code: # coding=utf-8
  # Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ PyTorch LayoutLM model. """
  
  
  import math
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_outputs import (
      BaseModelOutputWithCrossAttentions,
      BaseModelOutputWithPoolingAndCrossAttentions,
      MaskedLMOutput,
      TokenClassifierOutput,
  )
  from ...modeling_utils import (
      PreTrainedModel,
      apply_chunking_to_forward,
      find_pruneable_heads_and_indices,
      prune_linear_layer,
  )
  from ...utils import logging
  from .configuration_layoutlm import LayoutLMConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "LayoutLMConfig"
  _TOKENIZER_FOR_DOC = "LayoutLMTokenizer"
  
  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "layoutlm-base-uncased",
      "layoutlm-large-uncased",
  ]
  
  
  LayoutLMLayerNorm = torch.nn.LayerNorm
  
  
  class LayoutLMEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config):
          super(LayoutLMEmbeddings, 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.hidden_size)
          self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
          self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
          self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
          self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
  
          self.LayerNorm = LayoutLMLayerNorm(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 forward(
          self,
          input_ids=None,
          bbox=None,
          token_type_ids=None,
          position_ids=None,
          inputs_embeds=None,
      ):
          if input_ids is not None:
              input_shape = input_ids.size()
          else:
              input_shape = inputs_embeds.size()[:-1]
  
          seq_length = input_shape[1]
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          if position_ids is None:
              position_ids = self.position_ids[:, :seq_length]
  
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          if inputs_embeds is None:
              inputs_embeds = self.word_embeddings(input_ids)
  
          words_embeddings = inputs_embeds
          position_embeddings = self.position_embeddings(position_ids)
          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])
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = (
              words_embeddings
              + position_embeddings
              + left_position_embeddings
              + upper_position_embeddings
              + right_position_embeddings
              + lower_position_embeddings
              + h_position_embeddings
              + w_position_embeddings
              + token_type_embeddings
          )
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  # Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->LayoutLM
  class LayoutLMSelfAttention(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(
                  "The hidden size (%d) is not a multiple of the number of attention "
                  "heads (%d)" % (config.hidden_size, config.num_attention_heads)
              )
  
          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.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 forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          output_attentions=False,
      ):
          mixed_query_layer = self.query(hidden_states)
  
          # If this is instantiated as a cross-attention module, the keys
          # and values come from an encoder; the attention mask needs to be
          # such that the encoder's padding tokens are not attended to.
          if encoder_hidden_states is not None:
              mixed_key_layer = self.key(encoder_hidden_states)
              mixed_value_layer = self.value(encoder_hidden_states)
              attention_mask = encoder_attention_mask
          else:
              mixed_key_layer = self.key(hidden_states)
              mixed_value_layer = self.value(hidden_states)
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
          key_layer = self.transpose_for_scores(mixed_key_layer)
          value_layer = self.transpose_for_scores(mixed_value_layer)
  
          # Take the dot product between "query" and "key" to get the raw attention scores.
          attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
          if attention_mask is not None:
              # Apply the attention mask is (precomputed for all layers in LayoutLMModel forward() function)
              attention_scores = attention_scores + attention_mask
  
          # Normalize the attention scores to probabilities.
          attention_probs = nn.Softmax(dim=-1)(attention_scores)
  
          # 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)
  
          # Mask heads if we want to
          if head_mask is not None:
              attention_probs = attention_probs * head_mask
  
          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
  
  
  # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->LayoutLM
  class LayoutLMSelfOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->LayoutLM
  class LayoutLMAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.self = LayoutLMSelfAttention(config)
          self.output = LayoutLMSelfOutput(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,
          output_attentions=False,
      ):
          self_outputs = self.self(
              hidden_states,
              attention_mask,
              head_mask,
              encoder_hidden_states,
              encoder_attention_mask,
              output_attentions,
          )
          attention_output = self.output(self_outputs[0], hidden_states)
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
          return outputs
  
  
  # Copied from transformers.models.bert.modeling_bert.BertIntermediate
  class LayoutLMIntermediate(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
          if isinstance(config.hidden_act, str):
              self.intermediate_act_fn = ACT2FN[config.hidden_act]
          else:
              self.intermediate_act_fn = config.hidden_act
  
      def forward(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.intermediate_act_fn(hidden_states)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM
  class LayoutLMOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->LayoutLM
  class LayoutLMLayer(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 = LayoutLMAttention(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 = LayoutLMAttention(config)
          self.intermediate = LayoutLMIntermediate(config)
          self.output = LayoutLMOutput(config)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          output_attentions=False,
      ):
          self_attention_outputs = self.attention(
              hidden_states,
              attention_mask,
              head_mask,
              output_attentions=output_attentions,
          )
          attention_output = self_attention_outputs[0]
          outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
  
          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_attention_outputs = self.crossattention(
                  attention_output,
                  attention_mask,
                  head_mask,
                  encoder_hidden_states,
                  encoder_attention_mask,
                  output_attentions,
              )
              attention_output = cross_attention_outputs[0]
              outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights
  
          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
          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
  
  
  # Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->LayoutLM
  class LayoutLMEncoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.layer = nn.ModuleList([LayoutLMLayer(config) for _ in range(config.num_hidden_layers)])
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          output_attentions=False,
          output_hidden_states=False,
          return_dict=True,
      ):
          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
          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
  
              if getattr(self.config, "gradient_checkpointing", False):
  
                  def create_custom_forward(module):
                      def custom_forward(*inputs):
                          return module(*inputs, 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,
                  )
              else:
                  layer_outputs = layer_module(
                      hidden_states,
                      attention_mask,
                      layer_head_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      output_attentions,
                  )
              hidden_states = layer_outputs[0]
              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, all_hidden_states, all_self_attentions, all_cross_attentions]
                  if v is not None
              )
          return BaseModelOutputWithCrossAttentions(
              last_hidden_state=hidden_states,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
              cross_attentions=all_cross_attentions,
          )
  
  
  # Copied from transformers.models.bert.modeling_bert.BertPooler
  class LayoutLMPooler(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.activation = nn.Tanh()
  
      def forward(self, hidden_states):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
          first_token_tensor = hidden_states[:, 0]
          pooled_output = self.dense(first_token_tensor)
          pooled_output = self.activation(pooled_output)
          return pooled_output
  
  
  # Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->LayoutLM
  class LayoutLMPredictionHeadTransform(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          if isinstance(config.hidden_act, str):
              self.transform_act_fn = ACT2FN[config.hidden_act]
          else:
              self.transform_act_fn = config.hidden_act
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
      def forward(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.transform_act_fn(hidden_states)
          hidden_states = self.LayerNorm(hidden_states)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->LayoutLM
  class LayoutLMLMPredictionHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.transform = LayoutLMPredictionHeadTransform(config)
  
          # The output weights are the same as the input embeddings, but there is
          # an output-only bias for each token.
          self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
  
          self.bias = nn.Parameter(torch.zeros(config.vocab_size))
  
          # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
          self.decoder.bias = self.bias
  
      def forward(self, hidden_states):
          hidden_states = self.transform(hidden_states)
          hidden_states = self.decoder(hidden_states)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->LayoutLM
  class LayoutLMOnlyMLMHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.predictions = LayoutLMLMPredictionHead(config)
  
      def forward(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
          return prediction_scores
  
  
  class LayoutLMPreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = LayoutLMConfig
      base_model_prefix = "layoutlm"
      authorized_missing_keys = [r"position_ids"]
  
      def _init_weights(self, module):
          """ Initialize the weights """
          if isinstance(module, (nn.Linear, nn.Embedding)):
              # 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)
          elif isinstance(module, LayoutLMLayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
          if isinstance(module, nn.Linear) and module.bias is not None:
              module.bias.data.zero_()
  
  
  LAYOUTLM_START_DOCSTRING = r"""
      The LayoutLM model was proposed in `LayoutLM: Pre-training of Text and Layout for Document Image Understanding
      <https://arxiv.org/abs/1912.13318>`__ by....
  
      This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. Use
      it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
      behavior.
  
      Parameters:
          config (:class:`~transformers.LayoutLMConfig`): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
              weights.
  """
  
  LAYOUTLM_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`transformers.LayoutLMTokenizer`. See
              :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          bbox (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
              Bounding Boxes of each input sequence tokens. Selected in the range ``[0, config.max_2d_position_embeddings
              - 1]``.
  
              `What are bboxes? <../glossary.html#position-ids>`_
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: ``1`` for
              tokens that are NOT MASKED, ``0`` for MASKED tokens.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
              1]``: ``0`` corresponds to a `sentence A` token, ``1`` corresponds to a `sentence B` token
  
              `What are token type IDs? <../glossary.html#token-type-ids>`_
          position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
              config.max_position_embeddings - 1]``.
  
              `What are position IDs? <../glossary.html#position-ids>`_
          head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: :obj:`1`
              indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert `input_ids` indices into associated vectors
              than the model's internal embedding lookup matrix.
          output_attentions (:obj:`bool`, `optional`):
              If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under
              returned tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned
              tensors for more detail.
          return_dict (:obj:`bool`, `optional`):
              If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
              plain tuple.
  """
  
  
  @add_start_docstrings(
      "The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.",
      LAYOUTLM_START_DOCSTRING,
  )
  class LayoutLMModel(LayoutLMPreTrainedModel):
  
      config_class = LayoutLMConfig
      pretrained_model_archive_map = LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST
      base_model_prefix = "layoutlm"
  
      def __init__(self, config):
          super(LayoutLMModel, self).__init__(config)
          self.config = config
  
          self.embeddings = LayoutLMEmbeddings(config)
          self.encoder = LayoutLMEncoder(config)
          self.pooler = LayoutLMPooler(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)
  
      @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="layoutlm-base-uncased",
          output_type=BaseModelOutputWithPoolingAndCrossAttentions,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          bbox=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,
      ):
          """
          input_ids (torch.LongTensor of shape (batch_size, sequence_length)):
              Indices of input sequence tokens in the vocabulary.
          attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional):
              Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: 1 for tokens
              that are NOT MASKED, 0 for MASKED tokens.
          token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
              0 corresponds to a sentence A token, 1 corresponds to a sentence B token
          position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0,
              config.max_position_embeddings - 1].
          head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]: 1 indicates
              the head is not masked, 0 indicates the head is masked.
          inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional):
              Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
              is useful if you want more control over how to convert input_ids indices into associated vectors than the
              model’s internal embedding lookup matrix.
          output_attentions (bool, optional):
              If set to True, the attentions tensors of all attention layers are returned.
          output_hidden_states (bool, optional):
              If set to True, the hidden states of all layers are returned.
          return_dict (bool, optional):
              If set to True, the model will return a ModelOutput instead of a plain tuple.
          """
          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
  
          if attention_mask is None:
              attention_mask = torch.ones(input_shape, device=device)
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          if bbox is None:
              bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
  
          extended_attention_mask = 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
  
          embedding_output = self.embeddings(
              input_ids=input_ids,
              bbox=bbox,
              position_ids=position_ids,
              token_type_ids=token_type_ids,
              inputs_embeds=inputs_embeds,
          )
          encoder_outputs = self.encoder(
              embedding_output,
              extended_attention_mask,
              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,
          )
  
  
  @add_start_docstrings("""LayoutLM Model with a `language modeling` head on top. """, LAYOUTLM_START_DOCSTRING)
  class LayoutLMForMaskedLM(LayoutLMPreTrainedModel):
      config_class = LayoutLMConfig
      pretrained_model_archive_map = LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST
      base_model_prefix = "layoutlm"
  
      def __init__(self, config):
          super().__init__(config)
  
          self.layoutlm = LayoutLMModel(config)
          self.cls = LayoutLMOnlyMLMHead(config)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.layoutlm.embeddings.word_embeddings
  
      def get_output_embeddings(self):
          return self.cls.predictions.decoder
  
      @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="layoutlm-base-uncased",
          output_type=MaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          bbox=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          encoder_hidden_states=None,
          encoder_attention_mask=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.layoutlm(
              input_ids,
              bbox,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
          prediction_scores = self.cls(sequence_output)
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(
                  prediction_scores.view(-1, self.config.vocab_size),
                  labels.view(-1),
              )
  
          if not return_dict:
              output = (prediction_scores,) + outputs[2:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return MaskedLMOutput(
              loss=masked_lm_loss,
              logits=prediction_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
      Named-Entity-Recognition (NER) tasks.
      """,
      LAYOUTLM_START_DOCSTRING,
  )
  class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
      config_class = LayoutLMConfig
      pretrained_model_archive_map = LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST
      base_model_prefix = "layoutlm"
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
          self.layoutlm = LayoutLMModel(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.layoutlm.embeddings.word_embeddings
  
      @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="layoutlm-base-uncased",
          output_type=TokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          bbox=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.layoutlm(
              input_ids=input_ids,
              bbox=bbox,
              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,
          )
  
          sequence_output = outputs[0]
  
          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,
          )
  
apis: torch.ones torch.nn.Linear torch.nn.Softmax torch.nn.Embedding torch.nn.Tanh torch.nn.CrossEntropyLoss torch.nn.LayerNorm torch.arange torch.zeros torch.nn.Dropout torch.matmul