


'''simple docstring'''






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


a_       :     Optional[Any] 							=     logging.get_logger(__name__)

a_       :     Dict 							=     {
    "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  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""layoutlmv2"""
 def __init__(			self   ,					__magic_name__=3_05_22   ,					__magic_name__=7_68   ,					__magic_name__=12   ,					__magic_name__=12   ,					__magic_name__=30_72   ,					__magic_name__="gelu"   ,					__magic_name__=0.1   ,					__magic_name__=0.1   ,					__magic_name__=5_12   ,					__magic_name__=2   ,					__magic_name__=0.0_2   ,					__magic_name__=1e-12   ,					__magic_name__=0   ,					__magic_name__=10_24   ,					__magic_name__=1_28   ,					__magic_name__=32   ,					__magic_name__=True   ,					__magic_name__=2_56   ,					__magic_name__=64   ,					__magic_name__=True   ,					__magic_name__=[7, 7, 2_56]   ,					__magic_name__=1_28   ,					__magic_name__=1_28   ,					__magic_name__=True   ,					__magic_name__=True   ,					__magic_name__=False   ,					__magic_name__=None   ,					**__magic_name__   ,					)       ->				Union[str, Any]:
      super().__init__(
          vocab_size=__magic_name__   ,					hidden_size=__magic_name__   ,					num_hidden_layers=__magic_name__   ,					num_attention_heads=__magic_name__   ,					intermediate_size=__magic_name__   ,					hidden_act=__magic_name__   ,					hidden_dropout_prob=__magic_name__   ,					attention_probs_dropout_prob=__magic_name__   ,					max_position_embeddings=__magic_name__   ,					type_vocab_size=__magic_name__   ,					initializer_range=__magic_name__   ,					layer_norm_eps=__magic_name__   ,					pad_token_id=__magic_name__   ,					**__magic_name__   ,					)
      _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": [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], [1_28], [2_56], [5_12]],
          "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
          "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
          "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
          "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
          "MODEL.POST_NMS_TOPK_TEST": 10_00,
          "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": 1_01,
          "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
          "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    )       ->				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(__magic_name__   ,					__magic_name__    )
           setattr(__magic_name__   ,					attributes[-1]   ,					__magic_name__    )

      return detectrona_config





'''simple docstring'''






import math
from decimal import Decimal, getcontext






def 						_A					(lowerCAmelCase__	:int = 4_00_00_00		) ->	int:




     '''simple docstring'''



     try:
          _a         =				int(lowerCAmelCase__		)
     except (TypeError, ValueError):
          raise TypeError('Parameter n must be int or castable to int.'		)
     if n <= 0:
          raise ValueError('Parameter n must be greater than or equal to one.'		)
     _a         =				1_00
     _a         =				(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

     _a         =				(math.floor(math.log(n * (phi + 2)					,    lowerCAmelCase__		) - 1		) // 3) * 3 + 2
     _a         =				Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
     _a         =				num // 2
     return int(lowerCAmelCase__		)


if __name__ == "__main__":
 print(f'''{solution() = }''')

