
"""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


__A					=			logging.get_logger(__name__)


__A					=			{
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

__A					=			'\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 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						'''bark_module'''
			lowerCamelCase       =						['''past_key_values''']

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



			def __init__(				self	,	__UpperCAmelCase=10_24	,	__UpperCAmelCase=1_00_48	,	__UpperCAmelCase=1_00_48	,	__UpperCAmelCase=12	,	__UpperCAmelCase=12	,	__UpperCAmelCase=7_68	,	__UpperCAmelCase=0.0	,	__UpperCAmelCase=True	,	__UpperCAmelCase=0.0_2	,	__UpperCAmelCase=True	,	**__UpperCAmelCase	,	)					->	int:
				_lowerCAmelCase								=block_size
				_lowerCAmelCase								=input_vocab_size
				_lowerCAmelCase								=output_vocab_size
				_lowerCAmelCase								=num_layers
				_lowerCAmelCase								=num_heads
				_lowerCAmelCase								=hidden_size
				_lowerCAmelCase								=dropout
				_lowerCAmelCase								=bias
				_lowerCAmelCase								=use_cache
				_lowerCAmelCase								=initializer_range

				super().__init__(**__UpperCAmelCase    )



			@classmethod
			def    _lowerCAmelCase						(				cls	,	__UpperCAmelCase	,	__UpperCAmelCase = None	,	__UpperCAmelCase = False	,	__UpperCAmelCase = False	,	__UpperCAmelCase = None	,	__UpperCAmelCase = "main"	,	**__UpperCAmelCase	,	)					->	"PretrainedConfig":
				_lowerCAmelCase								=cache_dir
				_lowerCAmelCase								=force_download
				_lowerCAmelCase								=local_files_only
				_lowerCAmelCase								=revision

				cls._set_token_in_kwargs(__UpperCAmelCase	,	__UpperCAmelCase    )

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

				# get the config dict if we are loading from Bark
				if config_dict.get("""model_type"""    ) == "bark":
					_lowerCAmelCase								=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 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						'''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 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						'''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 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						'''fine_acoustics'''



			def __init__(				self	,	__UpperCAmelCase=True	,	__UpperCAmelCase=8	,	__UpperCAmelCase=1	,	**__UpperCAmelCase    )					->	List[Any]:
				_lowerCAmelCase								=n_codes_total
				_lowerCAmelCase								=n_codes_given

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







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						'''bark'''
			lowerCamelCase       =						True



			def __init__(				self	,	__UpperCAmelCase = None	,	__UpperCAmelCase = None	,	__UpperCAmelCase = None	,	__UpperCAmelCase = None	,	__UpperCAmelCase=0.0_2	,	**__UpperCAmelCase	,	)					->	Union[str, Any]:
				if semantic_config is None:
					_lowerCAmelCase								={}
					logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

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

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

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

				_lowerCAmelCase								=BarkSemanticConfig(**__UpperCAmelCase    )
				_lowerCAmelCase								=BarkCoarseConfig(**__UpperCAmelCase    )
				_lowerCAmelCase								=BarkFineConfig(**__UpperCAmelCase    )
				_lowerCAmelCase								=AutoConfig.for_model(**__UpperCAmelCase    )

				_lowerCAmelCase								=initializer_range

				super().__init__(**__UpperCAmelCase    )



			@classmethod
			def    _lowerCAmelCase						(				cls	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	**__UpperCAmelCase	,	)					->	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    _lowerCAmelCase						(				self    )					->	Dict:
				_lowerCAmelCase								=copy.deepcopy(self.__dict__    )

				_lowerCAmelCase								=self.semantic_config.to_dict()
				_lowerCAmelCase								=self.coarse_acoustics_config.to_dict()
				_lowerCAmelCase								=self.fine_acoustics_config.to_dict()
				_lowerCAmelCase								=self.codec_config.to_dict()

				_lowerCAmelCase								=self.__class__.model_type
				return output





"""simple docstring"""


import warnings

from .generation import FlaxGenerationMixin







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			# warning at import time
			warnings.warn(
			    '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
			    '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''					,      __magic_name__					,      )



