

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 		__magic_name__    (							__SCREAMING_SNAKE_CASE							):
   lowerCamelCase__				:			Tuple												=     'layoutlmv2'



   def __init__(       self,					lowercase_=30522,					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_,					)							->					Any:






     """simple docstring"""






     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__,					)
     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 )							->					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 				_UpperCAmelCase				(       self )							->					str:






     """simple docstring"""






     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(UpperCamelCase__,					UpperCamelCase__ )
       setattr(UpperCamelCase__,					attributes[-1],					UpperCamelCase__ )

     return detectrona_config



import math
from decimal import Decimal, getcontext




def     UpperCAmelCase__       (    _A   :				int = 4_00_00_00							):





  '''simple docstring'''







  try:
    a__						=int(_A							)
  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)			,   _A							) - 1							) // 3) * 3 + 2
  a__						=Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
  a__						=num // 2
  return int(_A							)


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