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
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {
      '''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 		__magic_name__    (							lowerCamelCase__							):
     lowerCamelCase__				:			Dict												=     "vit-hybrid"
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=768,					lowercase_=12,					lowercase_=12,					lowercase_=3072,					lowercase_="gelu",					lowercase_=0.0,					lowercase_=0.0,					lowercase_=0.02,					lowercase_=1E-12,					lowercase_=224,					lowercase_=1,					lowercase_=3,					lowercase_=[1, 1024, 24, 24],					lowercase_=True,					**lowercase_,					)							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       super().__init__(**UpperCamelCase_ )
  
       if backbone_config is None:
         logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.''' )
         a__						={
             '''global_padding''': '''same''',
             '''layer_type''': '''bottleneck''',
             '''depths''': [3, 4, 9],
             '''out_features''': ['''stage3'''],
             '''embedding_dynamic_padding''': True,
         }
  
       if isinstance(UpperCamelCase_,					UpperCamelCase_ ):
         if "model_type" in backbone_config:
           a__						=CONFIG_MAPPING[backbone_config['''model_type''']]
         else:
           logger.info(
               '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.''' )
           a__						=BitConfig
         a__						=backbone_config_class(**UpperCamelCase_ )
  
       a__						=backbone_featmap_shape
       a__						=backbone_config
       a__						=hidden_size
       a__						=num_hidden_layers
       a__						=num_attention_heads
       a__						=intermediate_size
       a__						=hidden_act
       a__						=hidden_dropout_prob
       a__						=attention_probs_dropout_prob
       a__						=initializer_range
       a__						=layer_norm_eps
       a__						=image_size
       a__						=patch_size
       a__						=num_channels
       a__						=qkv_bias
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=copy.deepcopy(self.__dict__ )
       a__						=self.backbone_config.to_dict()
       a__						=self.__class__.model_type
       return output
  
style_context: 
  
  import math
  from decimal import Decimal, getcontext
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int = 4_00_00_00							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    try:
      a__						=int(_A							)
    except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.'''							)
    if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.'''							)
    a__						=1_00
    a__						=(Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)
  
    a__						=(math.floor(math.log(n * (phi + 2)			,   _A							) - 1							) // 3) * 3 + 2
    a__						=Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
    a__						=num // 2
    return int(_A							)
  
  
  if __name__ == "__main__":
         print(f"""{solution() = }""")
  