




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a		:	Any       =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	Union[str, Any]       =      ['''DebertaV2TokenizerFast''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	List[str]       =      [
      '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFDebertaV2ForMaskedLM''',
      '''TFDebertaV2ForQuestionAnswering''',
      '''TFDebertaV2ForSequenceClassification''',
      '''TFDebertaV2ForTokenClassification''',
      '''TFDebertaV2Model''',
      '''TFDebertaV2PreTrainedModel''',
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	int       =      [
      '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DebertaV2ForMaskedLM''',
      '''DebertaV2ForMultipleChoice''',
      '''DebertaV2ForQuestionAnswering''',
      '''DebertaV2ForSequenceClassification''',
      '''DebertaV2ForTokenClassification''',
      '''DebertaV2Model''',
      '''DebertaV2PreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_deberta_va import (
      DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DebertaVaConfig,
      DebertaVaOnnxConfig,
  )
  from .tokenization_deberta_va import DebertaVaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta_va import (
        TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaVaForMaskedLM,
        TFDebertaVaForQuestionAnswering,
        TFDebertaVaForSequenceClassification,
        TFDebertaVaForTokenClassification,
        TFDebertaVaModel,
        TFDebertaVaPreTrainedModel,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta_va import (
        DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaVaForMaskedLM,
        DebertaVaForMultipleChoice,
        DebertaVaForQuestionAnswering,
        DebertaVaForSequenceClassification,
        DebertaVaForTokenClassification,
        DebertaVaModel,
        DebertaVaPreTrainedModel,
    )

else:
  import sys

  a		:	Tuple       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



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   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  List[str]		="""bark_module"""
     _SCREAMING_SNAKE_CASE  :  str		=["""past_key_values"""]

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



     def __init__(       self					,   lowerCAmelCase__=1024					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=12					,   lowerCAmelCase__=12					,   lowerCAmelCase__=768					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=True					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=True					,   **lowerCAmelCase__					,   ):
            _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__(**lowerCAmelCase__							)





     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = "main"					,   **lowerCAmelCase__					,   ):
            _A=						cache_dir
            _A=						force_download
            _A=						local_files_only
            _A=						revision

            cls._set_token_in_kwargs(lowerCAmelCase__					,   lowerCAmelCase__							)

            _A,      _A=						cls.get_config_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)

            # 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(lowerCAmelCase__					,   **lowerCAmelCase__							)





@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    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		="""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    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""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    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""fine_acoustics"""



     def __init__(       self					,   lowerCAmelCase__=True					,   lowerCAmelCase__=8					,   lowerCAmelCase__=1					,   **lowerCAmelCase__							):
            _A=						n_codes_total
            _A=						n_codes_given

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





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		="""bark"""
     _SCREAMING_SNAKE_CASE  :  Any		=True



     def __init__(       self					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__=0.02					,   **lowerCAmelCase__					,   ):
            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(**lowerCAmelCase__							)
            _A=						BarkCoarseConfig(**lowerCAmelCase__							)
            _A=						BarkFineConfig(**lowerCAmelCase__							)
            _A=						AutoConfig.for_model(**lowerCAmelCase__							)

            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)



     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   **lowerCAmelCase__					,   ):
            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()					,   **lowerCAmelCase__					,   )





     def       a__	(       self							):
            _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