






"""simple docstring"""


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


_UpperCamelCase   :						str     						= logging.get_logger(__name__)

_UpperCamelCase   :						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   (   snake_case_				):
  __lowercase    :    List[Any]						=					'layoutlmv2'



  def __init__(self  :		Any				,			lowercase_  :		Tuple=30_522				,			lowercase_  :		Union[str, Any]=768				,			lowercase_  :		int=12				,			lowercase_  :		Optional[int]=12				,			lowercase_  :		str=3_072				,			lowercase_  :		Dict="gelu"				,			lowercase_  :		Any=0.1				,			lowercase_  :		Optional[Any]=0.1				,			lowercase_  :		Dict=512				,			lowercase_  :		Union[str, Any]=2				,			lowercase_  :		List[str]=0.02				,			lowercase_  :		Tuple=1E-12				,			lowercase_  :		Dict=0				,			lowercase_  :		Tuple=1_024				,			lowercase_  :		Any=128				,			lowercase_  :		Optional[Any]=32				,			lowercase_  :		List[Any]=True				,			lowercase_  :		List[Any]=256				,			lowercase_  :		List[Any]=64				,			lowercase_  :		List[str]=True				,			lowercase_  :		int=[7, 7, 256]				,			lowercase_  :		List[Any]=128				,			lowercase_  :		Any=128				,			lowercase_  :		Tuple=True				,			lowercase_  :		Optional[Any]=True				,			lowercase_  :		int=False				,			lowercase_  :		List[Any]=None				,			**lowercase_  :		Union[str, Any]				,			)		->							Any:
   super().__init__(
       vocab_size=lowercase_				,			hidden_size=lowercase_				,			num_hidden_layers=lowercase_				,			num_attention_heads=lowercase_				,			intermediate_size=lowercase_				,			hidden_act=lowercase_				,			hidden_dropout_prob=lowercase_				,			attention_probs_dropout_prob=lowercase_				,			max_position_embeddings=lowercase_				,			type_vocab_size=lowercase_				,			initializer_range=lowercase_				,			layer_norm_eps=lowercase_				,			pad_token_id=lowercase_				,			**lowercase_				,			)
   __A	:       Any       =	max_ad_position_embeddings
   __A	:       Dict       =	max_rel_pos
   __A	:       List[str]       =	rel_pos_bins
   __A	:       List[Any]       =	fast_qkv
   __A	:       Union[str, Any]       =	max_rel_ad_pos
   __A	:       Any       =	rel_ad_pos_bins
   __A	:       List[Any]       =	convert_sync_batchnorm
   __A	:       Optional[Any]       =	image_feature_pool_shape
   __A	:       int       =	coordinate_size
   __A	:       Any       =	shape_size
   __A	:       Tuple       =	has_relative_attention_bias
   __A	:       Optional[Any]       =	has_spatial_attention_bias
   __A	:       int       =	has_visual_segment_embedding
   __A	:       Dict       =	(
       detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
   )



  @classmethod
  def 						A_				(self  :		List[str]   )		->							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": [[32], [64], [128], [256], [512]],
       "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
       "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
       "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
       "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
       "MODEL.POST_NMS_TOPK_TEST": 1_000,
       "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 						A_				(self  :		Union[str, Any]   )		->							Any:
   __A	:       int       =	detectrona.config.get_cfg()
   for k, v in self.detectrona_config_args.items():
    __A	:       Tuple       =	k.split('.'   )
    __A	:       Optional[Any]       =	detectrona_config
    for attribute in attributes[:-1]:
     __A	:       List[str]       =	getattr(lowercase_				,			lowercase_   )
    setattr(lowercase_				,			attributes[-1]				,			lowercase_   )

   return detectrona_config













"""simple docstring"""


import math




def 				a (     UpperCamelCase_							:				int		)  ->		list[int]:
 if not isinstance(UpperCamelCase_     ,	UpperCamelCase_		):
  __A	:       Any       =	F"Input value of [number={number}] must be an integer"
  raise TypeError(UpperCamelCase_		)
 if number < 1:
  __A	:       int       =	F"Input value of [number={number}] must be a positive integer"
  raise ValueError(UpperCamelCase_		)
 __A	:       Optional[Any]       =	[number]
 while number != 1:
  if number % 2 == 0:
   __A	:       Optional[int]       =	math.floor(math.sqrt(UpperCamelCase_		)		)
  else:
   __A	:       List[str]       =	math.floor(
       math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		)		)
  sequence.append(UpperCamelCase_		)
 return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()





