






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        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""bark_module"""
						snake_case__   					=			["""past_key_values"""]

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



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




											'''simple docstring'''



											__a				:	Tuple			=				block_size
											__a				:	Union[str, Any]			=				input_vocab_size
											__a				:	List[str]			=				output_vocab_size
											__a				:	Any			=				num_layers
											__a				:	Union[str, Any]			=				num_heads
											__a				:	int			=				hidden_size
											__a				:	Any			=				dropout
											__a				:	Dict			=				bias
											__a				:	List[str]			=				use_cache
											__a				:	List[Any]			=				initializer_range

											super().__init__(**__UpperCAmelCase      )



						@classmethod
						def 					UpperCAmelCase__				(cls:				Dict   ,				__UpperCAmelCase:				Union[str, os.PathLike]   ,				__UpperCAmelCase:				Optional[Union[str, os.PathLike]] = None   ,				__UpperCAmelCase:				bool = False   ,				__UpperCAmelCase:				bool = False   ,				__UpperCAmelCase:				Optional[Union[str, bool]] = None   ,				__UpperCAmelCase:				str = "main"   ,				**__UpperCAmelCase:				int   ,				)						->    "PretrainedConfig":




											'''simple docstring'''



											__a				:	List[str]			=				cache_dir
											__a				:	Tuple			=				force_download
											__a				:	str			=				local_files_only
											__a				:	Optional[Any]			=				revision

											cls._set_token_in_kwargs(__UpperCAmelCase   ,				__UpperCAmelCase      )

											__a				:	str			=				cls.get_config_dict(__UpperCAmelCase   ,				**__UpperCAmelCase      )

											# get the config dict if we are loading from Bark
											if config_dict.get("model_type"      ) == "bark":
																__a				:	Union[str, Any]			=				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"""



						snake_case__   					=			"""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"""



						snake_case__   					=			"""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"""



						snake_case__   					=			"""fine_acoustics"""



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




											'''simple docstring'''



											__a				:	List[Any]			=				n_codes_total
											__a				:	Tuple			=				n_codes_given

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




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""bark"""
						snake_case__   					=			True



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




											'''simple docstring'''



											if semantic_config is None:
																__a				:	Optional[Any]			=				{}
																logger.info("semantic_config is None. initializing the semantic model with default values."      )

											if coarse_acoustics_config is None:
																__a				:	Union[str, Any]			=				{}
																logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."      )

											if fine_acoustics_config is None:
																__a				:	Union[str, Any]			=				{}
																logger.info("fine_acoustics_config is None. initializing the fine model with default values."      )

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

											__a				:	Any			=				BarkSemanticConfig(**__UpperCAmelCase      )
											__a				:	Union[str, Any]			=				BarkCoarseConfig(**__UpperCAmelCase      )
											__a				:	Optional[int]			=				BarkFineConfig(**__UpperCAmelCase      )
											__a				:	Dict			=				AutoConfig.for_model(**__UpperCAmelCase      )

											__a				:	Dict			=				initializer_range

											super().__init__(**__UpperCAmelCase      )



						@classmethod
						def 					UpperCAmelCase__				(cls:				Dict   ,				__UpperCAmelCase:				BarkSemanticConfig   ,				__UpperCAmelCase:				BarkCoarseConfig   ,				__UpperCAmelCase:				BarkFineConfig   ,				__UpperCAmelCase:				AutoConfig   ,				**__UpperCAmelCase:				Union[str, Any]   ,				)						->    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 					UpperCAmelCase__				(self:				int      )						->    str:




											'''simple docstring'''



											__a				:	str			=				copy.deepcopy(self.__dict__      )

											__a				:	Optional[int]			=				self.semantic_config.to_dict()
											__a				:	int			=				self.coarse_acoustics_config.to_dict()
											__a				:	Dict			=				self.fine_acoustics_config.to_dict()
											__a				:	Dict			=				self.codec_config.to_dict()

											__a				:	Tuple			=				self.__class__.model_type
											return output













import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput




@dataclass
class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			42
						snake_case__   					=			None
						snake_case__   					=			None




class        snake_case_					(						unittest.TestCase			):

						"""simple docstring"""



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Tuple:




											'''simple docstring'''



											__a				:	List[str]			=				ModelOutputTest(a=30      )
											self.assertEqual(x.a   ,				30      )
											self.assertIsNone(x.b      )
											self.assertIsNone(x.c      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Tuple			=				x.d



						def 					UpperCAmelCase__				(self:				Dict      )						->    Union[str, Any]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )

											__a				:	int			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["b"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[str]			=				x["c"]

											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["c"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	int			=				x["b"]



						def 					UpperCAmelCase__				(self:				Union[str, Any]      )						->    Optional[int]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(list(x.values()      )   ,				[30]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a"]      )

											__a				:	List[str]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("b", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "b"]      )

											__a				:	str			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "c"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("c", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "c"]      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Dict			=				x.update({"d": 20}      )
											with self.assertRaises(__UpperCAmelCase      ):
																del x["a"]
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.pop("a"      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.setdefault("d"   ,				32      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Any:




											'''simple docstring'''



											__a				:	Optional[int]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				List[str]      )						->    Any:




											'''simple docstring'''



											__a				:	Dict			=				ModelOutputTest({"a": 30, "b": 10}      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[str]:




											'''simple docstring'''



											__a				:	List[Any]			=				ModelOutputTest([("a", 30), ("b", 10)]      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[Any]			=				ModelOutputTest([("a", 30), (10, 10)]      )

											__a				:	Optional[Any]			=				ModelOutputTest(a=(30, 30)      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(x.a   ,				(30, 30)      )





