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 				A    (_UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							"""layoutlmv2"""
  
  
  
        def __init__(					self     ,  lowercase_=3_0522     ,  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_     ,  )      ->   str:
  
  
  
               '''simple docstring'''
  
               super().__init__(
                   vocab_size=lowercase_     ,  hidden_size=lowercase_     ,  num_hidden_layers=lowercase_     ,  num_attention_heads=lowercase_     ,  intermediate_size=lowercase_     ,  hidden_act=lowercase_     ,  hidden_dropout_prob=lowercase_     ,  attention_probs_dropout_prob=lowercase_     ,  max_position_embeddings=lowercase_     ,  type_vocab_size=lowercase_     ,  initializer_range=lowercase_     ,  layer_norm_eps=lowercase_     ,  pad_token_id=lowercase_     ,  **lowercase_     ,  )
               _snake_case      :							Union[str, Any]     				=						max_ad_position_embeddings
               _snake_case      :							List[str]     				=						max_rel_pos
               _snake_case      :							str     				=						rel_pos_bins
               _snake_case      :							str     				=						fast_qkv
               _snake_case      :							List[Any]     				=						max_rel_ad_pos
               _snake_case      :							Optional[Any]     				=						rel_ad_pos_bins
               _snake_case      :							int     				=						convert_sync_batchnorm
               _snake_case      :							int     				=						image_feature_pool_shape
               _snake_case      :							Union[str, Any]     				=						coordinate_size
               _snake_case      :							Union[str, Any]     				=						shape_size
               _snake_case      :							Optional[Any]     				=						has_relative_attention_bias
               _snake_case      :							List[str]     				=						has_spatial_attention_bias
               _snake_case      :							str     				=						has_visual_segment_embedding
               _snake_case      :							str     				=						(
                   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": [57.375, 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    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						detectrona.config.get_cfg()
               for k, v in self.detectrona_config_args.items():
                      _snake_case      :							List[str]     				=						k.split('''.'''					)
                      _snake_case      :							Any     				=						detectrona_config
                      for attribute in attributes[:-1]:
                             _snake_case      :							Tuple     				=						getattr(lowercase_     ,  lowercase_					)
                      setattr(lowercase_     ,  attributes[-1]     ,  lowercase_					)
  
               return detectrona_config
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def 						A_			(  lowercase_  )			->						list:
         if len(lowercase_  ) <= 1:
                return data
         else:
                return [
                    *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                    data[0],
                    *quick_sort([e for e in data[1:] if e > data[0]]  ),
                ]
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  
  
  