





'''simple docstring'''



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


snake_case_   :  Tuple								=      logging.get_logger(__name__)

snake_case_   :  Tuple								=      {
    '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_ (  lowercase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    """layoutlmv2"""




			def __init__(					self			,     A_=3_05_22			,     A_=7_68			,     A_=12			,     A_=12			,     A_=30_72			,     A_="gelu"			,     A_=0.1			,     A_=0.1			,     A_=5_12			,     A_=2			,     A_=0.02			,     A_=1e-1_2			,     A_=0			,     A_=10_24			,     A_=1_28			,     A_=32			,     A_=True			,     A_=2_56			,     A_=64			,     A_=True			,     A_=[7, 7, 2_56]			,     A_=1_28			,     A_=1_28			,     A_=True			,     A_=True			,     A_=False			,     A_=None			,     **A_			,     ):
								super().__init__(
								    vocab_size=A_			,     hidden_size=A_			,     num_hidden_layers=A_			,     num_attention_heads=A_			,     intermediate_size=A_			,     hidden_act=A_			,     hidden_dropout_prob=A_			,     attention_probs_dropout_prob=A_			,     max_position_embeddings=A_			,     type_vocab_size=A_			,     initializer_range=A_			,     layer_norm_eps=A_			,     pad_token_id=A_			,     **A_			,     )
								_UpperCamelCase											=   max_ad_position_embeddings
								_UpperCamelCase											=   max_rel_pos
								_UpperCamelCase											=   rel_pos_bins
								_UpperCamelCase											=   fast_qkv
								_UpperCamelCase											=   max_rel_ad_pos
								_UpperCamelCase											=   rel_ad_pos_bins
								_UpperCamelCase											=   convert_sync_batchnorm
								_UpperCamelCase											=   image_feature_pool_shape
								_UpperCamelCase											=   coordinate_size
								_UpperCamelCase											=   shape_size
								_UpperCamelCase											=   has_relative_attention_bias
								_UpperCamelCase											=   has_spatial_attention_bias
								_UpperCamelCase											=   has_visual_segment_embedding
								_UpperCamelCase											=   (
								    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
								)




			@classmethod
			def       a	(					self   ):
								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   ):
								_UpperCamelCase											=   detectrona.config.get_cfg()
								for k, v in self.detectrona_config_args.items():
													_UpperCamelCase											=   k.split("."   )
													_UpperCamelCase											=   detectrona_config
													for attribute in attributes[:-1]:
																		_UpperCamelCase											=   getattr(A_			,     A_   )
													setattr(A_			,     attributes[-1]			,     A_   )

								return detectrona_config







'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
													_UpperCamelCase											=   mask_scale
													_UpperCamelCase											=   mask_init
													_UpperCamelCase											=   nn.Parameter(torch.empty(self.weight.size()   )   )
													self.init_mask()




			def       a	(					self   ):
								if self.mask_init == "constant":
													init.constant_(self.mask_scores			,     val=self.mask_scale   )
								elif self.mask_init == "uniform":
													init.uniform_(self.mask_scores			,     a=-self.mask_scale			,     b=self.mask_scale   )
								elif self.mask_init == "kaiming":
													init.kaiming_uniform_(self.mask_scores			,     a=math.sqrt(5   )   )




			def       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								elif self.pruning_method == "l0":
													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   -0.1, 1.1, 2 / 3
													if self.training:
																		_UpperCamelCase											=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0001			,     0.9999   )
																		_UpperCamelCase											=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
													else:
																		_UpperCamelCase											=   torch.sigmoid(self.mask_scores   )
													_UpperCamelCase											=   s * (r - l) + l
													_UpperCamelCase											=   s_bar.clamp(min=0.0			,     max=1.0   )
								# Mask weights with computed mask
								_UpperCamelCase											=   mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(A_			,     A_			,     self.bias   )



