text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  import numpy as np
  from modules.word_embedding import WordEmbedding
  from modules.aggregator_predict import AggPredictor  # 聚集
  from modules.selection_predict import SelPredictor
  from modules.sqlnet_condition_predict import SQLNetCondPredictor
  from modules.select_number import SelNumPredictor
  from modules.where_relation import WhereRelationPredictor
  
  
  class SQLNet(nn.Module):
      def __init__(self, word_emb, N_word, N_h=100, N_depth=2,
              gpu=False, use_ca=True, trainable_emb=False):
          super(SQLNet, self).__init__()
          self.use_ca = use_ca
          self.trainable_emb = trainable_emb
  
          self.gpu = gpu
          self.N_h = N_h
          self.N_depth = N_depth
  
          self.max_col_num = 45
          self.max_tok_num = 200
          self.SQL_TOK = ['<UNK>', '<END>', 'WHERE', 'AND', 'OR', '==', '>', '<', '!=', '<BEG>']
          self.COND_OPS = ['>', '<', '==', '!=']
  
          # Word embedding 词嵌入
          self.embed_layer = WordEmbedding(word_emb, N_word, gpu, self.SQL_TOK, our_model=True, trainable=trainable_emb)
  
          # Predict the number of selected columns 预测所选列的数量
          self.sel_num = SelNumPredictor(N_word, N_h, N_depth, use_ca=use_ca)
  
          #Predict which columns are selected 预测选择了哪些列
          self.sel_pred = SelPredictor(N_word, N_h, N_depth, self.max_tok_num, use_ca=use_ca)
  
          #Predict aggregation functions of corresponding selected columns 预测所选列对应的聚合函数
          self.agg_pred = AggPredictor(N_word, N_h, N_depth, use_ca=use_ca)
  
          #Predict number of conditions, condition columns, condition operations and condition values 预测条件、条件列、条件操作和条件值的数量
          self.cond_pred = SQLNetCondPredictor(N_word, N_h, N_depth, self.max_col_num, self.max_tok_num, use_ca, gpu)
  
          # Predict condition relationship, like 'and', 'or'
          self.where_rela_pred = WhereRelationPredictor(N_word, N_h, N_depth, use_ca=use_ca)
  
  
          self.CE = nn.CrossEntropyLoss()
          self.softmax = nn.Softmax(dim=-1)
          self.log_softmax = nn.LogSoftmax()
          self.bce_logit = nn.BCEWithLogitsLoss()
          if gpu:
              self.cuda()
  
      def generate_gt_where_seq_test(self, q, gt_cond_seq):
          ret_seq = []
          for cur_q, ans in zip(q, gt_cond_seq):
              temp_q = u"".join(cur_q)
              cur_q = [u'<BEG>'] + cur_q + [u'<END>']
              record = []
              record_cond = []
              for cond in ans:
                  if cond[2] not in temp_q:
                      record.append((False, cond[2]))
                  else:
                      record.append((True, cond[2]))
              for idx, item in enumerate(record):
                  temp_ret_seq = []
                  if item[0]:
                      temp_ret_seq.append(0)
                      temp_ret_seq.extend(list(range(temp_q.index(item[1])+1,temp_q.index(item[1])+len(item[1])+1)))
                      temp_ret_seq.append(len(cur_q)-1)
                  else:
                      temp_ret_seq.append([0,len(cur_q)-1])
                  record_cond.append(temp_ret_seq)
              ret_seq.append(record_cond)
          return ret_seq
  
      def forward(self, q, col, col_num, gt_where = None, gt_cond=None, reinforce=False, gt_sel=None, gt_sel_num=None):
          B = len(q)
  
          sel_num_score = None
          agg_score = None
          sel_score = None
          cond_score = None
          #Predict aggregator 预测聚合器
          if self.trainable_emb:
              x_emb_var, x_len = self.agg_embed_layer.gen_x_batch(q, col)
              col_inp_var, col_name_len, col_len = self.agg_embed_layer.gen_col_batch(col)
              max_x_len = max(x_len)
              agg_score = self.agg_pred(x_emb_var, x_len, col_inp_var,
                      col_name_len, col_len, col_num, gt_sel=gt_sel)
  
              x_emb_var, x_len = self.sel_embed_layer.gen_x_batch(q, col)
              col_inp_var, col_name_len, col_len = self.sel_embed_layer.gen_col_batch(col)
              max_x_len = max(x_len)
              sel_score = self.sel_pred(x_emb_var, x_len, col_inp_var,
                      col_name_len, col_len, col_num)
  
              x_emb_var, x_len = self.cond_embed_layer.gen_x_batch(q, col)
              col_inp_var, col_name_len, col_len = self.cond_embed_layer.gen_col_batch(col)
              max_x_len = max(x_len)
              cond_score = self.cond_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num, gt_where, gt_cond, reinforce=reinforce)
              where_rela_score = None
          else:
              x_emb_var, x_len = self.embed_layer.gen_x_batch(q, col)
              col_inp_var, col_name_len, col_len = self.embed_layer.gen_col_batch(col)
              sel_num_score = self.sel_num(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num)
              # x_emb_var: embedding of each question 嵌入每个问题
              # x_len: length of each question
              # col_inp_var: embedding of each header 嵌入每个列明
              # col_name_len: length of each header
              # col_len: number of headers in each table, array type 数组类型
              # col_num: number of headers in each table, list type 列表类型
              if gt_sel_num:
                  pr_sel_num = gt_sel_num
              else:
                  pr_sel_num = np.argmax(sel_num_score.data.cpu().numpy(), axis=1)
              sel_score = self.sel_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num)
  
              if gt_sel:
                  pr_sel = gt_sel
              else:
                  num = np.argmax(sel_num_score.data.cpu().numpy(), axis=1)
                  sel = sel_score.data.cpu().numpy()
                  pr_sel = [list(np.argsort(-sel[b])[:num[b]]) for b in range(len(num))]
              agg_score = self.agg_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num, gt_sel=pr_sel, gt_sel_num=pr_sel_num)
  
              where_rela_score = self.where_rela_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num)
  
              cond_score = self.cond_pred(x_emb_var, x_len, col_inp_var, col_name_len, col_len, col_num, gt_where, gt_cond, reinforce=reinforce)
  
          return (sel_num_score, sel_score, agg_score, cond_score, where_rela_score)
  
      def loss(self, score, truth_num, gt_where):
          sel_num_score, sel_score, agg_score, cond_score, where_rela_score = score
  
          B = len(truth_num)
          loss = 0
  
          # Evaluate select number 评估
          sel_num_truth = map(lambda x:x[0], truth_num)
          sel_num_truth = torch.from_numpy(np.array(sel_num_truth))
          if self.gpu:
              sel_num_truth = Variable(sel_num_truth.cuda())
          else:
              sel_num_truth = Variable(sel_num_truth)
          loss += self.CE(sel_num_score, sel_num_truth)
  
          # Evaluate select column
          T = len(sel_score[0])
          truth_prob = np.zeros((B,T), dtype=np.float32)
          for b in range(B):
              truth_prob[b][list(truth_num[b][1])] = 1
          data = torch.from_numpy(truth_prob)
          if self.gpu:
              sel_col_truth_var = Variable(data.cuda())
          else:
              sel_col_truth_var = Variable(data)
          sigm = nn.Sigmoid()
          sel_col_prob = sigm(sel_score)
          bce_loss = -torch.mean(
              3*(sel_col_truth_var * torch.log(sel_col_prob+1e-10)) +
              (1-sel_col_truth_var) * torch.log(1-sel_col_prob+1e-10)
          )
          loss += bce_loss
  
          # Evaluate select aggregation
          for b in range(len(truth_num)):
              data = torch.from_numpy(np.array(truth_num[b][2]))
              if self.gpu:
                  sel_agg_truth_var = Variable(data.cuda())
              else:
                  sel_agg_truth_var = Variable(data)
              sel_agg_pred = agg_score[b, :len(truth_num[b][1])]
              loss += (self.CE(sel_agg_pred, sel_agg_truth_var)) / len(truth_num)
  
          cond_num_score, cond_col_score, cond_op_score, cond_str_score = cond_score
  
          # Evaluate the number of conditions
          cond_num_truth = map(lambda x:x[3], truth_num)
          data = torch.from_numpy(np.array(cond_num_truth))
          if self.gpu:
              try:
                  cond_num_truth_var = Variable(data.cuda())
              except:
                  print "cond_num_truth_var error"
                  print data
                  exit(0)
          else:
              cond_num_truth_var = Variable(data)
          loss += self.CE(cond_num_score, cond_num_truth_var)
  
          # Evaluate the columns of conditions
          T = len(cond_col_score[0])
          truth_prob = np.zeros((B, T), dtype=np.float32)
          for b in range(B):
              if len(truth_num[b][4]) > 0:
                  truth_prob[b][list(truth_num[b][4])] = 1
          data = torch.from_numpy(truth_prob)
          if self.gpu:
              cond_col_truth_var = Variable(data.cuda())
          else:
              cond_col_truth_var = Variable(data)
  
          sigm = nn.Sigmoid()
          cond_col_prob = sigm(cond_col_score)
          bce_loss = -torch.mean(
              3*(cond_col_truth_var * torch.log(cond_col_prob+1e-10)) +
              (1-cond_col_truth_var) * torch.log(1-cond_col_prob+1e-10) )
          loss += bce_loss
  
          # Evaluate the operator of conditions 评估条件运算符
          for b in range(len(truth_num)):
              if len(truth_num[b][5]) == 0:
                  continue
              data = torch.from_numpy(np.array(truth_num[b][5]))
              if self.gpu:
                  cond_op_truth_var = Variable(data.cuda())
              else:
                  cond_op_truth_var = Variable(data)
              cond_op_pred = cond_op_score[b, :len(truth_num[b][5])]
              try:
                  loss += (self.CE(cond_op_pred, cond_op_truth_var) / len(truth_num))
              except:
                  print cond_op_pred
                  print cond_op_truth_var
                  exit(0)
  
          #Evaluate the strings of conditions
          for b in range(len(gt_where)):
              for idx in range(len(gt_where[b])):
                  cond_str_truth = gt_where[b][idx]
                  if len(cond_str_truth) == 1:
                      continue
                  data = torch.from_numpy(np.array(cond_str_truth[1:]))
                  if self.gpu:
                      cond_str_truth_var = Variable(data.cuda())
                  else:
                      cond_str_truth_var = Variable(data)
                  str_end = len(cond_str_truth)-1
                  cond_str_pred = cond_str_score[b, idx, :str_end]
                  loss += (self.CE(cond_str_pred, cond_str_truth_var) \
                          / (len(gt_where) * len(gt_where[b])))
  
          # Evaluate condition relationship, and / or
          where_rela_truth = map(lambda x:x[6], truth_num)
          data = torch.from_numpy(np.array(where_rela_truth))
          if self.gpu:
              try:
                  where_rela_truth = Variable(data.cuda())
              except:
                  print "where_rela_truth error"
                  print data
                  exit(0)
          else:
              where_rela_truth = Variable(data)
          loss += self.CE(where_rela_score, where_rela_truth)
          return loss
  
      def check_acc(self, vis_info, pred_queries, gt_queries):
          def gen_cond_str(conds, header):
              if len(conds) == 0:
                  return 'None'
              cond_str = []
              for cond in conds:
                  cond_str.append(header[cond[0]] + ' ' +
                      self.COND_OPS[cond[1]] + ' ' + unicode(cond[2]).lower())
              return 'WHERE ' + ' AND '.join(cond_str)
  
          tot_err = sel_num_err = agg_err = sel_err = 0.0
          cond_num_err = cond_col_err = cond_op_err = cond_val_err = cond_rela_err = 0.0
          for b, (pred_qry, gt_qry) in enumerate(zip(pred_queries, gt_queries)):
              good = True
              sel_pred, agg_pred, where_rela_pred = pred_qry['sel'], pred_qry['agg'], pred_qry['cond_conn_op']
              sel_gt, agg_gt, where_rela_gt = gt_qry['sel'], gt_qry['agg'], gt_qry['cond_conn_op']
  
              if where_rela_gt != where_rela_pred:
                  good = False
                  cond_rela_err += 1
  
              if len(sel_pred) != len(sel_gt):
                  good = False
                  sel_num_err += 1
  
              pred_sel_dict = {k:v for k,v in zip(list(sel_pred), list(agg_pred))}
              gt_sel_dict = {k:v for k,v in zip(sel_gt, agg_gt)}
              if set(sel_pred) != set(sel_gt):
                  good = False
                  sel_err += 1
              agg_pred = [pred_sel_dict[x] for x in sorted(pred_sel_dict.keys())]
              agg_gt = [gt_sel_dict[x] for x in sorted(gt_sel_dict.keys())]
              if agg_pred != agg_gt:
                  good = False
                  agg_err += 1
  
              cond_pred = pred_qry['conds']
              cond_gt = gt_qry['conds']
              if len(cond_pred) != len(cond_gt):
                  good = False
                  cond_num_err += 1
              else:
                  cond_op_pred, cond_op_gt = {}, {}
                  cond_val_pred, cond_val_gt = {}, {}
                  for p, g in zip(cond_pred, cond_gt):
                      cond_op_pred[p[0]] = p[1]
                      cond_val_pred[p[0]] = p[2]
                      cond_op_gt[g[0]] = g[1]
                      cond_val_gt[g[0]] = g[2]
  
                  if set(cond_op_pred.keys()) != set(cond_op_gt.keys()):
                      cond_col_err += 1
                      good=False
  
                  where_op_pred = [cond_op_pred[x] for x in sorted(cond_op_pred.keys())]
                  where_op_gt = [cond_op_gt[x] for x in sorted(cond_op_gt.keys())]
                  if where_op_pred != where_op_gt:
                      cond_op_err += 1
                      good=False
  
                  where_val_pred = [cond_val_pred[x] for x in sorted(cond_val_pred.keys())]
                  where_val_gt = [cond_val_gt[x] for x in sorted(cond_val_gt.keys())]
                  if where_val_pred != where_val_gt:
                      cond_val_err += 1
                      good=False
  
              if not good:
                  tot_err += 1
  
          return np.array((sel_num_err, sel_err, agg_err, cond_num_err, cond_col_err, cond_op_err, cond_val_err , cond_rela_err)), tot_err
  
  
      def gen_query(self, score, q, col, raw_q, reinforce=False, verbose=False):
          """
          :param score:
          :param q: token-questions
          :param col: token-headers
          :param raw_q: original question sequence
          :return:
          """
          def merge_tokens(tok_list, raw_tok_str):
              tok_str = raw_tok_str# .lower()
              alphabet = 'abcdefghijklmnopqrstuvwxyz0123456789$('
              special = {'-LRB-':'(',
                      '-RRB-':')',
                      '-LSB-':'[',
                      '-RSB-':']',
                      '``':'"',
                      '\'\'':'"',
                      '--':u'\u2013'}
              ret = ''
              double_quote_appear = 0
              for raw_tok in tok_list:
                  if not raw_tok:
                      continue
                  tok = special.get(raw_tok, raw_tok)
                  if tok == '"':
                      double_quote_appear = 1 - double_quote_appear
                  if len(ret) == 0:
                      pass
                  elif len(ret) > 0 and ret + ' ' + tok in tok_str:
                      ret = ret + ' '
                  elif len(ret) > 0 and ret + tok in tok_str:
                      pass
                  elif tok == '"':
                      if double_quote_appear:
                          ret = ret + ' '
                  # elif tok[0] not in alphabet:
                  #     pass
                  elif (ret[-1] not in ['(', '/', u'\u2013', '#', '$', '&']) \
                          and (ret[-1] != '"' or not double_quote_appear):
                      ret = ret + ' '
                  ret = ret + tok
              return ret.strip()
  
          sel_num_score, sel_score, agg_score, cond_score, where_rela_score = score
          # [64,4,6], [64,14], ..., [64,4]
          sel_num_score = sel_num_score.data.cpu().numpy()
          sel_score = sel_score.data.cpu().numpy()
          agg_score = agg_score.data.cpu().numpy()
          where_rela_score = where_rela_score.data.cpu().numpy()
          ret_queries = []
          B = len(agg_score)
          cond_num_score,cond_col_score,cond_op_score,cond_str_score =\
              [x.data.cpu().numpy() for x in cond_score]
          for b in range(B):
              cur_query = {}
              cur_query['sel'] = []
              cur_query['agg'] = []
              sel_num = np.argmax(sel_num_score[b])
              max_col_idxes = np.argsort(-sel_score[b])[:sel_num]
              # find the most-probable columns' indexes 查找最可能的列的索引
              max_agg_idxes = np.argsort(-agg_score[b])[:sel_num]
              cur_query['sel'].extend([int(i) for i in max_col_idxes])
              cur_query['agg'].extend([i[0] for i in max_agg_idxes])
              cur_query['cond_conn_op'] = np.argmax(where_rela_score[b])
              cur_query['conds'] = []
              cond_num = np.argmax(cond_num_score[b])
              all_toks = ['<BEG>'] + q[b] + ['<END>']
              max_idxes = np.argsort(-cond_col_score[b])[:cond_num]
              for idx in range(cond_num):
                  cur_cond = []
                  cur_cond.append(max_idxes[idx]) # where-col
                  cur_cond.append(np.argmax(cond_op_score[b][idx])) # where-op
                  cur_cond_str_toks = []
                  for str_score in cond_str_score[b][idx]:
                      str_tok = np.argmax(str_score[:len(all_toks)])
                      str_val = all_toks[str_tok]
                      if str_val == '<END>':
                          break
                      cur_cond_str_toks.append(str_val)
                  cur_cond.append(merge_tokens(cur_cond_str_toks, raw_q[b]))
                  cur_query['conds'].append(cur_cond)
              ret_queries.append(cur_query)
          return ret_queries
  