code: 
  
  
  
  import copy
  from typing import Dict
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import CONFIG_MAPPING
  from ..bit import BitConfig
  
  
  a__						:			Optional[Any]										=			logging.get_logger(__name__)
  
  a__						:			str										=			{
      'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
      # See all ViT hybrid models at https://huggingface.co/models?filter=vit
  }
  class        UpperCAmelCase__  (    lowerCamelCase__):
   __SCREAMING_SNAKE_CASE					      =				'vit-hybrid'
  
  
  
   def __init__(  self		,	lowercase=None		,	lowercase=7_6_8		,	lowercase=1_2		,	lowercase=1_2		,	lowercase=3_0_7_2		,	lowercase="gelu"		,	lowercase=0.0		,	lowercase=0.0		,	lowercase=0.02		,	lowercase=1E-12		,	lowercase=2_2_4		,	lowercase=1		,	lowercase=3		,	lowercase=[1, 1_0_2_4, 2_4, 2_4]		,	lowercase=True		,	**lowercase		,	)  ->							Tuple:
    super().__init__(**lowercase_			)
  
    if backbone_config is None:
     logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""			)
     __UpperCamelCase				      =  {
         """global_padding""": """same""",
         """layer_type""": """bottleneck""",
         """depths""": [3, 4, 9],
         """out_features""": ["""stage3"""],
         """embedding_dynamic_padding""": True,
     }
  
    if isinstance(lowercase_		,	lowercase_			):
     if "model_type" in backbone_config:
      __UpperCamelCase				      =  CONFIG_MAPPING[backbone_config["""model_type"""]]
     else:
      logger.info(
          """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""			)
      __UpperCamelCase				      =  BitConfig
     __UpperCamelCase				      =  backbone_config_class(**lowercase_			)
  
    __UpperCamelCase				      =  backbone_featmap_shape
    __UpperCamelCase				      =  backbone_config
    __UpperCamelCase				      =  hidden_size
    __UpperCamelCase				      =  num_hidden_layers
    __UpperCamelCase				      =  num_attention_heads
    __UpperCamelCase				      =  intermediate_size
    __UpperCamelCase				      =  hidden_act
    __UpperCamelCase				      =  hidden_dropout_prob
    __UpperCamelCase				      =  attention_probs_dropout_prob
    __UpperCamelCase				      =  initializer_range
    __UpperCamelCase				      =  layer_norm_eps
    __UpperCamelCase				      =  image_size
    __UpperCamelCase				      =  patch_size
    __UpperCamelCase				      =  num_channels
    __UpperCamelCase				      =  qkv_bias
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict[str, any]:
    __UpperCamelCase				      =  copy.deepcopy(self.__dict__			)
    __UpperCamelCase				      =  self.backbone_config.to_dict()
    __UpperCamelCase				      =  self.__class__.model_type
    return output
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  a__						:			Optional[int]										=			{
      'configuration_bark': [
          'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'BarkCoarseConfig',
          'BarkConfig',
          'BarkFineConfig',
          'BarkSemanticConfig',
      ],
      'processing_bark': ['BarkProcessor'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Any										=			[
       'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BarkFineModel',
       'BarkSemanticModel',
       'BarkCoarseModel',
       'BarkModel',
       'BarkPreTrainedModel',
       'BarkCausalModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_bark import (
       BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BarkCoarseConfig,
       BarkConfig,
       BarkFineConfig,
       BarkSemanticConfig,
   )
   from .processing_bark import BarkProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_bark import (
        BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
        BarkCausalModel,
        BarkCoarseModel,
        BarkFineModel,
        BarkModel,
        BarkPreTrainedModel,
        BarkSemanticModel,
    )
  
  else:
   import sys
  
   a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  