


"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '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      __lowerCamelCase	(       lowerCAmelCase__					):

     '''simple docstring'''
     A_							:						Any     =		"layoutlmv2"


     def __init__(   self     ,					__UpperCAmelCase=30522     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.02     ,					__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     ,					)							->				List[str]:
          super().__init__(
              vocab_size=_SCREAMING_SNAKE_CASE     ,					hidden_size=_SCREAMING_SNAKE_CASE     ,					num_hidden_layers=_SCREAMING_SNAKE_CASE     ,					num_attention_heads=_SCREAMING_SNAKE_CASE     ,					intermediate_size=_SCREAMING_SNAKE_CASE     ,					hidden_act=_SCREAMING_SNAKE_CASE     ,					hidden_dropout_prob=_SCREAMING_SNAKE_CASE     ,					attention_probs_dropout_prob=_SCREAMING_SNAKE_CASE     ,					max_position_embeddings=_SCREAMING_SNAKE_CASE     ,					type_vocab_size=_SCREAMING_SNAKE_CASE     ,					initializer_range=_SCREAMING_SNAKE_CASE     ,					layer_norm_eps=_SCREAMING_SNAKE_CASE     ,					pad_token_id=_SCREAMING_SNAKE_CASE     ,					**_SCREAMING_SNAKE_CASE     ,					)
          _a     						=					max_ad_position_embeddings
          _a     						=					max_rel_pos
          _a     						=					rel_pos_bins
          _a     						=					fast_qkv
          _a     						=					max_rel_ad_pos
          _a     						=					rel_ad_pos_bins
          _a     						=					convert_sync_batchnorm
          _a     						=					image_feature_pool_shape
          _a     						=					coordinate_size
          _a     						=					shape_size
          _a     						=					has_relative_attention_bias
          _a     						=					has_spatial_attention_bias
          _a     						=					has_visual_segment_embedding
          _a     						=					(
              detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
          )


     @classmethod
     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          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 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          _a     						=					detectrona.config.get_cfg()
          for k, v in self.detectrona_config_args.items():
               _a     						=					k.split('''.'''				)
               _a     						=					detectrona_config
               for attribute in attributes[:-1]:
                    _a     						=					getattr(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE				)
               setattr(_SCREAMING_SNAKE_CASE     ,					attributes[-1]     ,					_SCREAMING_SNAKE_CASE				)

          return detectrona_config


from __future__ import annotations




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# Base Case
							if counter == 0 or capacity == 0:
														return 0

							# If weight of the nth item is more than Knapsack of capacity,
							#   then this item cannot be included in the optimal solution,
							# else return the maximum of two cases:
							#   (1) nth item included
							#   (2) not included
							if weights[counter - 1] > capacity:
														return knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
