code: 
  '''simple docstring'''
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      "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 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							List[Any]       		=  """layoutlmv2"""
  
  
  
  			def __init__(					self       ,     lowerCamelCase__=30_522       ,     lowerCamelCase__=768       ,     lowerCamelCase__=12       ,     lowerCamelCase__=12       ,     lowerCamelCase__=3_072       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=512       ,     lowerCamelCase__=2       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=1e-12       ,     lowerCamelCase__=0       ,     lowerCamelCase__=1_024       ,     lowerCamelCase__=128       ,     lowerCamelCase__=32       ,     lowerCamelCase__=True       ,     lowerCamelCase__=256       ,     lowerCamelCase__=64       ,     lowerCamelCase__=True       ,     lowerCamelCase__=[7, 7, 256]       ,     lowerCamelCase__=128       ,     lowerCamelCase__=128       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=False       ,     lowerCamelCase__=None       ,     **lowerCamelCase__       ,     )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(
  									    vocab_size=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__       ,     max_position_embeddings=lowerCamelCase__       ,     type_vocab_size=lowerCamelCase__       ,     initializer_range=lowerCamelCase__       ,     layer_norm_eps=lowerCamelCase__       ,     pad_token_id=lowerCamelCase__       ,     **lowerCamelCase__       ,     )
  									lowercase__							 =       max_ad_position_embeddings
  									lowercase__							 =       max_rel_pos
  									lowercase__							 =       rel_pos_bins
  									lowercase__							 =       fast_qkv
  									lowercase__							 =       max_rel_ad_pos
  									lowercase__							 =       rel_ad_pos_bins
  									lowercase__							 =       convert_sync_batchnorm
  									lowercase__							 =       image_feature_pool_shape
  									lowercase__							 =       coordinate_size
  									lowercase__							 =       shape_size
  									lowercase__							 =       has_relative_attention_bias
  									lowercase__							 =       has_spatial_attention_bias
  									lowercase__							 =       has_visual_segment_embedding
  									lowercase__							 =       (
  									    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  									)
  
  
  
  			@classmethod
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return {
  									    "MODEL.MASK_ON": True,
  									    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
  									    "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": 2_000,
  									    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
  									    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
  									    "MODEL.POST_NMS_TOPK_TEST": 1_000,
  									    "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 A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       detectrona.config.get_cfg()
  									for k, v in self.detectrona_config_args.items():
  															lowercase__							 =       k.split("""."""       )
  															lowercase__							 =       detectrona_config
  															for attribute in attributes[:-1]:
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  															setattr(lowerCamelCase__       ,     attributes[-1]       ,     lowerCamelCase__       )
  
  									return detectrona_config
style_context: 
  '''simple docstring'''
  
  
  
  def  _A		(							lowercase__	):
  
  						# validate:
  						if not isinstance(lowercase__		,		lowercase__	) or not all(
  						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
  												raise ValueError("""The citations should be a list of non negative integers."""	)
  
  						citations.sort()
  						lowercase__							 =       len(lowercase__	)
  
  						for i in range(lowercase__	):
  												if citations[len_citations - 1 - i] <= i:
  																		return i
  
  						return len_citations
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  
  