code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  _lowerCAmelCase       :      Optional[int]							=							logging.get_logger(__name__)
  
  _lowerCAmelCase       :      Optional[Any]							=							{
      "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 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				'layoutlmv2'
  
  
  
        def __init__(  self  :			str  ,			A  :			Optional[int]=3_0_5_2_2  ,			A  :			List[Any]=7_6_8  ,			A  :			Tuple=1_2  ,			A  :			str=1_2  ,			A  :			str=3_0_7_2  ,			A  :			Optional[int]="gelu"  ,			A  :			int=0.1  ,			A  :			Any=0.1  ,			A  :			str=5_1_2  ,			A  :			str=2  ,			A  :			Optional[int]=0.02  ,			A  :			int=1e-12  ,			A  :			Any=0  ,			A  :			Union[str, Any]=1_0_2_4  ,			A  :			str=1_2_8  ,			A  :			List[str]=3_2  ,			A  :			Tuple=True  ,			A  :			Optional[Any]=2_5_6  ,			A  :			str=6_4  ,			A  :			Any=True  ,			A  :			Union[str, Any]=[7, 7, 2_5_6]  ,			A  :			Tuple=1_2_8  ,			A  :			int=1_2_8  ,			A  :			Optional[Any]=True  ,			A  :			int=True  ,			A  :			Dict=False  ,			A  :			Any=None  ,			**A  :			Tuple  ,			):
             super().__init__(
                 vocab_size=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  ,			max_position_embeddings=A  ,			type_vocab_size=A  ,			initializer_range=A  ,			layer_norm_eps=A  ,			pad_token_id=A  ,			**A  ,			)
             _UpperCAmelCase    :			Tuple              =					max_ad_position_embeddings
             _UpperCAmelCase    :			List[Any]              =					max_rel_pos
             _UpperCAmelCase    :			Union[str, Any]              =					rel_pos_bins
             _UpperCAmelCase    :			List[Any]              =					fast_qkv
             _UpperCAmelCase    :			str              =					max_rel_ad_pos
             _UpperCAmelCase    :			Dict              =					rel_ad_pos_bins
             _UpperCAmelCase    :			int              =					convert_sync_batchnorm
             _UpperCAmelCase    :			List[Any]              =					image_feature_pool_shape
             _UpperCAmelCase    :			Optional[Any]              =					coordinate_size
             _UpperCAmelCase    :			Dict              =					shape_size
             _UpperCAmelCase    :			List[Any]              =					has_relative_attention_bias
             _UpperCAmelCase    :			Any              =					has_spatial_attention_bias
             _UpperCAmelCase    :			str              =					has_visual_segment_embedding
             _UpperCAmelCase    :			Union[str, Any]              =					(
                 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": [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": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
                 "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                 "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
                 "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
                 "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
                 "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
                 "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": 1_4,
                 "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
                 "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
                 "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
                 "MODEL.RESNETS.DEPTH": 1_0_1,
                 "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
                 "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                 "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.RESNETS.NUM_GROUPS": 3_2,
                 "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
                 "MODEL.RESNETS.STRIDE_IN_1X1": False,
             }
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			str              =					detectrona.config.get_cfg()
             for k, v in self.detectrona_config_args.items():
                  _UpperCAmelCase    :			Optional[int]              =					k.split("."		)
                  _UpperCAmelCase    :			Union[str, Any]              =					detectrona_config
                  for attribute in attributes[:-1]:
                       _UpperCAmelCase    :			Optional[int]              =					getattr(A  ,			A		)
                  setattr(A  ,			attributes[-1]  ,			A		)
  
             return detectrona_config
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import sys
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if not numbers:
            return 0
       if target == 0 and target in numbers:
            return 0
       if not isinstance(SCREAMING_SNAKE_CASE__		,						(list, tuple)       ) or not all(
           isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
            raise ValueError("numbers must be an iterable of integers"       )
  
       _UpperCAmelCase    :			List[Any]              =					0
       _UpperCAmelCase    :			str              =					sys.maxsize
  
       while right < len(SCREAMING_SNAKE_CASE__       ):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                 _UpperCAmelCase    :			Any              =					min(SCREAMING_SNAKE_CASE__		,						right - left + 1       )
                 curr_sum -= numbers[left]
                 left += 1
            right += 1
  
       return 0 if min_len == sys.maxsize else min_len
  