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


UpperCamelCase_  =	logging.get_logger(__name__)

UpperCamelCase_  =	{
    "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     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     'layoutlmv2'





				def __init__(		self :		List[Any]					,      snake_case_ :		Any=3_0522					,      snake_case_ :		str=768					,      snake_case_ :		List[Any]=12					,      snake_case_ :		List[str]=12					,      snake_case_ :		Union[str, Any]=3072					,      snake_case_ :		Tuple="gelu"					,      snake_case_ :		int=0.1					,      snake_case_ :		List[Any]=0.1					,      snake_case_ :		Optional[int]=512					,      snake_case_ :		Optional[Any]=2					,      snake_case_ :		int=0.02					,      snake_case_ :		str=1E-12					,      snake_case_ :		Any=0					,      snake_case_ :		Union[str, Any]=1024					,      snake_case_ :		Any=128					,      snake_case_ :		Union[str, Any]=32					,      snake_case_ :		Optional[int]=True					,      snake_case_ :		Dict=256					,      snake_case_ :		List[Any]=64					,      snake_case_ :		Any=True					,      snake_case_ :		int=[7, 7, 256]					,      snake_case_ :		Tuple=128					,      snake_case_ :		Any=128					,      snake_case_ :		Tuple=True					,      snake_case_ :		List[Any]=True					,      snake_case_ :		Union[str, Any]=False					,      snake_case_ :		List[str]=None					,      **snake_case_ :		Dict					,      ):


								"""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     :  List[Any]   =				max_ad_position_embeddings
								A     :  List[str]   =				max_rel_pos
								A     :  int   =				rel_pos_bins
								A     :  Tuple   =				fast_qkv
								A     :  Union[str, Any]   =				max_rel_ad_pos
								A     :  Any   =				rel_ad_pos_bins
								A     :  Union[str, Any]   =				convert_sync_batchnorm
								A     :  Tuple   =				image_feature_pool_shape
								A     :  Optional[int]   =				coordinate_size
								A     :  Tuple   =				shape_size
								A     :  List[Any]   =				has_relative_attention_bias
								A     :  List[Any]   =				has_spatial_attention_bias
								A     :  int   =				has_visual_segment_embedding
								A     :  List[Any]   =				(
								    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
								)





				@classmethod
				def        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""simple docstring"""

								return {
								    "MODEL.MASK_ON": True,
								    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
								    "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 :		List[Any]							):


								"""simple docstring"""

								A     :  Optional[int]   =				detectrona.config.get_cfg()
								for k, v in self.detectrona_config_args.items():
												A     :  Tuple   =				k.split('''.'''							)
												A     :  Union[str, Any]   =				detectrona_config
												for attribute in attributes[:-1]:
																A     :  Optional[Any]   =				getattr(snake_case_					,      snake_case_							)
												setattr(snake_case_					,      attributes[-1]					,      snake_case_							)

								return detectrona_config

def 							_lowerCamelCase			(					lowerCamelCase_:  list		):



				'''simple docstring'''







				A     :  Optional[int]   =				1.3
				A     :  List[str]   =				len(lowerCamelCase_		)
				A     :  Optional[Any]   =				False

				while not completed:
								# Update the gap value for a next comb
								A     :  str   =				int(gap / shrink_factor		)
								if gap <= 1:
												A     :  Tuple   =				True

								A     :  List[Any]   =				0
								while index + gap < len(lowerCamelCase_		):
												if data[index] > data[index + gap]:
																# Swap values
																A ,       A     :  int   =				data[index + gap], data[index]
																A     :  Dict   =				False
												index += 1

				return data


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase_  =	input("Enter numbers separated by a comma:\n").strip()
					UpperCamelCase_  =	[int(item) for item in user_input.split(",")]
					print(comb_sort(unsorted))