





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


snake_case__   :				int						=	logging.get_logger(__name__)


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

snake_case__   :				Tuple						=	'\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 A_   (     _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      :Optional[int]       ,  _UpperCamelCase      :int=1024       ,  _UpperCamelCase      :Tuple=1_0048       ,  _UpperCamelCase      :str=1_0048       ,  _UpperCamelCase      :Dict=12       ,  _UpperCamelCase      :List[str]=12       ,  _UpperCamelCase      :Any=768       ,  _UpperCamelCase      :List[Any]=0.0       ,  _UpperCamelCase      :Union[str, Any]=True       ,  _UpperCamelCase      :Union[str, Any]=0.0_2       ,  _UpperCamelCase      :str=True       ,  **_UpperCamelCase      :Tuple       ,  )->   List[str]:
         __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__(**_UpperCamelCase					)






   @classmethod
   def _lowerCAmelCase (cls      :Dict       ,  _UpperCamelCase      :Union[str, os.PathLike]       ,  _UpperCamelCase      :Optional[Union[str, os.PathLike]] = None       ,  _UpperCamelCase      :bool = False       ,  _UpperCamelCase      :bool = False       ,  _UpperCamelCase      :Optional[Union[str, bool]] = None       ,  _UpperCamelCase      :str = "main"       ,  **_UpperCamelCase      :List[Any]       ,  )->   "PretrainedConfig":
         __A			  =							cache_dir
         __A			  =							force_download
         __A			  =							local_files_only
         __A			  =							revision

         cls._set_token_in_kwargs(_UpperCamelCase       ,  _UpperCamelCase					)

         __A  ,		__A			  =							cls.get_config_dict(_UpperCamelCase       ,  **_UpperCamelCase					)

         # 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(_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 A_   (     _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 A_   (     _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 A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					"""fine_acoustics"""




   def __init__(self      :Dict       ,  _UpperCamelCase      :List[str]=True       ,  _UpperCamelCase      :List[str]=8       ,  _UpperCamelCase      :int=1       ,  **_UpperCamelCase      :str					)->   Optional[Any]:
         __A			  =							n_codes_total
         __A			  =							n_codes_given

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





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					"""bark"""
   lowerCAmelCase__  	=					True




   def __init__(self      :Optional[Any]       ,  _UpperCamelCase      :Dict = None       ,  _UpperCamelCase      :Dict = None       ,  _UpperCamelCase      :Dict = None       ,  _UpperCamelCase      :Dict = None       ,  _UpperCamelCase      :str=0.0_2       ,  **_UpperCamelCase      :Optional[Any]       ,  )->   Tuple:
         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(**_UpperCamelCase					)
         __A			  =							BarkCoarseConfig(**_UpperCamelCase					)
         __A			  =							BarkFineConfig(**_UpperCamelCase					)
         __A			  =							AutoConfig.for_model(**_UpperCamelCase					)

         __A			  =							initializer_range

         super().__init__(**_UpperCamelCase					)




   @classmethod
   def _lowerCAmelCase (cls      :Tuple       ,  _UpperCamelCase      :BarkSemanticConfig       ,  _UpperCamelCase      :BarkCoarseConfig       ,  _UpperCamelCase      :BarkFineConfig       ,  _UpperCamelCase      :AutoConfig       ,  **_UpperCamelCase      :Union[str, Any]       ,  )->   Dict:
         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      :Optional[Any]					)->   List[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












from typing import TYPE_CHECKING

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


snake_case__   :				Dict						=	{
    'configuration_videomae': ['VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VideoMAEConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VideoMAEForPreTraining',
           'VideoMAEModel',
           'VideoMAEPreTrainedModel',
           'VideoMAEForVideoClassification',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				str						=	['VideoMAEFeatureExtractor']
       snake_case__   :				Tuple						=	['VideoMAEImageProcessor']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

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





