text: # coding=utf-8
  # Copyright 2021 The UCLA NLP Authors and The HuggingFace Inc. team. All rights reserved.
  #
  # 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 VisualBERT model. """
  
  
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple
  
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss, KLDivLoss, LogSoftmax
  
  from ...activations import ACT2FN
  from ...file_utils import (
      ModelOutput,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import (
      BaseModelOutput,
      BaseModelOutputWithPooling,
      MultipleChoiceModelOutput,
      SequenceClassifierOutput,
  )
  from ...modeling_utils import (
      PreTrainedModel,
      apply_chunking_to_forward,
      find_pruneable_heads_and_indices,
      prune_linear_layer,
  )
  from ...utils import logging
  from .configuration_visual_bert import VisualBertConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "VisualBertConfig"
  
  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "uclanlp/visualbert-vqa",
      "uclanlp/visualbert-vqa-pre",
      "uclanlp/visualbert-vqa-coco-pre",
      "uclanlp/visualbert-vcr",
      "uclanlp/visualbert-vcr-pre",
      "uclanlp/visualbert-vcr-coco-pre",
      "uclanlp/visualbert-nlvr2",
      "uclanlp/visualbert-nlvr2-pre",
      "uclanlp/visualbert-nlvr2-coco-pre"
      # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
  ]
  
  
  class VisualBertEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings and visual embeddings."""
  
      def __init__(self, config):
          super().__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.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
  
          # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
          # any TensorFlow checkpoint file
  
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
          # position_ids (1, len position emb) is contiguous in memory and exported when serialized
          self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
  
          # For Visual Features
          # Token type and position embedding for image features
          self.visual_token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
          self.visual_position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
  
          if config.special_visual_initialize:
              self.visual_token_type_embeddings.weight.data = torch.nn.Parameter(
                  self.token_type_embeddings.weight.data.clone(), requires_grad=True
              )
              self.visual_position_embeddings.weight.data = torch.nn.Parameter(
                  self.position_embeddings.weight.data.clone(), requires_grad=True
              )
  
          self.visual_projection = nn.Linear(config.visual_embedding_dim, config.hidden_size)
  
      def forward(
          self,
          input_ids=None,
          token_type_ids=None,
          position_ids=None,
          inputs_embeds=None,
          visual_embeds=None,
          visual_token_type_ids=None,
          image_text_alignment=None,
      ):
          if input_ids is not None:
              input_shape = input_ids.size()
          else:
              input_shape = inputs_embeds.size()[:-1]
  
          seq_length = input_shape[1]
  
          if position_ids is None:
              position_ids = self.position_ids[:, :seq_length]
  
          if inputs_embeds is None:
              inputs_embeds = self.word_embeddings(input_ids)
  
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.input_embeds.device)
  
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = inputs_embeds + token_type_embeddings
  
          # Absolute Position Embeddings
          position_embeddings = self.position_embeddings(position_ids)
          embeddings += position_embeddings
  
          if visual_embeds is not None:
              if visual_token_type_ids is None:
                  visual_token_type_ids = torch.ones(
                      visual_embeds.size()[:-1], dtype=torch.long, device=self.position_ids.device
                  )
  
              visual_embeds = self.visual_projection(visual_embeds)
              visual_token_type_embeddings = self.visual_token_type_embeddings(visual_token_type_ids)
  
              if image_text_alignment is not None:
                  # image_text_alignment = Batch x image_length x alignment_number.
                  # Each element denotes the position of the word corresponding to the image feature. -1 is the padding value.
  
                  dtype = token_type_embeddings.dtype
                  image_text_alignment_mask = (image_text_alignment != -1).long()
                  # Get rid of the -1.
                  image_text_alignment = image_text_alignment_mask * image_text_alignment
  
                  # Batch x image_length x alignment length x dim
                  visual_position_embeddings = self.position_embeddings(image_text_alignment)
                  visual_position_embeddings *= image_text_alignment_mask.to(dtype=dtype).unsqueeze(-1)
                  visual_position_embeddings = visual_position_embeddings.sum(2)
  
                  # We want to averge along the alignment_number dimension.
                  image_text_alignment_mask = image_text_alignment_mask.to(dtype=dtype).sum(2)
  
                  if (image_text_alignment_mask == 0).sum() != 0:
                      image_text_alignment_mask[image_text_alignment_mask == 0] = 1  # Avoid divide by zero error
                      logger.warning(
                          "Found 0 values in `image_text_alignment_mask`. Setting them to 1 to avoid divide-by-zero error."
                      )
                  visual_position_embeddings = visual_position_embeddings / image_text_alignment_mask.unsqueeze(-1)
  
                  visual_position_ids = torch.zeros(
                      *visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device
                  )
  
                  # When fine-tuning the detector , the image_text_alignment is sometimes padded too long.
                  if visual_position_embeddings.size(1) != visual_embeds.size(1):
                      if visual_position_embeddings.size(1) < visual_embeds.size(1):
                          raise ValueError(
                              f"Visual position embeddings length: {visual_position_embeddings.size(1)}"
                              f"should be the same as `visual_embeds` length: {visual_embeds.size(1)}"
                          )
                      visual_position_embeddings = visual_position_embeddings[:, : visual_embeds.size(1), :]
  
                  visual_position_embeddings = visual_position_embeddings + self.visual_position_embeddings(
                      visual_position_ids
                  )
              else:
                  visual_position_ids = torch.zeros(
                      *visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device
                  )
                  visual_position_embeddings = self.visual_position_embeddings(visual_position_ids)
  
              visual_embeddings = visual_embeds + visual_position_embeddings + visual_token_type_embeddings
  
              embeddings = torch.cat((embeddings, visual_embeddings), dim=1)
  
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  class VisualBertSelfAttention(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.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,
          output_attentions=False,
      ):
          mixed_query_layer = self.query(hidden_states)
  
          key_layer = self.transpose_for_scores(self.key(hidden_states))
          value_layer = self.transpose_for_scores(self.value(hidden_states))
  
          query_layer = self.transpose_for_scores(mixed_query_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 VisualBertSelfAttentionModel 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->VisualBert
  class VisualBertSelfOutput(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
  
  
  class VisualBertAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.self = VisualBertSelfAttention(config)
          self.output = VisualBertSelfOutput(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,
          output_attentions=False,
      ):
          self_outputs = self.self(
              hidden_states,
              attention_mask,
              head_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 with Bert->VisualBert
  class VisualBertIntermediate(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->VisualBert
  class VisualBertOutput(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
  
  
  class VisualBertLayer(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 = VisualBertAttention(config)
          self.intermediate = VisualBertIntermediate(config)
          self.output = VisualBertOutput(config)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_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
  
          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
  
  
  class VisualBertEncoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.layer = nn.ModuleList([VisualBertLayer(config) for _ in range(config.num_hidden_layers)])
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_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
  
          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) and self.training:
  
                  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,
                  )
              else:
                  layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
  
              hidden_states = layer_outputs[0]
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
  
          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,
                  ]
                  if v is not None
              )
          return BaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions
          )
  
  
  # Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->VisualBert
  class VisualBertPooler(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->VisualBert
  class VisualBertPredictionHeadTransform(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->VisualBert
  class VisualBertLMPredictionHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.transform = VisualBertPredictionHeadTransform(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.BertPreTrainingHeads with Bert->VisualBert
  class VisualBertPreTrainingHeads(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.predictions = VisualBertLMPredictionHead(config)
          self.seq_relationship = nn.Linear(config.hidden_size, 2)
  
      def forward(self, sequence_output, pooled_output):
          prediction_scores = self.predictions(sequence_output)
          seq_relationship_score = self.seq_relationship(pooled_output)
          return prediction_scores, seq_relationship_score
  
  
  class VisualBertPreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = VisualBertConfig
      base_model_prefix = "visual_bert"
      _keys_to_ignore_on_load_missing = [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, nn.LayerNorm):
              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_()
  
  
  @dataclass
  class VisualBertForPreTrainingOutput(ModelOutput):
      """
      Output type of :class:`~transformers.VisualBertForPreTraining`.
  
      Args:
          loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
              Total loss as the sum of the masked language modeling loss and the sentence-image prediction
              (classification) loss.
          prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
              Prediction scores of the sentence-image prediction (classification) head (scores of True/False continuation
              before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[torch.FloatTensor] = None
      prediction_logits: torch.FloatTensor = None
      seq_relationship_logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  VISUAL_BERT_START_DOCSTRING = r"""
      This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
      methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
      pruning heads etc.)
  
      This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
      subclass. 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.VisualBertConfig`): 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.
  """
  
  VISUAL_BERT_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.BertTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-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 tokens that are **masked**.
  
              `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]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, 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 :obj:`input_ids` indices into associated
              vectors than the model's internal embedding lookup matrix.
  
          visual_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, visual_seq_length, visual_embedding_dim)`, `optional`):
              The embedded representation of the visual inputs, generally derived using using an object detector.
  
          visual_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, visual_seq_length)`, `optional`):
              Mask to avoid performing attention on visual embeddings. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          visual_token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, visual_seq_length)`, `optional`):
              Segment token indices to indicate different portions of the visual embeds.
  
              `What are token type IDs? <../glossary.html#token-type-ids>`_ The authors of VisualBERT set the
              `visual_token_type_ids` to `1` for all tokens.
  
          image_text_alignment (:obj:`torch.LongTensor` of shape :obj:`(batch_size, visual_seq_length, alignment_number)`, `optional`):
              Image-Text alignment uses to decide the position IDs of the visual embeddings.
  
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  @add_start_docstrings(
      "The bare VisualBert Model transformer outputting raw hidden-states without any specific head on top.",
      VISUAL_BERT_START_DOCSTRING,
  )
  class VisualBertModel(VisualBertPreTrainedModel):
      """
  
      The model can behave as an encoder (with only self-attention) following the architecture described in `Attention is
      all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
      Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
      """
  
      def __init__(self, config, add_pooling_layer=True):
          super().__init__(config)
          self.config = config
  
          self.embeddings = VisualBertEmbeddings(config)
          self.encoder = VisualBertEncoder(config)
  
          self.pooler = VisualBertPooler(config) if add_pooling_layer else None
  
          self.bypass_transformer = config.bypass_transformer
  
          if self.bypass_transformer:
              self.additional_layer = VisualBertLayer(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(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          visual_embeds=None,
          visual_attention_mask=None,
          visual_token_type_ids=None,
          image_text_alignment=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
  
          Returns:
  
          Example::
  
              >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image.
              >>> from transformers import BertTokenizer, VisualBertModel
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = VisualBertModel.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
  
              >>> inputs = tokenizer("The capital of France is Paris.", return_tensors="pt")
              >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
              >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) #example
              >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
  
              >>> inputs.update({{
              ...     "visual_embeds": visual_embeds,
              ...     "visual_token_type_ids": visual_token_type_ids,
              ...     "visual_attention_mask": visual_attention_mask
              ... }})
  
              >>> outputs = model(**inputs)
  
              >>> last_hidden_states = outputs.last_hidden_state
          """
  
          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()
              batch_size, seq_length = input_shape
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
              batch_size, seq_length = input_shape
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if visual_embeds is None:
              raise ValueError(
                  f"`visual_embeds` can not be of type {type(visual_embeds)} when using a VisualBert Model."
              )
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          visual_input_shape = visual_embeds.size()[:-1]
  
          if attention_mask is None:
              attention_mask = torch.ones(input_shape, device=device)
  
          if visual_attention_mask is None:
              visual_attention_mask = torch.ones(visual_input_shape, device=device)
  
          # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
          # ourselves in which case we just need to make it broadcastable to all heads.
  
          combined_attention_mask = torch.cat((attention_mask, visual_attention_mask), dim=-1)
          extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
              combined_attention_mask, [batch_size, input_shape + visual_input_shape], device
          )
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
          head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
  
          embedding_output = self.embeddings(
              input_ids=input_ids,
              position_ids=position_ids,
              token_type_ids=token_type_ids,
              inputs_embeds=inputs_embeds,
              visual_embeds=visual_embeds,
              visual_token_type_ids=visual_token_type_ids,
              image_text_alignment=image_text_alignment,
          )
  
          if self.bypass_transformer and visual_embeds is not None:
              text_length = input_ids.size(1)
              text_embedding_output = embedding_output[:, :text_length, :]
              visual_embedding_output = embedding_output[:, text_length:, :]
  
              text_extended_attention_mask = extended_attention_mask[:, :, text_length, :text_length]
  
              encoded_outputs = self.encoder(
                  text_embedding_output,
                  attention_mask=text_extended_attention_mask,
                  output_attentions=output_attentions,
                  output_hidden_states=output_hidden_states,
                  return_dict=return_dict,
              )
              sequence_output = encoded_outputs[0]
              concatenated_input = torch.cat((sequence_output, visual_embedding_output), dim=1)
              sequence_output = self.additional_layer(concatenated_input, extended_attention_mask)
              pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
  
          else:
              encoder_outputs = self.encoder(
                  embedding_output,
                  attention_mask=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 self.pooler is not None else None
  
          if not return_dict:
              return (sequence_output, pooled_output) + encoder_outputs[1:]
  
          return BaseModelOutputWithPooling(
              last_hidden_state=sequence_output,
              pooler_output=pooled_output,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      VisualBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
      `sentence-image prediction (classification)` head.
      """,
      VISUAL_BERT_START_DOCSTRING,
  )
  class VisualBertForPreTraining(VisualBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.visual_bert = VisualBertModel(config)
          self.cls = VisualBertPreTrainingHeads(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.cls.predictions.decoder
  
      def set_output_embeddings(self, new_embeddings):
          self.cls.predictions.decoder = new_embeddings
  
      @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          visual_embeds=None,
          visual_attention_mask=None,
          visual_token_type_ids=None,
          image_text_alignment=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          sentence_image_labels=None,
      ):
          r"""
              labels (:obj:`torch.LongTensor` of shape ``(batch_size, total_sequence_length)``, `optional`):
                  Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
                  config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
                  (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
              sentence_image_labels (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
                  Labels for computing the sentence-image prediction (classification) loss. Input should be a sequence
                  pair (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
  
                  - 0 indicates sequence B is a matching pair of sequence A for the given image,
                  - 1 indicates sequence B is a random sequence w.r.t A for the given image.
  
          Returns:
  
          Example::
  
              >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
              >>> from transformers import BertTokenizer, VisualBertForPreTraining
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = VisualBertForPreTraining.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
  
              >>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
              >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
              >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) #example
              >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
  
              >>> inputs.update({{
              ...     "visual_embeds": visual_embeds,
              ...     "visual_token_type_ids": visual_token_type_ids,
              ...     "visual_attention_mask": visual_attention_mask
              ... }})
              >>> max_length  = inputs["input_ids"].shape[-1]+visual_embeds.shape[-2]
              >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt", padding="max_length", max_length=max_length)["input_ids"]
              >>> sentence_image_labels = torch.tensor(1).unsqueeze(0) # Batch_size
  
  
              >>> outputs = model(**inputs, labels=labels, sentence_image_labels=sentence_image_labels)
              >>> loss = outputs.loss
              >>> prediction_logits = outputs.prediction_logits
              >>> seq_relationship_logits = outputs.seq_relationship_logits
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.visual_bert(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              visual_embeds=visual_embeds,
              visual_attention_mask=visual_attention_mask,
              visual_token_type_ids=visual_token_type_ids,
              image_text_alignment=image_text_alignment,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output, pooled_output = outputs[:2]
          prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
  
          total_loss = None
          if labels is not None and sentence_image_labels is not None:
              total_size = attention_mask.size(-1) + visual_attention_mask.size(-1)
              if labels.size(-1) != total_size:
                  raise ValueError(
                      f"The labels provided should have same sequence length as total attention mask."
                      f"Found labels with sequence length {labels.size(-1)}, expected {total_size}."
                  )
  
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
              sentence_image_loss = loss_fct(seq_relationship_score.view(-1, 2), sentence_image_labels.view(-1))
              total_loss = masked_lm_loss + sentence_image_loss
  
          if labels is not None and sentence_image_labels is None:
              total_size = attention_mask.size(-1) + visual_attention_mask.size(-1)
              if labels.size(-1) != total_size:
                  raise ValueError(
                      f"The labels provided should have same sequence length as total attention mask."
                      f"Found labels with sequence length {labels.size(-1)}, expected {total_size}."
                  )
  
              loss_fct = CrossEntropyLoss()
              total_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (prediction_scores, seq_relationship_score) + outputs[2:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return VisualBertForPreTrainingOutput(
              loss=total_loss,
              prediction_logits=prediction_scores,
              seq_relationship_logits=seq_relationship_score,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      VisualBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
      a softmax) e.g. for VCR tasks.
      """,
      VISUAL_BERT_START_DOCSTRING,
  )
  class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.visual_bert = VisualBertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.cls = nn.Linear(config.hidden_size, 1)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(
          VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
      )
      @replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          visual_embeds=None,
          visual_attention_mask=None,
          visual_token_type_ids=None,
          image_text_alignment=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
      ):
          r"""
              labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                  Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
                  num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors.
                  (See :obj:`input_ids` above)
  
          Returns:
  
          Example::
  
              >>> from transformers import BertTokenizer, VisualBertForMultipleChoice
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = VisualBertForMultipleChoice.from_pretrained('uclanlp/visualbert-vcr')
  
              >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
              >>> choice0 = "It is eaten with a fork and a knife."
              >>> choice1 = "It is eaten while held in the hand."
  
              >>> visual_embeds = get_visual_embeddings(image)
              >>> # (batch_size, num_choices, visual_seq_length, visual_embedding_dim)
              >>> visual_embeds = visual_embeds.expand(1, 2, *visual_embeds.shape)
              >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
              >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
  
              >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1
  
              >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
              >>> # batch size is 1
              >>> inputs_dict = {{k: v.unsqueeze(0) for k,v in encoding.items()}}
              >>> inputs_dict.update({{
              ... visual_embeds=visual_embeds,
              ... visual_attention_mask=visual_attention_mask,
              ... visual_token_type_ids=visual_token_type_ids,
              ... labels=labels
              ... }})
              >>> outputs = model(**inputs_dict)
  
              >>> loss = outputs.loss
              >>> logits = outputs.logits
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
  
          input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
          attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
          token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
          position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
          inputs_embeds = (
              inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
              if inputs_embeds is not None
              else None
          )
  
          visual_embeds = (
              visual_embeds.view(-1, visual_embeds.size(-2), visual_embeds.size(-1))
              if visual_embeds is not None
              else None
          )
          visual_attention_mask = (
              visual_attention_mask.view(-1, visual_attention_mask.size(-1))
              if visual_attention_mask is not None
              else None
          )
          visual_token_type_ids = (
              visual_token_type_ids.view(-1, visual_token_type_ids.size(-1))
              if visual_token_type_ids is not None
              else None
          )
  
          outputs = self.visual_bert(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              visual_embeds=visual_embeds,
              visual_attention_mask=visual_attention_mask,
              visual_token_type_ids=visual_token_type_ids,
              image_text_alignment=image_text_alignment,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          _, pooled_output = outputs[0], outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.cls(pooled_output)
          reshaped_logits = logits.view(-1, num_choices)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels)
  
          if not return_dict:
              output = (reshaped_logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return MultipleChoiceModelOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      VisualBert Model with a classification/regression head on top (a dropout and a linear layer on top of the pooled
      output) for VQA.
      """,
      VISUAL_BERT_START_DOCSTRING,
  )
  class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.visual_bert = VisualBertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.cls = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          visual_embeds=None,
          visual_attention_mask=None,
          visual_token_type_ids=None,
          image_text_alignment=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
      ):
          r"""
              labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, total_sequence_length)`, `optional`):
                  Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
                  config.num_labels - 1]`. A KLDivLoss is computed between the labels and the returned logits.
  
          Returns:
  
          Example::
  
              >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
              >>> from transformers import BertTokenizer, VisualBertForQuestionAnswering
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = VisualBertForQuestionAnswering.from_pretrained('uclanlp/visualbert-vqa')
  
              >>> text = "Who is eating the apple?"
              >>> inputs = tokenizer(text, return_tensors='pt')
              >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
              >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) #example
              >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
  
              >>> inputs.update({{
              ...     "visual_embeds": visual_embeds,
              ...     "visual_token_type_ids": visual_token_type_ids,
              ...     "visual_attention_mask": visual_attention_mask
              ... }})
  
              >>> labels = torch.tensor([[0.0,1.0]]).unsqueeze(0)  # Batch size 1, Num labels 2
  
              >>> outputs = model(**inputs, labels=labels)
              >>> loss = outputs.loss
              >>> scores = outputs.logits
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # Get the index of the last text token
          index_to_gather = attention_mask.sum(1) - 2  # as in original code
  
          outputs = self.visual_bert(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              visual_embeds=visual_embeds,
              visual_attention_mask=visual_attention_mask,
              visual_token_type_ids=visual_token_type_ids,
              image_text_alignment=image_text_alignment,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
  
          # TO-CHECK: From the original code
          index_to_gather = (
              index_to_gather.unsqueeze(-1).unsqueeze(-1).expand(index_to_gather.size(0), 1, sequence_output.size(-1))
          )
          pooled_output = torch.gather(sequence_output, 1, index_to_gather)
  
          pooled_output = self.dropout(pooled_output)
          logits = self.cls(pooled_output)
          reshaped_logits = logits.view(-1, self.num_labels)
  
          loss = None
          if labels is not None:
              loss_fct = torch.nn.KLDivLoss(reduction="batchmean")
              log_softmax = torch.nn.LogSoftmax(dim=-1)
              reshaped_logits = log_softmax(reshaped_logits)
              loss = loss_fct(reshaped_logits, labels.contiguous())
          if not return_dict:
              output = (reshaped_logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      VisualBert Model with a sequence classification head on top (a dropout and a linear layer on top of the pooled
      output) for Visual Reasoning e.g. for NLVR task.
      """,
      VISUAL_BERT_START_DOCSTRING,
  )
  class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.visual_bert = VisualBertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.cls = nn.Linear(config.hidden_size, config.num_labels)  # 2
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          visual_embeds=None,
          visual_attention_mask=None,
          visual_token_type_ids=None,
          image_text_alignment=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
      ):
          r"""
              labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                  Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
                  config.num_labels - 1]`. A classification loss is computed (Cross-Entropy) against these labels.
  
          Returns:
  
          Example::
  
              >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
              >>> from transformers import BertTokenizer, VisualBertForVisualReasoning
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = VisualBertForVisualReasoning.from_pretrained('uclanlp/visualbert-nlvr2')
  
              >>> text = "Who is eating the apple?"
              >>> inputs = tokenizer(text, return_tensors='pt')
              >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
              >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) #example
              >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
  
              >>> inputs.update({{
              ...     "visual_embeds": visual_embeds,
              ...     "visual_token_type_ids": visual_token_type_ids,
              ...     "visual_attention_mask": visual_attention_mask
              ... }})
  
              >>> labels = torch.tensor(1).unsqueeze(0)  # Batch size 1, Num choices 2
  
              >>> outputs = model(**inputs, labels=labels)
              >>> loss = outputs.loss
              >>> scores = outputs.logits
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.visual_bert(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              visual_embeds=visual_embeds,
              visual_attention_mask=visual_attention_mask,
              visual_token_type_ids=visual_token_type_ids,
              image_text_alignment=image_text_alignment,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          # sequence_output = outputs[0]
          pooled_output = outputs[1]
          pooled_output = self.dropout(pooled_output)
          logits = self.cls(pooled_output)
          reshaped_logits = logits.contiguous()
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  class VisualBertRegionToPhraseAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0:
              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.num_attention_heads = 1  # 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, query, key, attention_mask):
          attention_mask = attention_mask.to(query.dtype)
          attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
          attention_mask = (1.0 - attention_mask) * -10000.0
  
          mixed_query_layer = self.query(query)
          mixed_key_layer = self.key(key)
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
          key_layer = self.transpose_for_scores(mixed_key_layer)
  
          attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
  
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
  
          attention_scores = attention_scores + attention_mask
  
          attention_scores = attention_scores.squeeze(1)
          return attention_scores
  
  
  @add_start_docstrings(
      """
      VisualBert Model with a Masked Language Modeling head and an attention layer on top for Region-to-Phrase Alignment
      e.g. for Flickr30 Entities task.
      """,
      VISUAL_BERT_START_DOCSTRING,
  )
  class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.visual_bert = VisualBertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.cls = VisualBertPreTrainingHeads(config)
          self.attention = VisualBertRegionToPhraseAttention(config)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          visual_embeds=None,
          visual_attention_mask=None,
          visual_token_type_ids=None,
          image_text_alignment=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          region_to_phrase_position=None,
          labels=None,
      ):
          r"""
              region_to_phrase_position (:obj:`torch.LongTensor` of shape ``(batch_size, total_sequence_length)``, `optional`):
                  The positions depicting the position of the image embedding corresponding to the textual tokens.
  
              labels (:obj:`torch.LongTensor` of shape ``(batch_size, total_sequence_length, visual_sequence_length)``, `optional`):
                  Labels for computing the masked language modeling loss. KLDivLoss is computed against these labels and
                  the outputs from the attention layer.
  
          Returns:
  
          Example::
  
              >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
              >>> from transformers import BertTokenizer, VisualBertForRegionToPhraseAlignment
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = VisualBertForRegionToPhraseAlignment.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
  
              >>> text = "Who is eating the apple?"
              >>> inputs = tokenizer(text, return_tensors='pt')
              >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
              >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) #example
              >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
              >>> region_to_phrase_position = torch.ones((1, inputs["input_ids"].shape[-1]+visual_embeds.shape[-2]))
  
              >>> inputs.update({{
              ...     "region_to_phrase_position": region_to_phrase_position,
              ...     "visual_embeds": visual_embeds,
              ...     "visual_token_type_ids": visual_token_type_ids,
              ...     "visual_attention_mask": visual_attention_mask
              ... }})
  
              >>> labels = torch.ones((1, inputs["input_ids"].shape[-1]+visual_embeds.shape[-2], visual_embeds.shape[-2])) # Batch size 1
  
              >>> outputs = model(**inputs, labels=labels)
              >>> loss = outputs.loss
              >>> scores = outputs.logits
          """
          if region_to_phrase_position is None:
              raise ValueError("`region_to_phrase_position` should not be None when using Flickr Model.")
  
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.visual_bert(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              visual_embeds=visual_embeds,
              visual_attention_mask=visual_attention_mask,
              visual_token_type_ids=visual_token_type_ids,
              image_text_alignment=image_text_alignment,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
  
          region_to_phrase_position_mask = (region_to_phrase_position != -1).long()
  
          # Make the -1 become 0
          region_to_phrase_position = region_to_phrase_position * region_to_phrase_position_mask
  
          # Selected_positions = batch x selected position x dim
          expanded_region_to_phrase_positions = region_to_phrase_position.unsqueeze(2).expand(
              region_to_phrase_position.size(0), region_to_phrase_position.size(1), sequence_output.size(2)
          )
          selected_positions = sequence_output.gather(1, expanded_region_to_phrase_positions)
  
          # Visual Features = batch x visual_feature_length x dim
          # This will need separate image and visual masks.
          visual_features = sequence_output[:, attention_mask.size(1) :]
  
          if visual_features.size(1) != visual_attention_mask.size(1):
              raise ValueError(
                  f"Visual features length :{visual_features.size(1)} should be the same"
                  f" as visual attention mask length: {visual_attention_mask.size(1)}."
              )
  
          logits = self.attention(selected_positions, visual_features, visual_attention_mask)
  
          loss = None
  
          if labels is not None:
  
              # scores = batch x selected position x visual_feature
              # scores = selected_positions.bmm(visual_features.transpose(1,2))
              # label = batch x selected_postion x needed position
              loss_fct = KLDivLoss(reduction="batchmean")
              log_softmax = LogSoftmax(dim=-1)
              scores = log_softmax(logits)
              labels = labels.contiguous()
              loss = loss_fct(scores, labels)
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  