





'''simple docstring'''




from __future__ import annotations




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->list[int]:
	snake_case__  					=      {}
	for index, val in enumerate(UpperCAmelCase_       ):
		snake_case__  					=      target - val
		if compl in chk_map:
			return [chk_map[compl], index]
		snake_case__  					=      index
	return []


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(f"""{two_sum([2, 7, 11, 15], 9) = }""")










'''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



