code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->int:
  	snake_case__  					=      len(UpperCAmelCase_       )
  	if n in (0, 1):
  		return 0
  	return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase_       )       )       )
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  a__				: Optional[Any]									=    logging.get_logger(__name__)
  
  a__				: List[str]									=    {
      '''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 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''layoutlmv2'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=3_0522			,					UpperCamelCase_=768			,					UpperCamelCase_=12			,					UpperCamelCase_=12			,					UpperCamelCase_=3072			,					UpperCamelCase_="gelu"			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.1			,					UpperCamelCase_=512			,					UpperCamelCase_=2			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=1E-1_2			,					UpperCamelCase_=0			,					UpperCamelCase_=1024			,					UpperCamelCase_=128			,					UpperCamelCase_=32			,					UpperCamelCase_=True			,					UpperCamelCase_=256			,					UpperCamelCase_=64			,					UpperCamelCase_=True			,					UpperCamelCase_=[7, 7, 256]			,					UpperCamelCase_=128			,					UpperCamelCase_=128			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_=False			,					UpperCamelCase_=None			,					**UpperCamelCase_			,					)			->		Tuple:
  		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_			,					)
  		snake_case__  					=      max_ad_position_embeddings
  		snake_case__  					=      max_rel_pos
  		snake_case__  					=      rel_pos_bins
  		snake_case__  					=      fast_qkv
  		snake_case__  					=      max_rel_ad_pos
  		snake_case__  					=      rel_ad_pos_bins
  		snake_case__  					=      convert_sync_batchnorm
  		snake_case__  					=      image_feature_pool_shape
  		snake_case__  					=      coordinate_size
  		snake_case__  					=      shape_size
  		snake_case__  					=      has_relative_attention_bias
  		snake_case__  					=      has_spatial_attention_bias
  		snake_case__  					=      has_visual_segment_embedding
  		snake_case__  					=      (
  		    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  		)
  
  
  
  
  
  	@classmethod
  	def      _snake_case      (		self		)			->		Dict:
  		return {
  		    "MODEL.MASK_ON": True,
  		    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
  		    "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      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      detectrona.config.get_cfg()
  		for k, v in self.detectrona_config_args.items():
  			snake_case__  					=      k.split('.'		)
  			snake_case__  					=      detectrona_config
  			for attribute in attributes[:-1]:
  				snake_case__  					=      getattr(UpperCamelCase_			,					UpperCamelCase_		)
  			setattr(UpperCamelCase_			,					attributes[-1]			,					UpperCamelCase_		)
  
  		return detectrona_config
  
  
  
  