__(**_lowerCamelCase     )







    @classmethod
    def 				lowercase_    (							cls      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					str:
         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 				lowercase_    (							self     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Any        =		copy.deepcopy(self.__dict__     )

         __lowerCamelCase       :     str        =		self.semantic_config.to_dict()
         __lowerCamelCase       :     Any        =		self.coarse_acoustics_config.to_dict()
         __lowerCamelCase       :     Optional[int]        =		self.fine_acoustics_config.to_dict()
         __lowerCamelCase       :     Tuple        =		self.codec_config.to_dict()

         __lowerCamelCase       :     Tuple        =		self.__class__.model_type
         return output










'''simple docstring'''



def       UpperCAmelCase__ (     UpperCAmelCase_   :    int = 4_00_00_00							)					->		int:
     __lowerCamelCase       :     int        =		1
     __lowerCamelCase       :     Union[str, Any]        =		2
     __lowerCamelCase       :     Optional[int]        =		0
     while j <= n:
          if j % 2 == 0:
               total += j
          __lowerCamelCase    ,	__lowerCamelCase       :     List[Any]        =		j, i + j

     return total


if __name__ == "__main__":
      print(f'''{solution() = }''')






