




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_    ='\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 				__UpperCamelCase      (			_lowerCamelCase	):





				'''simple docstring'''



				__a				:			int					    ="""bark_module"""
				__a				:			Tuple					    =["""past_key_values"""]

				__a				:			List[str]					    ={
				    """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_=0.02							,			UpperCAmelCase_=True							,			**UpperCAmelCase_							,			):
											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    __snake_case		(			cls							,			UpperCAmelCase_							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = False							,			UpperCAmelCase_ = False							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = "main"							,			**UpperCAmelCase_							,			):
											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 				__UpperCamelCase      (			_lowerCamelCase	):





				'''simple docstring'''



				__a				:			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 				__UpperCamelCase      (			_lowerCamelCase	):





				'''simple docstring'''



				__a				:			Any					    ="""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 				__UpperCamelCase      (			_lowerCamelCase	):





				'''simple docstring'''



				__a				:			str					    ="""fine_acoustics"""



				def __init__(			self							,			UpperCAmelCase_=True							,			UpperCAmelCase_=8							,			UpperCAmelCase_=1							,			**UpperCAmelCase_    ):
											lowerCAmelCase =		n_codes_total
											lowerCAmelCase =		n_codes_given

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




class 				__UpperCamelCase      (			_lowerCamelCase	):





				'''simple docstring'''



				__a				:			Tuple					    ="""bark"""
				__a				:			int					    =True



				def __init__(			self							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = None							,			UpperCAmelCase_=0.02							,			**UpperCAmelCase_							,			):
											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    __snake_case		(			cls							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			**UpperCAmelCase_							,			):
											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    __snake_case		(			self    ):
											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










from typing import TYPE_CHECKING

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


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:
       snake_case__   :				Tuple						=	['RealmTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				str						=	[
           'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RealmEmbedder',
           'RealmForOpenQA',
           'RealmKnowledgeAugEncoder',
           'RealmPreTrainedModel',
           'RealmReader',
           'RealmScorer',
           'load_tf_weights_in_realm',
       ]
       snake_case__   :				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

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





