



"""simple docstring"""


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


snake_case_         = logging.get_logger(__name__)

snake_case_         = {
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}







class    A_    (  UpperCAmelCase_       ):



      """simple docstring"""
      __UpperCamelCase	       =							"""layoutlm"""






      def __init__(      self				:Union[str, Any]     ,						lowercase_				:Optional[int]=3_05_22     ,						lowercase_				:Optional[Any]=7_68     ,						lowercase_				:List[Any]=12     ,						lowercase_				:List[str]=12     ,						lowercase_				:Dict=30_72     ,						lowercase_				:Tuple="gelu"     ,						lowercase_				:int=0.1     ,						lowercase_				:Tuple=0.1     ,						lowercase_				:List[str]=5_12     ,						lowercase_				:str=2     ,						lowercase_				:List[Any]=0.02     ,						lowercase_				:Optional[Any]=1E-12     ,						lowercase_				:Optional[int]=0     ,						lowercase_				:Tuple="absolute"     ,						lowercase_				:Tuple=True     ,						lowercase_				:Any=10_24     ,						**lowercase_				:Optional[int]     ,						)       ->    Dict:
            super().__init__(pad_token_id=lowercase_     ,						**lowercase_      )
            UpperCAmelCase    =				vocab_size
            UpperCAmelCase    =				hidden_size
            UpperCAmelCase    =				num_hidden_layers
            UpperCAmelCase    =				num_attention_heads
            UpperCAmelCase    =				hidden_act
            UpperCAmelCase    =				intermediate_size
            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    =				position_embedding_type
            UpperCAmelCase    =				use_cache
            UpperCAmelCase    =				max_ad_position_embeddings







class    A_    (  UpperCAmelCase_       ):



      """simple docstring"""






      def __init__(      self				:Dict     ,						lowercase_				:Optional[int]     ,						lowercase_				:Any = "default"     ,						lowercase_				:List[Any] = None     ,						)       ->    Optional[Any]:
            super().__init__(lowercase_     ,						task=lowercase_     ,						patching_specs=lowercase_      )
            UpperCAmelCase    =				config.max_ad_position_embeddings - 1






      @property
      def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'sequence'}),
                    ('bbox', {0: 'batch', 1: 'sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'sequence'}),
                    ('token_type_ids', {0: 'batch', 1: 'sequence'}),
                ]      )






      def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:Optional[Any]     ,						lowercase_				:str = -1     ,						lowercase_				:Any = -1     ,						lowercase_				:List[Any] = False     ,						lowercase_				:Any = None     ,						)       ->    Mapping[str, Any]:
            UpperCAmelCase    =				super().generate_dummy_inputs(
                lowercase_     ,						batch_size=lowercase_     ,						seq_length=lowercase_     ,						is_pair=lowercase_     ,						framework=lowercase_      )

            # Generate a dummy bbox
            UpperCAmelCase    =				[48, 84, 73, 1_28]

            if not framework == TensorType.PYTORCH:
                  raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'      )

            if not is_torch_available():
                  raise ValueError('Cannot generate dummy inputs without PyTorch installed.'      )
            import torch

            UpperCAmelCase   ,					UpperCAmelCase    =				input_dict['input_ids'].shape
            UpperCAmelCase    =				torch.tensor([*[box] * seq_length]      ).tile(lowercase_     ,						1     ,						1      )
            return input_dict










"""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				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												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 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												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 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												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(lowercase_     ,						lowercase_     ,						self.bias      )





