code: 
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {
      '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
      '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
      # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
  }
  
  # soft dependency
  if is_detectrona_available():
         import detectrona
  
  
  
  
  
  
  
  class 		__magic_name__    (							__SCREAMING_SNAKE_CASE							):
     lowerCamelCase__				:			Tuple												=     'layoutlmv2'
  
  
  
     def __init__(       self,					lowercase_=30522,					lowercase_=768,					lowercase_=12,					lowercase_=12,					lowercase_=3072,					lowercase_="gelu",					lowercase_=0.1,					lowercase_=0.1,					lowercase_=512,					lowercase_=2,					lowercase_=0.02,					lowercase_=1E-12,					lowercase_=0,					lowercase_=1024,					lowercase_=128,					lowercase_=32,					lowercase_=True,					lowercase_=256,					lowercase_=64,					lowercase_=True,					lowercase_=[7, 7, 256],					lowercase_=128,					lowercase_=128,					lowercase_=True,					lowercase_=True,					lowercase_=False,					lowercase_=None,					**lowercase_,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       super().__init__(
           vocab_size=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__,					max_position_embeddings=UpperCamelCase__,					type_vocab_size=UpperCamelCase__,					initializer_range=UpperCamelCase__,					layer_norm_eps=UpperCamelCase__,					pad_token_id=UpperCamelCase__,					**UpperCamelCase__,					)
       a__						=max_ad_position_embeddings
       a__						=max_rel_pos
       a__						=rel_pos_bins
       a__						=fast_qkv
       a__						=max_rel_ad_pos
       a__						=rel_ad_pos_bins
       a__						=convert_sync_batchnorm
       a__						=image_feature_pool_shape
       a__						=coordinate_size
       a__						=shape_size
       a__						=has_relative_attention_bias
       a__						=has_spatial_attention_bias
       a__						=has_visual_segment_embedding
       a__						=(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )
  
  
  
     @classmethod
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return {
           "MODEL.MASK_ON": True,
           "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
           "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
           "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
           "MODEL.POST_NMS_TOPK_TEST": 1000,
           "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
           "MODEL.ROI_HEADS.NUM_CLASSES": 5,
           "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
           "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
           "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
           "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 101,
           "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 32,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
         a__						=k.split('''.''' )
         a__						=detectrona_config
         for attribute in attributes[:-1]:
           a__						=getattr(UpperCamelCase__,					UpperCamelCase__ )
         setattr(UpperCamelCase__,					attributes[-1],					UpperCamelCase__ )
  
       return detectrona_config
  
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() = }""")
  