text: # coding=utf-8
  import math
  
  import torch
  import torch.nn.functional as F
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  
  # import detectron2
  # from detectron2.modeling import META_ARCH_REGISTRY
  from transformers import PreTrainedModel
  from transformers.modeling_outputs import (
      BaseModelOutputWithPastAndCrossAttentions,
      BaseModelOutputWithPoolingAndCrossAttentions,
      TokenClassifierOutput,
  )
  from transformers.modeling_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
  from transformers.models.layoutlm.modeling_layoutlm import LayoutLMIntermediate as LayoutLMv2Intermediate
  from transformers.models.layoutlm.modeling_layoutlm import LayoutLMOutput as LayoutLMv2Output
  from transformers.models.layoutlm.modeling_layoutlm import LayoutLMPooler as LayoutLMv2Pooler
  from transformers.models.layoutlm.modeling_layoutlm import LayoutLMSelfOutput as LayoutLMv2SelfOutput
  from transformers.utils import logging
  
  from ...modules.decoders.re import REDecoder
  from ...utils import ReOutput
  from .configuration_layoutlmv2 import LayoutLMv2Config
  from .detectron2_config import add_layoutlmv2_config
  
  
  logger = logging.get_logger(__name__)
  
  LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "layoutlmv2-base-uncased",
      "layoutlmv2-large-uncased",
  ]
  
  
  LayoutLMv2LayerNorm = torch.nn.LayerNorm
  
  
  class LayoutLMv2Embeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config):
          super(LayoutLMv2Embeddings, self).__init__()
          self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
          self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
  
          self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
          self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
          self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
          self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
          self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
  
          self.LayerNorm = LayoutLMv2LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
          self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
  
      def _cal_spatial_position_embeddings(self, bbox):
          try:
              left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
              upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
              right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
              lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
          except IndexError as e:
              raise IndexError("The :obj:`bbox`coordinate values should be within 0-1000 range.") from e
  
          h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
          w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
  
          spatial_position_embeddings = torch.cat(
              [
                  left_position_embeddings,
                  upper_position_embeddings,
                  right_position_embeddings,
                  lower_position_embeddings,
                  h_position_embeddings,
                  w_position_embeddings,
              ],
              dim=-1,
          )
          return spatial_position_embeddings
  
  
  class LayoutLMv2SelfAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
              raise ValueError(
                  f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                  f"heads ({config.num_attention_heads})"
              )
          self.fast_qkv = config.fast_qkv
          self.num_attention_heads = config.num_attention_heads
          self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
  
          self.has_relative_attention_bias = config.has_relative_attention_bias
          self.has_spatial_attention_bias = config.has_spatial_attention_bias
  
          if config.fast_qkv:
              self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False)
              self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
              self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
          else:
              self.query = nn.Linear(config.hidden_size, self.all_head_size)
              self.key = nn.Linear(config.hidden_size, self.all_head_size)
              self.value = nn.Linear(config.hidden_size, self.all_head_size)
  
          self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
  
      def transpose_for_scores(self, x):
          new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
          x = x.view(*new_x_shape)
          return x.permute(0, 2, 1, 3)
  
      def compute_qkv(self, hidden_states):
          if self.fast_qkv:
              qkv = self.qkv_linear(hidden_states)
              q, k, v = torch.chunk(qkv, 3, dim=-1)
              if q.ndimension() == self.q_bias.ndimension():
                  q = q + self.q_bias
                  v = v + self.v_bias
              else:
                  _sz = (1,) * (q.ndimension() - 1) + (-1,)
                  q = q + self.q_bias.view(*_sz)
                  v = v + self.v_bias.view(*_sz)
          else:
              q = self.query(hidden_states)
              k = self.key(hidden_states)
              v = self.value(hidden_states)
          return q, k, v
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
          rel_pos=None,
          rel_2d_pos=None,
      ):
          q, k, v = self.compute_qkv(hidden_states)
  
          # (B, L, H*D) -> (B, H, L, D)
          query_layer = self.transpose_for_scores(q)
          key_layer = self.transpose_for_scores(k)
          value_layer = self.transpose_for_scores(v)
  
          query_layer = query_layer / math.sqrt(self.attention_head_size)
          # [BSZ, NAT, L, L]
          attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
          if self.has_relative_attention_bias:
              attention_scores += rel_pos
          if self.has_spatial_attention_bias:
              attention_scores += rel_2d_pos
          attention_scores = attention_scores.float().masked_fill_(attention_mask.to(torch.bool), float("-inf"))
          attention_probs = F.softmax(attention_scores, dim=-1, dtype=torch.float32).type_as(value_layer)
          # This is actually dropping out entire tokens to attend to, which might
          # seem a bit unusual, but is taken from the original Transformer paper.
          attention_probs = self.dropout(attention_probs)
          context_layer = torch.matmul(attention_probs, value_layer)
          context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
          new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
          context_layer = context_layer.view(*new_context_layer_shape)
  
          outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
          return outputs
  
  
  class LayoutLMv2Attention(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.self = LayoutLMv2SelfAttention(config)
          self.output = LayoutLMv2SelfOutput(config)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          if len(heads) == 0:
              return
          heads, index = find_pruneable_heads_and_indices(
              heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
          )
  
          # Prune linear layers
          self.self.query = prune_linear_layer(self.self.query, index)
          self.self.key = prune_linear_layer(self.self.key, index)
          self.self.value = prune_linear_layer(self.self.value, index)
          self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
  
          # Update hyper params and store pruned heads
          self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
          self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
          rel_pos=None,
          rel_2d_pos=None,
      ):
          self_outputs = self.self(
              hidden_states,
              attention_mask,
              head_mask,
              encoder_hidden_states,
              encoder_attention_mask,
              past_key_value,
              output_attentions,
              rel_pos=rel_pos,
              rel_2d_pos=rel_2d_pos,
          )
          attention_output = self.output(self_outputs[0], hidden_states)
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
          return outputs
  
  
  class LayoutLMv2Layer(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.chunk_size_feed_forward = config.chunk_size_feed_forward
          self.seq_len_dim = 1
          self.attention = LayoutLMv2Attention(config)
          self.is_decoder = config.is_decoder
          self.add_cross_attention = config.add_cross_attention
          if self.add_cross_attention:
              assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
              self.crossattention = LayoutLMv2Attention(config)
          self.intermediate = LayoutLMv2Intermediate(config)
          self.output = LayoutLMv2Output(config)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
          rel_pos=None,
          rel_2d_pos=None,
      ):
          # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
          self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
          self_attention_outputs = self.attention(
              hidden_states,
              attention_mask,
              head_mask,
              output_attentions=output_attentions,
              past_key_value=self_attn_past_key_value,
              rel_pos=rel_pos,
              rel_2d_pos=rel_2d_pos,
          )
          attention_output = self_attention_outputs[0]
  
          # if decoder, the last output is tuple of self-attn cache
          if self.is_decoder:
              outputs = self_attention_outputs[1:-1]
              present_key_value = self_attention_outputs[-1]
          else:
              outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
  
          cross_attn_present_key_value = None
          if self.is_decoder and encoder_hidden_states is not None:
              assert hasattr(
                  self, "crossattention"
              ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
  
              # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
              cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
              cross_attention_outputs = self.crossattention(
                  attention_output,
                  attention_mask,
                  head_mask,
                  encoder_hidden_states,
                  encoder_attention_mask,
                  cross_attn_past_key_value,
                  output_attentions,
              )
              attention_output = cross_attention_outputs[0]
              outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
  
              # add cross-attn cache to positions 3,4 of present_key_value tuple
              cross_attn_present_key_value = cross_attention_outputs[-1]
              present_key_value = present_key_value + cross_attn_present_key_value
  
          layer_output = apply_chunking_to_forward(
              self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
          )
          outputs = (layer_output,) + outputs
  
          # if decoder, return the attn key/values as the last output
          if self.is_decoder:
              outputs = outputs + (present_key_value,)
  
          return outputs
  
      def feed_forward_chunk(self, attention_output):
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.output(intermediate_output, attention_output)
          return layer_output
  
  
  def relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
      ret = 0
      if bidirectional:
          num_buckets //= 2
          ret += (relative_position > 0).long() * num_buckets
          n = torch.abs(relative_position)
      else:
          n = torch.max(-relative_position, torch.zeros_like(relative_position))
      # now n is in the range [0, inf)
  
      # half of the buckets are for exact increments in positions
      max_exact = num_buckets // 2
      is_small = n < max_exact
  
      # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
      val_if_large = max_exact + (
          torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
      ).to(torch.long)
      val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
  
      ret += torch.where(is_small, n, val_if_large)
      return ret
  
  
  class LayoutLMv2Encoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.layer = nn.ModuleList([LayoutLMv2Layer(config) for _ in range(config.num_hidden_layers)])
  
          self.has_relative_attention_bias = config.has_relative_attention_bias
          self.has_spatial_attention_bias = config.has_spatial_attention_bias
  
          if self.has_relative_attention_bias:
              self.rel_pos_bins = config.rel_pos_bins
              self.max_rel_pos = config.max_rel_pos
              self.rel_pos_onehot_size = config.rel_pos_bins
              self.rel_pos_bias = nn.Linear(self.rel_pos_onehot_size, config.num_attention_heads, bias=False)
  
          if self.has_spatial_attention_bias:
              self.max_rel_2d_pos = config.max_rel_2d_pos
              self.rel_2d_pos_bins = config.rel_2d_pos_bins
              self.rel_2d_pos_onehot_size = config.rel_2d_pos_bins
              self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False)
              self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False)
  
      def _cal_1d_pos_emb(self, hidden_states, position_ids):
          rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
          rel_pos = relative_position_bucket(
              rel_pos_mat,
              num_buckets=self.rel_pos_bins,
              max_distance=self.max_rel_pos,
          )
          rel_pos = F.one_hot(rel_pos, num_classes=self.rel_pos_onehot_size).type_as(hidden_states)
          rel_pos = self.rel_pos_bias(rel_pos).permute(0, 3, 1, 2)
          rel_pos = rel_pos.contiguous()
          return rel_pos
  
      def _cal_2d_pos_emb(self, hidden_states, bbox):
          position_coord_x = bbox[:, :, 0]
          position_coord_y = bbox[:, :, 3]
          rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1)
          rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1)
          rel_pos_x = relative_position_bucket(
              rel_pos_x_2d_mat,
              num_buckets=self.rel_2d_pos_bins,
              max_distance=self.max_rel_2d_pos,
          )
          rel_pos_y = relative_position_bucket(
              rel_pos_y_2d_mat,
              num_buckets=self.rel_2d_pos_bins,
              max_distance=self.max_rel_2d_pos,
          )
          rel_pos_x = F.one_hot(rel_pos_x, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states)
          rel_pos_y = F.one_hot(rel_pos_y, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states)
          rel_pos_x = self.rel_pos_x_bias(rel_pos_x).permute(0, 3, 1, 2)
          rel_pos_y = self.rel_pos_y_bias(rel_pos_y).permute(0, 3, 1, 2)
          rel_pos_x = rel_pos_x.contiguous()
          rel_pos_y = rel_pos_y.contiguous()
          rel_2d_pos = rel_pos_x + rel_pos_y
          return rel_2d_pos
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=False,
          output_hidden_states=False,
          return_dict=True,
          bbox=None,
          position_ids=None,
      ):
          all_hidden_states = () if output_hidden_states else None
          all_self_attentions = () if output_attentions else None
          all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
  
          next_decoder_cache = () if use_cache else None
  
          rel_pos = self._cal_1d_pos_emb(hidden_states, position_ids) if self.has_relative_attention_bias else None
          rel_2d_pos = self._cal_2d_pos_emb(hidden_states, bbox) if self.has_spatial_attention_bias else None
  
          for i, layer_module in enumerate(self.layer):
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              layer_head_mask = head_mask[i] if head_mask is not None else None
              past_key_value = past_key_values[i] if past_key_values is not None else None
  
              if getattr(self.config, "gradient_checkpointing", False) and self.training:
  
                  if use_cache:
                      logger.warn(
                          "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
                          "`use_cache=False`..."
                      )
                      use_cache = False
  
                  def create_custom_forward(module):
                      def custom_forward(*inputs):
                          return module(*inputs, past_key_value, output_attentions)
  
                      return custom_forward
  
                  layer_outputs = torch.utils.checkpoint.checkpoint(
                      create_custom_forward(layer_module),
                      hidden_states,
                      attention_mask,
                      layer_head_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      rel_pos=rel_pos,
                      rel_2d_pos=rel_2d_pos,
                  )
              else:
                  layer_outputs = layer_module(
                      hidden_states,
                      attention_mask,
                      layer_head_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      past_key_value,
                      output_attentions,
                      rel_pos=rel_pos,
                      rel_2d_pos=rel_2d_pos,
                  )
  
              hidden_states = layer_outputs[0]
              if use_cache:
                  next_decoder_cache += (layer_outputs[-1],)
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
                  if self.config.add_cross_attention:
                      all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
  
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(
                  v
                  for v in [
                      hidden_states,
                      next_decoder_cache,
                      all_hidden_states,
                      all_self_attentions,
                      all_cross_attentions,
                  ]
                  if v is not None
              )
          return BaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=hidden_states,
              past_key_values=next_decoder_cache,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
              cross_attentions=all_cross_attentions,
          )
  
  
  class LayoutLMv2PreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = LayoutLMv2Config
      pretrained_model_archive_map = LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST
      base_model_prefix = "layoutlmv2"
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      def _init_weights(self, module):
          """Initialize the weights"""
          if isinstance(module, nn.Linear):
              # Slightly different from the TF version which uses truncated_normal for initialization
              # cf https://github.com/pytorch/pytorch/pull/5617
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.Embedding):
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
              if module.padding_idx is not None:
                  module.weight.data[module.padding_idx].zero_()
          elif isinstance(module, LayoutLMv2LayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
  
  
  def my_convert_sync_batchnorm(module, process_group=None):
      # same as `nn.modules.SyncBatchNorm.convert_sync_batchnorm` but allowing converting from `detectron2.layers.FrozenBatchNorm2d`
      if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
          return nn.modules.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
      module_output = module
      # if isinstance(module, detectron2.layers.FrozenBatchNorm2d):
      #     module_output = torch.nn.SyncBatchNorm(
      #         num_features=module.num_features,
      #         eps=module.eps,
      #         affine=True,
      #         track_running_stats=True,
      #         process_group=process_group,
      #     )
      #     module_output.weight = torch.nn.Parameter(module.weight)
      #     module_output.bias = torch.nn.Parameter(module.bias)
      #     module_output.running_mean = module.running_mean
      #     module_output.running_var = module.running_var
      #     module_output.num_batches_tracked = torch.tensor(0, dtype=torch.long, device=module.running_mean.device)
      for name, child in module.named_children():
          module_output.add_module(name, my_convert_sync_batchnorm(child, process_group))
      del module
      return module_output
  
  
  class VisualBackbone(nn.Module):
      def __init__(self, config):
          super().__init__()
          # self.cfg = detectron2.config.get_cfg()
          add_layoutlmv2_config(self.cfg)
          meta_arch = self.cfg.MODEL.META_ARCHITECTURE
          model = META_ARCH_REGISTRY.get(meta_arch)(self.cfg)
          # assert isinstance(model.backbone, detectron2.modeling.backbone.FPN)
          self.backbone = model.backbone
          if (
              config.convert_sync_batchnorm
              and torch.distributed.is_available()
              and torch.distributed.is_initialized()
              and torch.distributed.get_rank() > -1
          ):
              self_rank = torch.distributed.get_rank()
              node_size = torch.cuda.device_count()
              world_size = torch.distributed.get_world_size()
              assert world_size % node_size == 0
  
              node_global_ranks = [
                  list(range(i * node_size, (i + 1) * node_size)) for i in range(world_size // node_size)
              ]
              sync_bn_groups = [
                  torch.distributed.new_group(ranks=node_global_ranks[i]) for i in range(world_size // node_size)
              ]
              node_rank = self_rank // node_size
              assert self_rank in node_global_ranks[node_rank]
  
              self.backbone = my_convert_sync_batchnorm(self.backbone, process_group=sync_bn_groups[node_rank])
  
          assert len(self.cfg.MODEL.PIXEL_MEAN) == len(self.cfg.MODEL.PIXEL_STD)
          num_channels = len(self.cfg.MODEL.PIXEL_MEAN)
          self.register_buffer(
              "pixel_mean",
              torch.Tensor(self.cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1),
          )
          self.register_buffer("pixel_std", torch.Tensor(self.cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1))
          self.out_feature_key = "p2"
          if torch.is_deterministic():
              logger.warning("using `AvgPool2d` instead of `AdaptiveAvgPool2d`")
              input_shape = (224, 224)
              backbone_stride = self.backbone.output_shape()[self.out_feature_key].stride
              self.pool = nn.AvgPool2d(
                  (
                      math.ceil(math.ceil(input_shape[0] / backbone_stride) / config.image_feature_pool_shape[0]),
                      math.ceil(math.ceil(input_shape[1] / backbone_stride) / config.image_feature_pool_shape[1]),
                  )
              )
          else:
              self.pool = nn.AdaptiveAvgPool2d(config.image_feature_pool_shape[:2])
          if len(config.image_feature_pool_shape) == 2:
              config.image_feature_pool_shape.append(self.backbone.output_shape()[self.out_feature_key].channels)
          assert self.backbone.output_shape()[self.out_feature_key].channels == config.image_feature_pool_shape[2]
  
      def forward(self, images):
          images_input = (images.tensor - self.pixel_mean) / self.pixel_std
          features = self.backbone(images_input)
          features = features[self.out_feature_key]
          features = self.pool(features).flatten(start_dim=2).transpose(1, 2).contiguous()
          return features
  
  
  class LayoutLMv2Model(LayoutLMv2PreTrainedModel):
      def __init__(self, config):
          super(LayoutLMv2Model, self).__init__(config)
          self.config = config
          self.has_visual_segment_embedding = config.has_visual_segment_embedding
          self.embeddings = LayoutLMv2Embeddings(config)
  
          # self.visual = VisualBackbone(config)
          # self.visual_proj = nn.Linear(config.image_feature_pool_shape[-1], config.hidden_size)
          # if self.has_visual_segment_embedding:
          #     self.visual_segment_embedding = nn.Parameter(nn.Embedding(1, config.hidden_size).weight[0])
          # self.visual_LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          # self.visual_dropout = nn.Dropout(config.hidden_dropout_prob)
  
          self.encoder = LayoutLMv2Encoder(config)
          self.pooler = LayoutLMv2Pooler(config)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
  
      def _prune_heads(self, heads_to_prune):
          """
          Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
          class PreTrainedModel
          """
          for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(heads)
  
      def _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids):
          seq_length = input_ids.size(1)
          if position_ids is None:
              position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
              position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
          if token_type_ids is None:
              token_type_ids = torch.zeros_like(input_ids)
  
          words_embeddings = self.embeddings.word_embeddings(input_ids)
          position_embeddings = self.embeddings.position_embeddings(position_ids)
          spatial_position_embeddings = self.embeddings._cal_spatial_position_embeddings(bbox)
          token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids)
          embeddings = words_embeddings + position_embeddings + spatial_position_embeddings + token_type_embeddings
          embeddings = self.embeddings.LayerNorm(embeddings)
          embeddings = self.embeddings.dropout(embeddings)
          return embeddings
  
      def _calc_img_embeddings(self, image, bbox, position_ids):
          visual_embeddings = self.visual_proj(self.visual(image))
          position_embeddings = self.embeddings.position_embeddings(position_ids)
          spatial_position_embeddings = self.embeddings._cal_spatial_position_embeddings(bbox)
          embeddings = visual_embeddings + position_embeddings + spatial_position_embeddings
          if self.has_visual_segment_embedding:
              embeddings += self.visual_segment_embedding
          embeddings = self.visual_LayerNorm(embeddings)
          embeddings = self.visual_dropout(embeddings)
          return embeddings
  
      def forward(
          self,
          input_ids=None,
          bbox=None,
          image=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = input_ids.size()
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          visual_shape = list(input_shape)
          visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
          visual_shape = torch.Size(visual_shape)
          final_shape = list(input_shape)
          # final_shape[1] += visual_shape[1]
          final_shape = torch.Size(final_shape)
  
          # visual_bbox_x = (
          #     torch.arange(
          #         0,
          #         1000 * (self.config.image_feature_pool_shape[1] + 1),
          #         1000,
          #         device=device,
          #         dtype=bbox.dtype,
          #     )
          #     // self.config.image_feature_pool_shape[1]
          # )
          # visual_bbox_y = (
          #     torch.arange(
          #         0,
          #         1000 * (self.config.image_feature_pool_shape[0] + 1),
          #         1000,
          #         device=device,
          #         dtype=bbox.dtype,
          #     )
          #     // self.config.image_feature_pool_shape[0]
          # )
          # visual_bbox = torch.stack(
          #     [
          #         visual_bbox_x[:-1].repeat(self.config.image_feature_pool_shape[0], 1),
          #         visual_bbox_y[:-1].repeat(self.config.image_feature_pool_shape[1], 1).transpose(0, 1),
          #         visual_bbox_x[1:].repeat(self.config.image_feature_pool_shape[0], 1),
          #         visual_bbox_y[1:].repeat(self.config.image_feature_pool_shape[1], 1).transpose(0, 1),
          #     ],
          #     dim=-1,
          # ).view(-1, bbox.size(-1))
          # visual_bbox = visual_bbox.repeat(final_shape[0], 1, 1)
          final_bbox = bbox
  
          if attention_mask is None:
              attention_mask = torch.ones(input_shape, device=device)
  
          # visual_attention_mask = torch.ones(visual_shape, device=device)
          final_attention_mask = attention_mask
  
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          if position_ids is None:
              seq_length = input_shape[1]
              position_ids = self.embeddings.position_ids[:, :seq_length]
              position_ids = position_ids.expand_as(input_ids)
  
          # visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=input_ids.device).repeat(
          #     input_shape[0], 1
          # )
          final_position_ids = position_ids
  
          if bbox is None:
              bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
  
          text_layout_emb = self._calc_text_embeddings(
              input_ids=input_ids,
              bbox=bbox,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
          )
  
          # visual_emb = self._calc_img_embeddings(
          #     image=image,
          #     bbox=visual_bbox,
          #     position_ids=visual_position_ids,
          # )
          final_emb = text_layout_emb
  
          extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2)
  
          extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
  
          if head_mask is not None:
              if head_mask.dim() == 1:
                  head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                  head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
              elif head_mask.dim() == 2:
                  head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
              head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
          else:
              head_mask = [None] * self.config.num_hidden_layers
  
          encoder_outputs = self.encoder(
              final_emb,
              extended_attention_mask,
              bbox=final_bbox,
              position_ids=final_position_ids,
              head_mask=head_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          sequence_output = encoder_outputs[0]
          pooled_output = self.pooler(sequence_output)
  
          if not return_dict:
              return (sequence_output, pooled_output) + encoder_outputs[1:]
  
          return BaseModelOutputWithPoolingAndCrossAttentions(
              last_hidden_state=sequence_output,
              pooler_output=pooled_output,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
              cross_attentions=encoder_outputs.cross_attentions,
          )
  
  
  class LayoutLMv2ForTokenClassification(LayoutLMv2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
          self.layoutlmv2 = LayoutLMv2Model(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.layoutlmv2.embeddings.word_embeddings
  
      def forward(
          self,
          input_ids=None,
          bbox=None,
          image=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.layoutlmv2(
              input_ids=input_ids,
              bbox=bbox,
              image=image,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          seq_length = input_ids.size(1)
          sequence_output, image_output = outputs[0][:, :seq_length], outputs[0][:, seq_length:]
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
  
              if attention_mask is not None:
                  active_loss = attention_mask.view(-1) == 1
                  active_logits = logits.view(-1, self.num_labels)[active_loss]
                  active_labels = labels.view(-1)[active_loss]
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return TokenClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  class LayoutLMv2ForRelationExtraction(LayoutLMv2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.layoutlmv2 = LayoutLMv2Model(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.extractor = REDecoder(config)
          self.init_weights()
  
      def forward(
          self,
          input_ids,
          bbox,
          labels=None,
          image=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          entities=None,
          relations=None,
      ):
          outputs = self.layoutlmv2(
              input_ids=input_ids,
              bbox=bbox,
              image=image,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
          )
  
          seq_length = input_ids.size(1)
          sequence_output, image_output = outputs[0][:, :seq_length], outputs[0][:, seq_length:]
          sequence_output = self.dropout(sequence_output)
          loss, pred_relations = self.extractor(sequence_output, entities, relations)
  
          return ReOutput(
              loss=loss,
              entities=entities,
              relations=relations,
              pred_relations=pred_relations,
              hidden_states=outputs[0],
          )
  