



'''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


_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 _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    int						      =			"bark_module"
		_SCREAMING_SNAKE_CASE					:    Optional[Any]						      =			["past_key_values"]

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





		def __init__(     self ,       __UpperCAmelCase=1_024 ,       __UpperCAmelCase=10_048 ,       __UpperCAmelCase=10_048 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=0.0 ,       __UpperCAmelCase=True ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=True ,       **__UpperCAmelCase ,       )       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     block_size
					__UpperCAmelCase   :      Tuple										=     input_vocab_size
					__UpperCAmelCase   :      List[str]										=     output_vocab_size
					__UpperCAmelCase   :      int										=     num_layers
					__UpperCAmelCase   :      Dict										=     num_heads
					__UpperCAmelCase   :      Union[str, Any]										=     hidden_size
					__UpperCAmelCase   :      Tuple										=     dropout
					__UpperCAmelCase   :      Optional[Any]										=     bias
					__UpperCAmelCase   :      int										=     use_cache
					__UpperCAmelCase   :      Any										=     initializer_range

					super().__init__(**__UpperCAmelCase				)





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase = None ,       __UpperCAmelCase = False ,       __UpperCAmelCase = False ,       __UpperCAmelCase = None ,       __UpperCAmelCase = "main" ,       **__UpperCAmelCase ,       )       ->     "PretrainedConfig":

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     cache_dir
					__UpperCAmelCase   :      Any										=     force_download
					__UpperCAmelCase   :      List[Any]										=     local_files_only
					__UpperCAmelCase   :      List[str]										=     revision

					cls._set_token_in_kwargs(__UpperCAmelCase ,       __UpperCAmelCase				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     cls.get_config_dict(__UpperCAmelCase ,       **__UpperCAmelCase				)

					# get the config dict if we are loading from Bark
					if config_dict.get("""model_type"""				) == "bark":
								__UpperCAmelCase   :      str										=     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"    )	,				"\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 _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    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 _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"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 _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    List[Any]						      =			"fine_acoustics"





		def __init__(     self ,       __UpperCAmelCase=True ,       __UpperCAmelCase=8 ,       __UpperCAmelCase=1 ,       **__UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     n_codes_total
					__UpperCAmelCase   :      Tuple										=     n_codes_given

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



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"bark"
		_SCREAMING_SNAKE_CASE					:    Union[str, Any]						      =			True





		def __init__(     self ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase=0.02 ,       **__UpperCAmelCase ,       )       ->     Dict:

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

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

					if fine_acoustics_config is None:
								__UpperCAmelCase   :      Optional[int]										=     {}
								logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""				)

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

					__UpperCAmelCase   :      int										=     BarkSemanticConfig(**__UpperCAmelCase				)
					__UpperCAmelCase   :      List[str]										=     BarkCoarseConfig(**__UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     BarkFineConfig(**__UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     AutoConfig.for_model(**__UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range

					super().__init__(**__UpperCAmelCase				)





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       **__UpperCAmelCase ,       )       ->     Any:

					'''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() ,       **__UpperCAmelCase ,       )





		def 							__A       (     self				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     copy.deepcopy(self.__dict__				)

					__UpperCAmelCase   :      Dict										=     self.semantic_config.to_dict()
					__UpperCAmelCase   :      Dict										=     self.coarse_acoustics_config.to_dict()
					__UpperCAmelCase   :      str										=     self.fine_acoustics_config.to_dict()
					__UpperCAmelCase   :      str										=     self.codec_config.to_dict()

					__UpperCAmelCase   :      Any										=     self.__class__.model_type
					return output









'''simple docstring'''
from math import pi


def   lowercase_	(   lowerCAmelCase__	:  float ):

			"""simple docstring"""





			return degree / (180 / pi)


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

					testmod()




