

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



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() = }""")
