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


__a							  =						logging.get_logger(__name__)

__a							  =						{
    """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        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"layoutlmv2"







	def __init__(      self				:			Dict							,			snake_case__				:			int=3_05_22							,			snake_case__				:			Any=7_68							,			snake_case__				:			Tuple=12							,			snake_case__				:			Dict=12							,			snake_case__				:			Tuple=30_72							,			snake_case__				:			str="gelu"							,			snake_case__				:			Optional[Any]=0.1							,			snake_case__				:			List[Any]=0.1							,			snake_case__				:			Dict=5_12							,			snake_case__				:			List[str]=2							,			snake_case__				:			Any=0.02							,			snake_case__				:			Tuple=1E-12							,			snake_case__				:			Dict=0							,			snake_case__				:			Union[str, Any]=10_24							,			snake_case__				:			int=1_28							,			snake_case__				:			List[Any]=32							,			snake_case__				:			Any=True							,			snake_case__				:			List[str]=2_56							,			snake_case__				:			Optional[int]=64							,			snake_case__				:			int=True							,			snake_case__				:			Tuple=[7, 7, 2_56]							,			snake_case__				:			Union[str, Any]=1_28							,			snake_case__				:			Optional[int]=1_28							,			snake_case__				:			Optional[Any]=True							,			snake_case__				:			Union[str, Any]=True							,			snake_case__				:			str=False							,			snake_case__				:			List[str]=None							,			**snake_case__				:			int							,			):
			"""simple docstring"""





			super().__init__(
			    vocab_size=snake_case__							,			hidden_size=snake_case__							,			num_hidden_layers=snake_case__							,			num_attention_heads=snake_case__							,			intermediate_size=snake_case__							,			hidden_act=snake_case__							,			hidden_dropout_prob=snake_case__							,			attention_probs_dropout_prob=snake_case__							,			max_position_embeddings=snake_case__							,			type_vocab_size=snake_case__							,			initializer_range=snake_case__							,			layer_norm_eps=snake_case__							,			pad_token_id=snake_case__							,			**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        _a					(      self				:			Dict ):
			"""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], [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        _a					(      self				:			Optional[int] ):
			"""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(snake_case__							,			snake_case__ )
					setattr(snake_case__							,			attributes[-1]							,			snake_case__ )

			return detectrona_config






from __future__ import annotations

import math
import random

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->list[int]:
		A				=d * e - 1
		A				=0
		A				=0
		while p == 0:
				A				=random.randint(2  ,				n - 1		)
				A				=k
				while True:
						if t % 2 == 0:
								A				=t // 2
								A				=(g**t) % n
								A				=math.gcd(x - 1  ,				a_		)
								if x > 1 and y > 1:
										A				=y
										A				=n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()




