

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


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)


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

_SCREAMING_SNAKE_CASE							 =			"""
    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      lowerCamelCase_ (			__lowerCAmelCase):
 _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=1_024  ,  _UpperCAmelCase=10_048  ,  _UpperCAmelCase=10_048  ,  _UpperCAmelCase=12  ,  _UpperCAmelCase=12  ,  _UpperCAmelCase=768  ,  _UpperCAmelCase=0.0  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=0.0_2  ,  _UpperCAmelCase=True  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  Any		=     block_size
   __UpperCAmelCase	:  Any		=     input_vocab_size
   __UpperCAmelCase	:  int		=     output_vocab_size
   __UpperCAmelCase	:  Union[str, Any]		=     num_layers
   __UpperCAmelCase	:  Any		=     num_heads
   __UpperCAmelCase	:  Optional[int]		=     hidden_size
   __UpperCAmelCase	:  Dict		=     dropout
   __UpperCAmelCase	:  Any		=     bias
   __UpperCAmelCase	:  Optional[int]		=     use_cache
   __UpperCAmelCase	:  List[str]		=     initializer_range

   super().__init__(**__SCREAMING_SNAKE_CASE							)




 @classmethod
 def     a       (			cls  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = "main"  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  Any		=     cache_dir
   __UpperCAmelCase	:  Optional[Any]		=     force_download
   __UpperCAmelCase	:  Tuple		=     local_files_only
   __UpperCAmelCase	:  Dict		=     revision

   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE							)

   __UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE							)

   # get the config dict if we are loading from Bark
   if config_dict.get('model_type'							) == "bark":
     __UpperCAmelCase	:  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(__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE							)



@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_ (			__lowerCAmelCase):
 _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_ (			__lowerCAmelCase):
 _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_ (			__lowerCAmelCase):
 _lowerCAmelCase									=	"""fine_acoustics"""




 def __init__(			self  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=8  ,  _UpperCAmelCase=1  ,  **_UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     n_codes_total
   __UpperCAmelCase	:  Optional[int]		=     n_codes_given

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



class      lowerCamelCase_ (			__lowerCAmelCase):
 _lowerCAmelCase									=	"""bark"""
 _lowerCAmelCase									=	True




 def __init__(			self  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase=0.0_2  ,  **_UpperCAmelCase  ,  ):

   if semantic_config is None:
     __UpperCAmelCase	:  int		=     {}
     logger.info('semantic_config is None. initializing the semantic model with default values.'							)

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

   if fine_acoustics_config is None:
     __UpperCAmelCase	:  Optional[int]		=     {}
     logger.info('fine_acoustics_config is None. initializing the fine model with default values.'							)

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

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

   __UpperCAmelCase	:  Dict		=     initializer_range

   super().__init__(**__SCREAMING_SNAKE_CASE							)




 @classmethod
 def     a       (			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()  ,  **__SCREAMING_SNAKE_CASE  ,  )




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     copy.deepcopy(self.__dict__							)

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

   __UpperCAmelCase	:  str		=     self.__class__.model_type
   return output












"""simple docstring"""


from typing import TYPE_CHECKING

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


a__       :  List[Any]       		=					{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__       :  Union[str, Any]       		=					["""RealmTokenizerFast"""]

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

   a__       :  Any       		=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

