




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						:							int			     =						logging.get_logger(__name__)


__lowerCAmelCase						:							Optional[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        snake_case__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Dict									=   """bark_module"""
				SCREAMING_SNAKE_CASE_			:				int									=   ["""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							:	int   , __lowerCamelCase							:	Any=10_24   , __lowerCamelCase							:	Optional[Any]=1_00_48   , __lowerCamelCase							:	Tuple=1_00_48   , __lowerCamelCase							:	Optional[int]=12   , __lowerCamelCase							:	Optional[Any]=12   , __lowerCamelCase							:	List[str]=7_68   , __lowerCamelCase							:	Union[str, Any]=0.0   , __lowerCamelCase							:	Tuple=True   , __lowerCamelCase							:	List[str]=0.02   , __lowerCamelCase							:	List[str]=True   , **__lowerCamelCase							:	Any   , )   ->	Optional[Any]:
							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__(**a_				)





				@classmethod
				def 				__UpperCAmelCase				( cls							:	List[Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Tuple = None   , __lowerCamelCase							:	str = False   , __lowerCamelCase							:	str = False   , __lowerCamelCase							:	int = None   , __lowerCamelCase							:	Optional[int] = "main"   , **__lowerCamelCase							:	int   , )   ->	Optional[int]:
							a       =     cache_dir
							a       =     force_download
							a       =     local_files_only
							a       =     revision

							cls._set_token_in_kwargs(a_   , a_				)

							a       =     cls.get_config_dict(a_   , **a_				)

							# 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(a_   , **a_				)




@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        snake_case__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   """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        snake_case__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   """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        snake_case__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				int									=   """fine_acoustics"""





				def __init__( self							:	List[Any]   , __lowerCamelCase							:	int=True   , __lowerCamelCase							:	List[Any]=8   , __lowerCamelCase							:	Tuple=1   , **__lowerCamelCase							:	Optional[Any]				)   ->	List[str]:
							a       =     n_codes_total
							a       =     n_codes_given

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




class        snake_case__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Tuple									=   """bark"""
				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   True





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

							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(**a_				)
							a       =     BarkCoarseConfig(**a_				)
							a       =     BarkFineConfig(**a_				)
							a       =     AutoConfig.for_model(**a_				)

							a       =     initializer_range

							super().__init__(**a_				)





				@classmethod
				def 				__UpperCAmelCase				( cls							:	int   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[int]   , **__lowerCamelCase							:	int   , )   ->	Union[str, 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()   , **a_   , )





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Optional[Any]:
							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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE					:       Tuple							   =							{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							["""RealmTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							[
			    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RealmEmbedder""",
			    """RealmForOpenQA""",
			    """RealmKnowledgeAugEncoder""",
			    """RealmPreTrainedModel""",
			    """RealmReader""",
			    """RealmScorer""",
			    """load_tf_weights_in_realm""",
			]
			SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							["""RealmRetriever"""]


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			SCREAMING_SNAKE_CASE					:       str							   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





