
"""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


__lowerCAmelCase							:  List[str] 				=		logging.get_logger(__name__)


__lowerCAmelCase							:  Union[str, Any] 				=		{
    '''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							:  int 				=		'''
    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 SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		List[str]         =		'bark_module'
	snake_case__							:		Optional[Any]         =		['past_key_values']

	snake_case__							:		int         =		{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	    'vocab_size': 'input_vocab_size',
	    'window_size': 'block_size',
	}


	def __init__(  self :Any       ,    __magic_name__ :Optional[Any]=1024       ,    __magic_name__ :Union[str, Any]=10048       ,    __magic_name__ :int=10048       ,    __magic_name__ :Union[str, Any]=12       ,    __magic_name__ :List[Any]=12       ,    __magic_name__ :Union[str, Any]=768       ,    __magic_name__ :Optional[Any]=0.0       ,    __magic_name__ :Union[str, Any]=True       ,    __magic_name__ :Tuple=0.02       ,    __magic_name__ :Any=True       ,    **__magic_name__ :str       ,    )			->   Tuple:

			'''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__(**__magic_name__			)






	@classmethod
	def    _UpperCamelCase							(  cls :Optional[int]       ,    __magic_name__ :Union[str, os.PathLike]       ,    __magic_name__ :Optional[Union[str, os.PathLike]] = None       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :Optional[Union[str, bool]] = None       ,    __magic_name__ :str = "main"       ,    **__magic_name__ :List[Any]       ,    )			->   "PretrainedConfig":

			'''simple docstring'''






			a__   		=     cache_dir
			a__   		=     force_download
			a__   		=     local_files_only
			a__   		=     revision

			cls._set_token_in_kwargs(__magic_name__       ,    __magic_name__			)

			a__		,	a__   		=     cls.get_config_dict(__magic_name__       ,    **__magic_name__			)

			# 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(__magic_name__       ,    **__magic_name__			)

@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 SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		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 SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		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 SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		List[str]         =		'fine_acoustics'


	def __init__(  self :str       ,    __magic_name__ :Optional[int]=True       ,    __magic_name__ :List[Any]=8       ,    __magic_name__ :int=1       ,    **__magic_name__ :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     n_codes_total
			a__   		=     n_codes_given

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

class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		Any         =		'bark'
	snake_case__							:		Union[str, Any]         =		True


	def __init__(  self :Union[str, Any]       ,    __magic_name__ :Dict = None       ,    __magic_name__ :Dict = None       ,    __magic_name__ :Dict = None       ,    __magic_name__ :Dict = None       ,    __magic_name__ :Union[str, Any]=0.02       ,    **__magic_name__ :Optional[int]       ,    )			->   Union[str, Any]:

			'''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(**__magic_name__			)
			a__   		=     BarkCoarseConfig(**__magic_name__			)
			a__   		=     BarkFineConfig(**__magic_name__			)
			a__   		=     AutoConfig.for_model(**__magic_name__			)

			a__   		=     initializer_range

			super().__init__(**__magic_name__			)


	@classmethod
	def    _UpperCamelCase							(  cls :List[str]       ,    __magic_name__ :BarkSemanticConfig       ,    __magic_name__ :BarkCoarseConfig       ,    __magic_name__ :BarkFineConfig       ,    __magic_name__ :AutoConfig       ,    **__magic_name__ :str       ,    )			->   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()       ,    **__magic_name__       ,    )






	def    _UpperCamelCase							(  self :Optional[Any]			)			->   Optional[int]:

			'''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"""




def __snake_case (      UpperCamelCase    )       ->    float:


		"""simple docstring"""





		return -num if num < 0 else num

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_min() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs_val(UpperCamelCase    ) < abs_val(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs(UpperCamelCase    ) > abs(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max_sort() arg is an empty sequence'''    )
		return sorted(UpperCamelCase						,					key=UpperCamelCase    )[-1]

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		assert abs_val(0    ) == 0
		assert abs_val(34    ) == 34
		assert abs_val(-100_000_000_000    ) == 100_000_000_000

		a__   		=     [-3, -1, 2, -11]
		assert abs_max(UpperCamelCase    ) == -11
		assert abs_max_sort(UpperCamelCase    ) == -11
		assert abs_min(UpperCamelCase    ) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34




