content: # PREVALENT, 2020, weituo.hao@duke.edu
  # Modified in Recurrent VLN-BERT, 2020, Yicong.Hong@anu.edu.au
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import json
  import logging
  import math
  import os
  import sys
  from io import open
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from pytorch_transformers import BertPreTrainedModel, BertConfig
  import pdb
  
  logger = logging.getLogger(__name__)
  
  def gelu(x):
      """Implementation of the gelu activation function.
          For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
          0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
          Also see https://arxiv.org/abs/1606.08415
      """
      return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
  
  
  def swish(x):
      return x * torch.sigmoid(x)
  
  
  ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
  
  
  try:
      from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
  except (ImportError, AttributeError) as e:
      logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
  BertLayerNorm = torch.nn.LayerNorm
  
  class BertEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings.
      """
      def __init__(self, config):
          super(BertEmbeddings, self).__init__()
          self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
          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 = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, input_ids, token_type_ids=None, position_ids=None):
          seq_length = input_ids.size(1)
          if position_ids is None:
              position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
              position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
          if token_type_ids is None:
              token_type_ids = torch.zeros_like(input_ids)
  
          words_embeddings = self.word_embeddings(input_ids)
          position_embeddings = self.position_embeddings(position_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = words_embeddings + position_embeddings + token_type_embeddings
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  class BertSelfAttention(nn.Module):
      def __init__(self, config):
          super(BertSelfAttention, self).__init__()
          if config.hidden_size % config.num_attention_heads != 0:
              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.output_attentions = True
  
          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, head_mask=None):
          mixed_query_layer = self.query(hidden_states)
          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)
          # Apply the attention mask is (precomputed for all layers in BertModel 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_scores) if self.output_attentions else (context_layer,)
          return outputs
  
  
  class BertSelfOutput(nn.Module):
      def __init__(self, config):
          super(BertSelfOutput, self).__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = BertLayerNorm(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 BertAttention(nn.Module):
      def __init__(self, config):
          super(BertAttention, self).__init__()
          self.self = BertSelfAttention(config)
          self.output = BertSelfOutput(config)
  
      def forward(self, input_tensor, attention_mask, head_mask=None):
          self_outputs = self.self(input_tensor, attention_mask, head_mask)
          attention_output = self.output(self_outputs[0], input_tensor)
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
          return outputs
  
  
  class BertIntermediate(nn.Module):
      def __init__(self, config):
          super(BertIntermediate, self).__init__()
          self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
          if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
              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
  
  
  class BertOutput(nn.Module):
      def __init__(self, config):
          super(BertOutput, self).__init__()
          self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
          self.LayerNorm = BertLayerNorm(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 BertLayer(nn.Module):
      def __init__(self, config):
          super(BertLayer, self).__init__()
          self.attention = BertAttention(config)
          self.intermediate = BertIntermediate(config)
          self.output = BertOutput(config)
  
      def forward(self, hidden_states, attention_mask, head_mask=None):
          attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
          attention_output = attention_outputs[0]
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.output(intermediate_output, attention_output)
          outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
          return outputs
  
  
  class BertPooler(nn.Module):
      def __init__(self, config):
          super(BertPooler, self).__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
  
  
  class BertXAttention(nn.Module):
      def __init__(self, config, ctx_dim=None):
          super().__init__()
          self.att = BertOutAttention(config, ctx_dim=ctx_dim)
          self.output = BertSelfOutput(config)
  
      def forward(self, input_tensor, ctx_tensor, ctx_att_mask=None):
          output, attention_scores = self.att(input_tensor, ctx_tensor, ctx_att_mask)
          attention_output = self.output(output, input_tensor)
          return attention_output, attention_scores
  
  
  class BertOutAttention(nn.Module):
      def __init__(self, config, ctx_dim=None):
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0:
              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
  
          # visual_dim = 2048
          if ctx_dim is None:
              ctx_dim =config.hidden_size
          self.query = nn.Linear(config.hidden_size, self.all_head_size)
          self.key = nn.Linear(ctx_dim, self.all_head_size)
          self.value = nn.Linear(ctx_dim, 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, context, attention_mask=None):
          mixed_query_layer = self.query(hidden_states)
          mixed_key_layer = self.key(context)
          mixed_value_layer = self.value(context)
  
          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)
  
          # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
          if attention_mask is not None:
              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)
  
          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)
          return context_layer, attention_scores
  
  
  class LXRTXLayer(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          # Lang self-att and FFN layer
          self.lang_self_att = BertAttention(config)
          self.lang_inter = BertIntermediate(config)
          self.lang_output = BertOutput(config)
          # Visn self-att and FFN layer
          self.visn_self_att = BertAttention(config)
          self.visn_inter = BertIntermediate(config)
          self.visn_output = BertOutput(config)
          # The cross attention layer
          self.visual_attention = BertXAttention(config)
  
      def cross_att(self, lang_input, lang_attention_mask, visn_input, visn_attention_mask):
          ''' Cross Attention -- cross for vision not for language '''
          visn_att_output, attention_scores = self.visual_attention(visn_input, lang_input, ctx_att_mask=lang_attention_mask)
          return visn_att_output, attention_scores
  
      def self_att(self, visn_input, visn_attention_mask):
          ''' Self Attention -- on visual features with language clues '''
          visn_att_output = self.visn_self_att(visn_input, visn_attention_mask)
          return visn_att_output
  
      def output_fc(self, visn_input):
          ''' Feed forward '''
          visn_inter_output = self.visn_inter(visn_input)
          visn_output = self.visn_output(visn_inter_output, visn_input)
          return visn_output
  
      def forward(self, lang_feats, lang_attention_mask,
                        visn_feats, visn_attention_mask, tdx):
  
          ''' visual self-attention with state '''
          visn_att_output = torch.cat((lang_feats[:, 0:1, :], visn_feats), dim=1)
          state_vis_mask = torch.cat((lang_attention_mask[:,:,:,0:1], visn_attention_mask), dim=-1)
  
          ''' state and vision attend to language '''
          visn_att_output, cross_attention_scores = self.cross_att(lang_feats[:, 1:, :], lang_attention_mask[:, :, :, 1:], visn_att_output, state_vis_mask)
  
          language_attention_scores = cross_attention_scores[:, :, 0, :]
  
          state_visn_att_output = self.self_att(visn_att_output, state_vis_mask)
          state_visn_output = self.output_fc(state_visn_att_output[0])
  
          visn_att_output = state_visn_output[:, 1:, :]
          lang_att_output = torch.cat((state_visn_output[:, 0:1, :], lang_feats[:,1:,:]), dim=1)
  
          visual_attention_scores = state_visn_att_output[1][:, :, 0, 1:]
  
          return lang_att_output, visn_att_output, language_attention_scores, visual_attention_scores
  
  
  class VisionEncoder(nn.Module):
      def __init__(self, vision_size, config):
          super().__init__()
          feat_dim = vision_size
          # Object feature encoding
          self.visn_fc = nn.Linear(feat_dim, config.hidden_size)
          self.visn_layer_norm = BertLayerNorm(config.hidden_size, eps=1e-12)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, visn_input):
          feats = visn_input
          x = self.visn_fc(feats)
          x = self.visn_layer_norm(x)
          output = self.dropout(x)
          return output
  
  
  class VLNBert(BertPreTrainedModel):
      def __init__(self, config):
          super(VLNBert, self).__init__(config)
          self.embeddings = BertEmbeddings(config)
          self.pooler = BertPooler(config)
  
          self.img_dim = config.img_feature_dim            # 2176
          logger.info('VLNBert Image Dimension: {}'.format(self.img_dim))
          self.img_feature_type = config.img_feature_type  # ''
          self.vl_layers = config.vl_layers                # 4
          self.la_layers = config.la_layers                # 9
          self.lalayer = nn.ModuleList(
              [BertLayer(config) for _ in range(self.la_layers)])
          self.addlayer = nn.ModuleList(
              [LXRTXLayer(config) for _ in range(self.vl_layers)])
          # self.vision_encoder = VisionEncoder(self.config.img_feature_dim, self.config)
          # self.apply(self.init_weights)
          self.init_weights()
          # del self.img_dim
          # del self.vision_encoder
          # del self.embeddings
  
      def forward(self, mode, input_ids, token_type_ids=None,
              attention_mask=None, lang_mask=None, vis_mask=None,
              position_ids=None, head_mask=None, img_feats=None):
  
          attention_mask = lang_mask
  
          if token_type_ids is None:
              token_type_ids = torch.zeros_like(input_ids)
  
          extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
  
          extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
  
          head_mask = [None] * self.config.num_hidden_layers
  
          if mode == 'language':
              ''' LXMERT language branch (in VLN only perform this at initialization) '''
              if input_ids.shape[-1] == 768: # rxr instruction
                  text_embeds = input_ids
              else:                          # r2r instruction
                  embedding_output = self.embeddings(input_ids,
                      position_ids=position_ids, token_type_ids=token_type_ids)
                  text_embeds = embedding_output
  
              for layer_module in self.lalayer:
                  temp_output = layer_module(text_embeds, extended_attention_mask)
                  text_embeds = temp_output[0]
  
              sequence_output = text_embeds
              pooled_output = self.pooler(sequence_output)
  
              return pooled_output, sequence_output
  
  
          elif mode == 'visual':
              ''' LXMERT visual branch (no language processing during navigation) '''
              text_embeds = input_ids
              text_mask = extended_attention_mask
  
              # img_embedding_output = self.vision_encoder(img_feats)
              img_embedding_output = img_feats
              img_seq_len = img_feats.shape[1]
              batch_size = text_embeds.size(0)
  
              img_seq_mask = vis_mask
  
              extended_img_mask = img_seq_mask.unsqueeze(1).unsqueeze(2)
              extended_img_mask = extended_img_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
              extended_img_mask = (1.0 - extended_img_mask) * -10000.0
              img_mask = extended_img_mask
  
              lang_output = text_embeds
              visn_output = img_embedding_output
  
              for tdx, layer_module in enumerate(self.addlayer):
                  lang_output, visn_output, language_attention_scores, visual_attention_scores = layer_module(lang_output, text_mask, visn_output, img_mask, tdx)
  
              sequence_output = lang_output
              pooled_output = self.pooler(sequence_output)
  
              visual_action_scores = visual_attention_scores.mean(dim=1)
  
              return pooled_output, visual_action_scores
  