



"""simple docstring"""





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


SCREAMING_SNAKE_CASE				:     Dict          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[Any]          =	{
    '''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       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   'vit-hybrid'







   def __init__(self       ,	lowerCamelCase=None       ,	lowerCamelCase=768       ,	lowerCamelCase=12       ,	lowerCamelCase=12       ,	lowerCamelCase=3_072       ,	lowerCamelCase="gelu"       ,	lowerCamelCase=0.0       ,	lowerCamelCase=0.0       ,	lowerCamelCase=0.02       ,	lowerCamelCase=1e-12       ,	lowerCamelCase=224       ,	lowerCamelCase=1       ,	lowerCamelCase=3       ,	lowerCamelCase=[1, 1_024, 24, 24]       ,	lowerCamelCase=True       ,	**lowerCamelCase       ,	):




      '''simple docstring'''




      super().__init__(**lowerCamelCase				)

      if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""				)
         _lowerCAmelCase					     =		{
             """global_padding""": """same""",
             """layer_type""": """bottleneck""",
             """depths""": [3, 4, 9],
             """out_features""": ["""stage3"""],
             """embedding_dynamic_padding""": True,
         }

      if isinstance(lowerCamelCase       ,	lowerCamelCase				):
         if "model_type" in backbone_config:
            _lowerCAmelCase					     =		CONFIG_MAPPING[backbone_config["""model_type"""]]
         else:
            logger.info(
                """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""				)
            _lowerCAmelCase					     =		BitConfig
         _lowerCAmelCase					     =		backbone_config_class(**lowerCamelCase				)

      _lowerCAmelCase					     =		backbone_featmap_shape
      _lowerCAmelCase					     =		backbone_config
      _lowerCAmelCase					     =		hidden_size
      _lowerCAmelCase					     =		num_hidden_layers
      _lowerCAmelCase					     =		num_attention_heads
      _lowerCAmelCase					     =		intermediate_size
      _lowerCAmelCase					     =		hidden_act
      _lowerCAmelCase					     =		hidden_dropout_prob
      _lowerCAmelCase					     =		attention_probs_dropout_prob
      _lowerCAmelCase					     =		initializer_range
      _lowerCAmelCase					     =		layer_norm_eps
      _lowerCAmelCase					     =		image_size
      _lowerCAmelCase					     =		patch_size
      _lowerCAmelCase					     =		num_channels
      _lowerCAmelCase					     =		qkv_bias







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		copy.deepcopy(self.__dict__				)
      _lowerCAmelCase					     =		self.backbone_config.to_dict()
      _lowerCAmelCase					     =		self.__class__.model_type
      return output




"""simple docstring"""





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	{
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE				:     Any          =	[
      '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''AlignModel''',
      '''AlignPreTrainedModel''',
      '''AlignTextModel''',
      '''AlignVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_align import (
      ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AlignConfig,
      AlignTextConfig,
      AlignVisionConfig,
  )
  from .processing_align import AlignProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_align import (
        ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
        AlignModel,
        AlignPreTrainedModel,
        AlignTextModel,
        AlignVisionModel,
    )

else:
  import sys

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