
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     :					Dict       =logging.get_logger(__name__)


_UpperCAmelCase     :					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""",
}

_UpperCAmelCase     :					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 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[str]   =							"""bark_module"""
						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							["""past_key_values"""]

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




						def __init__(			self				,  __lowercase=1_0_2_4				,  __lowercase=1_0_0_4_8				,  __lowercase=1_0_0_4_8				,  __lowercase=1_2				,  __lowercase=1_2				,  __lowercase=7_6_8				,  __lowercase=0.0				,  __lowercase=True				,  __lowercase=0.02				,  __lowercase=True				,  **__lowercase				,  )   ->      List[Any]:
										lowerCAmelCase_	: Optional[Any]      			=			block_size
										lowerCAmelCase_	: Tuple      			=			input_vocab_size
										lowerCAmelCase_	: str      			=			output_vocab_size
										lowerCAmelCase_	: Dict      			=			num_layers
										lowerCAmelCase_	: Optional[Any]      			=			num_heads
										lowerCAmelCase_	: List[str]      			=			hidden_size
										lowerCAmelCase_	: Optional[Any]      			=			dropout
										lowerCAmelCase_	: Dict      			=			bias
										lowerCAmelCase_	: Any      			=			use_cache
										lowerCAmelCase_	: List[str]      			=			initializer_range

										super().__init__(**__lowercase							)




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

										cls._set_token_in_kwargs(__lowercase				,  __lowercase							)

										lowerCAmelCase_	: Dict      			=			cls.get_config_dict(__lowercase				,  **__lowercase							)

										# 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(__lowercase				,  **__lowercase							)







@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__ :					int   =							"""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__ :					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__ :					Dict   =							"""fine_acoustics"""




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

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







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[str]   =							"""bark"""
						SCREAMING_SNAKE_CASE__ :					Dict   =							True




						def __init__(			self				,  __lowercase = None				,  __lowercase = None				,  __lowercase = None				,  __lowercase = None				,  __lowercase=0.02				,  **__lowercase				,  )   ->      Optional[Any]:
										if semantic_config is None:
														lowerCAmelCase_	: int      			=			{}
														logger.info('''semantic_config is None. initializing the semantic model with default values.'''							)

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

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

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

										lowerCAmelCase_	: List[str]      			=			BarkSemanticConfig(**__lowercase							)
										lowerCAmelCase_	: Any      			=			BarkCoarseConfig(**__lowercase							)
										lowerCAmelCase_	: Union[str, Any]      			=			BarkFineConfig(**__lowercase							)
										lowerCAmelCase_	: Optional[Any]      			=			AutoConfig.for_model(**__lowercase							)

										lowerCAmelCase_	: Tuple      			=			initializer_range

										super().__init__(**__lowercase							)




						@classmethod
						def   lowercase_	(			cls				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  **__lowercase				,  )   ->      Dict:
										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()				,  **__lowercase				,  )




						def   lowercase_	(			self							)   ->      Tuple:
										lowerCAmelCase_	: Dict      			=			copy.deepcopy(self.__dict__							)

										lowerCAmelCase_	: int      			=			self.semantic_config.to_dict()
										lowerCAmelCase_	: int      			=			self.coarse_acoustics_config.to_dict()
										lowerCAmelCase_	: List[Any]      			=			self.fine_acoustics_config.to_dict()
										lowerCAmelCase_	: Optional[Any]      			=			self.codec_config.to_dict()

										lowerCAmelCase_	: Any      			=			self.__class__.model_type
										return output



import math







class 			snake_case__:







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Tuple:
										lowerCAmelCase_	: List[str]      			=			x
										lowerCAmelCase_	: Dict      			=			y
										lowerCAmelCase_	: List[Any]      			=			z




						def __repr__(			self							)   ->      str:
										return f"""Point({self.x}, {self.y}, {self.z})"""






def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	float:
				return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2	)	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()