




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


__lowerCAmelCase						:							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',
}

__lowerCAmelCase						:							Dict			     =						'\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__				(_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				int									=   """bark_module"""
				SCREAMING_SNAKE_CASE_			:				str									=   ["""past_key_values"""]

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





				def __init__( self							:	Optional[Any]   , __lowerCamelCase							:	Dict=10_24   , __lowerCamelCase							:	Dict=1_00_48   , __lowerCamelCase							:	Union[str, Any]=1_00_48   , __lowerCamelCase							:	int=12   , __lowerCamelCase							:	Optional[Any]=12   , __lowerCamelCase							:	Optional[int]=7_68   , __lowerCamelCase							:	List[Any]=0.0   , __lowerCamelCase							:	List[Any]=True   , __lowerCamelCase							:	str=0.02   , __lowerCamelCase							:	Any=True   , **__lowerCamelCase							:	Union[str, Any]   , )   ->	int:
							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__(**__UpperCAmelCase				)





				@classmethod
				def 				__UpperCAmelCase				( cls							:	List[Any]   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	Tuple = None   , __lowerCamelCase							:	Any = False   , __lowerCamelCase							:	List[str] = False   , __lowerCamelCase							:	Any = None   , __lowerCamelCase							:	Union[str, Any] = "main"   , **__lowerCamelCase							:	Any   , )   ->	"PretrainedConfig":
							a       =     cache_dir
							a       =     force_download
							a       =     local_files_only
							a       =     revision

							cls._set_token_in_kwargs(__UpperCAmelCase   , __UpperCAmelCase				)

							a       ,						a       =     cls.get_config_dict(__UpperCAmelCase   , **__UpperCAmelCase				)

							# 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(__UpperCAmelCase   , **__UpperCAmelCase				)




@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        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Dict									=   """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        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   """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        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				List[str]									=   """fine_acoustics"""





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

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




class        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   """bark"""
				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   True





				def __init__( self							:	Any   , __lowerCamelCase							:	Any = None   , __lowerCamelCase							:	str = None   , __lowerCamelCase							:	Any = None   , __lowerCamelCase							:	List[Any] = None   , __lowerCamelCase							:	List[str]=0.02   , **__lowerCamelCase							:	Optional[int]   , )   ->	Union[str, Any]:
							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(**__UpperCAmelCase				)
							a       =     BarkCoarseConfig(**__UpperCAmelCase				)
							a       =     BarkFineConfig(**__UpperCAmelCase				)
							a       =     AutoConfig.for_model(**__UpperCAmelCase				)

							a       =     initializer_range

							super().__init__(**__UpperCAmelCase				)





				@classmethod
				def 				__UpperCAmelCase				( cls							:	int   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Optional[int]   , **__lowerCamelCase							:	List[str]   , )   ->	Any:
							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()   , **__UpperCAmelCase   , )





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Dict:
							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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					=			logging.get_logger(__name__)

__A					=			{
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						'''nystromformer'''



			def __init__(				self	,	__UpperCAmelCase=3_00_00	,	__UpperCAmelCase=7_68	,	__UpperCAmelCase=12	,	__UpperCAmelCase=12	,	__UpperCAmelCase=30_72	,	__UpperCAmelCase="gelu_new"	,	__UpperCAmelCase=0.1	,	__UpperCAmelCase=0.1	,	__UpperCAmelCase=5_10	,	__UpperCAmelCase=2	,	__UpperCAmelCase=64	,	__UpperCAmelCase=64	,	__UpperCAmelCase=65	,	__UpperCAmelCase=False	,	__UpperCAmelCase=0.0_2	,	__UpperCAmelCase=1e-5	,	__UpperCAmelCase=1	,	__UpperCAmelCase=0	,	__UpperCAmelCase=2	,	**__UpperCAmelCase	,	)					->	Union[str, Any]:
				_lowerCAmelCase								=vocab_size
				_lowerCAmelCase								=max_position_embeddings
				_lowerCAmelCase								=hidden_size
				_lowerCAmelCase								=num_hidden_layers
				_lowerCAmelCase								=num_attention_heads
				_lowerCAmelCase								=intermediate_size
				_lowerCAmelCase								=hidden_act
				_lowerCAmelCase								=hidden_dropout_prob
				_lowerCAmelCase								=attention_probs_dropout_prob
				_lowerCAmelCase								=initializer_range
				_lowerCAmelCase								=type_vocab_size
				_lowerCAmelCase								=segment_means_seq_len
				_lowerCAmelCase								=num_landmarks
				_lowerCAmelCase								=conv_kernel_size
				_lowerCAmelCase								=inv_coeff_init_option
				_lowerCAmelCase								=layer_norm_eps
				super().__init__(pad_token_id=__UpperCAmelCase	,	bos_token_id=__UpperCAmelCase	,	eos_token_id=__UpperCAmelCase	,	**__UpperCAmelCase    )



