text: # adapted from https://github.com/yangheng95/LCF-ABSA
  from argparse import Namespace
  from typing import Dict
  
  import numpy as np
  import torch
  import torch.nn as nn
  from transformers.modeling_bert import BertPooler, BertSelfAttention
  
  from NewsSentiment.consts import *
  from NewsSentiment.dataset import FXDataset
  from NewsSentiment.models.FXBaseModel import FXBaseModel
  
  
  class GlobalContext(nn.Module):
      def __init__(self, global_context_seqs_per_doc):
          super(GlobalContext, self).__init__()
          self.global_context_seqs_per_doc = global_context_seqs_per_doc
  
      def forward(self, inputs):
          pass
  
  
  class SelfAttention(nn.Module):
      def __init__(self, config, opt):
          super(SelfAttention, self).__init__()
          self.opt = opt
          self.config = config
          self.SA = BertSelfAttention(config)
          self.tanh = torch.nn.Tanh()
  
      def forward(self, inputs):
          zero_tensor = torch.tensor(
              np.zeros((inputs.size(0), 1, 1, self.opt.max_seq_len), dtype=np.float32),
              dtype=torch.float32,
          ).to(self.opt.device)
          SA_out = self.SA(inputs, zero_tensor)
          return self.tanh(SA_out[0])
  
  
  class LCF_BERT(FXBaseModel):
      @staticmethod
      def get_language_models():
          return (BERT_BASE_UNCASED,)
  
      @staticmethod
      def get_input_field_ids():
          return [
              (BERT_BASE_UNCASED, FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS),
              (
                  BERT_BASE_UNCASED,
                  FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS_SEGMENT_IDS,
              ),
              (BERT_BASE_UNCASED, FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS),
              (BERT_BASE_UNCASED, FIELD_TARGET_IDS_WITH_SPECIAL_TOKENS),
              (BERT_BASE_UNCASED, FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS_TARGET_MASK),
          ]
  
      def __init__(self, transformer_models: Dict, opt: Namespace):
          super(LCF_BERT, self).__init__()
  
          bert = transformer_models[BERT_BASE_UNCASED]
          self.bert_spc = bert
          self.opt = opt
  
          # self.bert_local = copy.deepcopy(bert)  # Uncomment the line to use dual Bert
          self.bert_local = (
              bert  # Default to use single Bert and reduce memory requirements
          )
          self.dropout = nn.Dropout(self.opt.dropout)
          # while the paper describes 3 self attentions, the original implementation by the authors
          # uses only one. we stick with the original implementation.
          # answer by the author: the version found PyTorch-ABSA repository and below is better than what was
          # described in the paper (cf. https://github.com/yangheng95/LC-ABSA/issues/10#issuecomment-670301603)
          # self.bert_local_SA = SelfAttention(bert.config, self.opt)
          # self.bert_global_SA = SelfAttention(bert.config, self.opt)
          self.linear_double = nn.Linear(
              bert.config.hidden_size * 2, bert.config.hidden_size
          )
          self.bert_SA = SelfAttention(bert.config, self.opt)
          self.linear_single = nn.Linear(bert.config.hidden_size, bert.config.hidden_size)
          self.bert_pooler = BertPooler(bert.config)
  
          self.dense = nn.Linear(bert.config.hidden_size, self.opt.polarities_dim)
  
      def feature_dynamic_mask(self, text_local_indices, aspect_indices):
          texts = text_local_indices.cpu().numpy()
          asps = aspect_indices.cpu().numpy()
          mask_len = self.opt.SRD
          masked_text_raw_indices = np.ones(
              (
                  text_local_indices.size(0),
                  self.opt.max_seq_len,
                  self.bert_local.config.hidden_size,
              ),
              dtype=np.float32,
          )
          for text_i, asp_i in zip(range(len(texts)), range(len(asps))):
              asp_len = np.count_nonzero(asps[asp_i]) - 2
              try:
                  asp_begin = np.argwhere(texts[text_i] == asps[asp_i][1])[0][0]
              except:
                  continue
              if asp_begin >= mask_len:
                  mask_begin = asp_begin - mask_len
              else:
                  mask_begin = 0
              for i in range(mask_begin):
                  masked_text_raw_indices[text_i][i] = np.zeros(
                      (self.bert_local.config.hidden_size), dtype=np.float
                  )
              for j in range(asp_begin + asp_len + mask_len, self.opt.max_seq_len):
                  masked_text_raw_indices[text_i][j] = np.zeros(
                      (self.bert_local.config.hidden_size), dtype=np.float
                  )
          masked_text_raw_indices = torch.from_numpy(masked_text_raw_indices)
          return masked_text_raw_indices.to(self.opt.device)
  
      def feature_dynamic_weighted(self, text_local_indices, aspect_indices):
          texts = text_local_indices.cpu().numpy()
          asps = aspect_indices.cpu().numpy()
          masked_text_raw_indices = np.ones(
              (
                  text_local_indices.size(0),
                  self.opt.max_seq_len,
                  self.bert_local.config.hidden_size,
              ),
              dtype=np.float32,
          )
          for text_i, asp_i in zip(range(len(texts)), range(len(asps))):
              asp_len = np.count_nonzero(asps[asp_i]) - 2
              try:
                  asp_begin = np.argwhere(texts[text_i] == asps[asp_i][1])[0][0]
                  asp_avg_index = (asp_begin * 2 + asp_len) / 2
              except:
                  continue
              distances = np.zeros(np.count_nonzero(texts[text_i]), dtype=np.float32)
              for i in range(1, np.count_nonzero(texts[text_i]) - 1):
                  if abs(i - asp_avg_index) + asp_len / 2 > self.opt.SRD:
                      distances[i] = 1 - (
                          abs(i - asp_avg_index) + asp_len / 2 - self.opt.SRD
                      ) / np.count_nonzero(texts[text_i])
                  else:
                      distances[i] = 1
              for i in range(len(distances)):
                  masked_text_raw_indices[text_i][i] = (
                      masked_text_raw_indices[text_i][i] * distances[i]
                  )
          masked_text_raw_indices = torch.from_numpy(masked_text_raw_indices)
          return masked_text_raw_indices.to(self.opt.device)
  
      def forward(self, inputs):
          text_target_bert_indices = FXDataset.get_input_by_params(
              inputs, BERT_BASE_UNCASED, FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS,
          )
  
          text_target_bert_segments_ids = FXDataset.get_input_by_params(
              inputs,
              BERT_BASE_UNCASED,
              FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS_SEGMENT_IDS,
          )
          text_local_indices = FXDataset.get_input_by_params(
              inputs, BERT_BASE_UNCASED, FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS
          )
          aspect_indices = FXDataset.get_input_by_params(
              inputs, BERT_BASE_UNCASED, FIELD_TARGET_IDS_WITH_SPECIAL_TOKENS
          )
  
          # apply bert and dropout
          bert_spc_out, _, _ = self.bert_spc(
              text_target_bert_indices, text_target_bert_segments_ids
          )
          bert_spc_out = self.dropout(bert_spc_out)
  
          bert_local_out, _, _ = self.bert_local(text_local_indices)
          bert_local_out = self.dropout(bert_local_out)
  
          if self.opt.local_context_focus == "cdm":
              masked_local_text_vec = self.feature_dynamic_mask(
                  text_local_indices, aspect_indices
              )
              bert_local_out = torch.mul(bert_local_out, masked_local_text_vec)
          elif self.opt.local_context_focus == "cdw":
              weighted_text_local_features = self.feature_dynamic_weighted(
                  text_local_indices, aspect_indices
              )
              bert_local_out = torch.mul(bert_local_out, weighted_text_local_features)
  
          # attention
          # bert_local_out = self.bert_local_SA(bert_local_out)
          # bert_spc_out = self.bert_global_SA(bert_spc_out)
  
          # cat
          out_cat = torch.cat((bert_local_out, bert_spc_out), dim=-1)
  
          # "interactive learning layer"
          mean_pool = self.linear_double(out_cat)
          self_attention_out = self.bert_SA(mean_pool)
          pooled_out = self.bert_pooler(self_attention_out)
  
          dense_out = self.dense(pooled_out)
          return dense_out
  