






"""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


lowercase_								=	logging.get_logger(__name__)


lowercase_								=	{
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

lowercase_								=	'\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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Tuple							=					"bark_module"
   A_		:      int							=					["past_key_values"]

   A_		:      List[str]							=					{
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
       "vocab_size": "input_vocab_size",
       "window_size": "block_size",
   }


   def __init__(      self					:	str,			_lowerCamelCase					:	Union[str, Any]=10_24,			_lowerCamelCase					:	Optional[Any]=1_00_48,			_lowerCamelCase					:	Dict=1_00_48,			_lowerCamelCase					:	str=12,			_lowerCamelCase					:	Union[str, Any]=12,			_lowerCamelCase					:	Dict=7_68,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Union[str, Any]=True,			_lowerCamelCase					:	str=0.02,			_lowerCamelCase					:	Tuple=True,			**_lowerCamelCase					:	Optional[Any],			):

     '''simple docstring'''
     __A		     =					block_size
     __A		     =					input_vocab_size
     __A		     =					output_vocab_size
     __A		     =					num_layers
     __A		     =					num_heads
     __A		     =					hidden_size
     __A		     =					dropout
     __A		     =					bias
     __A		     =					use_cache
     __A		     =					initializer_range

     super().__init__(**_lowerCamelCase    )






   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	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					:	int,			):

     '''simple docstring'''
     __A		     =					cache_dir
     __A		     =					force_download
     __A		     =					local_files_only
     __A		     =					revision

     cls._set_token_in_kwargs(_lowerCamelCase,			_lowerCamelCase    )

     __A     ,       __A		     =					cls.get_config_dict(_lowerCamelCase,			**_lowerCamelCase    )

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


   '''simple docstring'''

   A_		:      Union[str, Any]							=					"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						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Tuple							=					"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						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      str							=					"fine_acoustics"


   def __init__(      self					:	Dict,			_lowerCamelCase					:	Union[str, Any]=True,			_lowerCamelCase					:	List[str]=8,			_lowerCamelCase					:	Optional[int]=1,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     __A		     =					n_codes_total
     __A		     =					n_codes_given

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





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"bark"
   A_		:      Tuple							=					True


   def __init__(      self					:	Union[str, Any],			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Tuple=0.02,			**_lowerCamelCase					:	List[str],			):

     '''simple docstring'''
     if semantic_config is None:
       __A		     =					{}
       logger.info('''semantic_config is None. initializing the semantic model with default values.'''    )

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

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

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

     __A		     =					BarkSemanticConfig(**_lowerCamelCase    )
     __A		     =					BarkCoarseConfig(**_lowerCamelCase    )
     __A		     =					BarkFineConfig(**_lowerCamelCase    )
     __A		     =					AutoConfig.for_model(**_lowerCamelCase    )

     __A		     =					initializer_range

     super().__init__(**_lowerCamelCase    )


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	List[Any],			_lowerCamelCase					:	BarkSemanticConfig,			_lowerCamelCase					:	BarkCoarseConfig,			_lowerCamelCase					:	BarkFineConfig,			_lowerCamelCase					:	AutoConfig,			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					copy.deepcopy(self.__dict__    )

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

     __A		     =					self.__class__.model_type
     return output












"""simple docstring"""


from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"led"
   A_		:      Any							=					{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }


   def __init__(      self					:	Dict,			_lowerCamelCase					:	str=5_02_65,			_lowerCamelCase					:	List[Any]=1_63_84,			_lowerCamelCase					:	int=10_24,			_lowerCamelCase					:	List[Any]=12,			_lowerCamelCase					:	int=40_96,			_lowerCamelCase					:	str=16,			_lowerCamelCase					:	Dict=12,			_lowerCamelCase					:	Optional[int]=40_96,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	int=0.0,			_lowerCamelCase					:	str=0.0,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Tuple="gelu",			_lowerCamelCase					:	Tuple=10_24,			_lowerCamelCase					:	Dict=0.1,			_lowerCamelCase					:	Tuple=0.0,			_lowerCamelCase					:	Union[str, Any]=0.0,			_lowerCamelCase					:	Optional[int]=0.02,			_lowerCamelCase					:	List[str]=2,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Dict=1,			_lowerCamelCase					:	str=0,			_lowerCamelCase					:	Union[str, Any]=2,			_lowerCamelCase					:	Union[List[int], int] = 5_12,			**_lowerCamelCase					:	Any,			):

     '''simple docstring'''
     __A		     =					vocab_size
     __A		     =					max_encoder_position_embeddings
     __A		     =					max_decoder_position_embeddings
     __A		     =					d_model
     __A		     =					encoder_ffn_dim
     __A		     =					encoder_layers
     __A		     =					encoder_attention_heads
     __A		     =					decoder_ffn_dim
     __A		     =					decoder_layers
     __A		     =					decoder_attention_heads
     __A		     =					dropout
     __A		     =					attention_dropout
     __A		     =					activation_dropout
     __A		     =					activation_function
     __A		     =					init_std
     __A		     =					encoder_layerdrop
     __A		     =					decoder_layerdrop
     __A		     =					classifier_dropout
     __A		     =					use_cache
     __A		     =					encoder_layers
     __A		     =					attention_window

     super().__init__(
         pad_token_id=_lowerCamelCase,			bos_token_id=_lowerCamelCase,			eos_token_id=_lowerCamelCase,			is_encoder_decoder=_lowerCamelCase,			decoder_start_token_id=_lowerCamelCase,			**_lowerCamelCase,			)




