



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










'''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__)





