text: ''' This module will handle the text generation with beam search. '''
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from transformer.Models import Transformer
  from transformer.Beam import Beam
  import pdb
  
  class Translator(object):
      ''' Load with trained model and handle the beam search '''
  
      def __init__(self, opt):
          self.opt = opt
          self.device = torch.device('cuda' if opt.cuda else 'cpu')
  
          checkpoint = torch.load(opt.model)
          model_opt = checkpoint['settings']
          self.model_opt = model_opt
  
          model = Transformer(
              model_opt.src_vocab_size,
              model_opt.tgt_vocab_size,
              model_opt.max_token_seq_len,
              tgt_emb_prj_weight_sharing=model_opt.proj_share_weight,
              emb_src_tgt_weight_sharing=model_opt.embs_share_weight,
              d_k=model_opt.d_k,
              d_v=model_opt.d_v,
              d_model=model_opt.d_model,
              d_word_vec=model_opt.d_word_vec,
              d_inner=model_opt.d_inner_hid,
              n_layers=model_opt.n_layers,
              n_head=model_opt.n_head,
              dropout=model_opt.dropout)
  
          model.load_state_dict(checkpoint['model'])
          print('[Info] Trained model state loaded.')
  
          model.word_prob_prj = nn.LogSoftmax(dim=1)
  
          model = model.to(self.device)
  
          self.model = model
          self.model.eval()
  
      def translate_batch(self, src_seq, src_pos):
          ''' Translation work in one batch '''
  
          def get_inst_idx_to_tensor_position_map(inst_idx_list):
              ''' Indicate the position of an instance in a tensor. '''
              return {inst_idx: tensor_position for tensor_position, inst_idx in enumerate(inst_idx_list)}
  
          def collect_active_part(beamed_tensor, curr_active_inst_idx, n_prev_active_inst, n_bm):
              ''' Collect tensor parts associated to active instances. '''
  
              _, *d_hs = beamed_tensor.size()
              n_curr_active_inst = len(curr_active_inst_idx)
              new_shape = (n_curr_active_inst * n_bm, *d_hs)
  
              beamed_tensor = beamed_tensor.view(n_prev_active_inst, -1)
              beamed_tensor = beamed_tensor.index_select(0, curr_active_inst_idx)
              beamed_tensor = beamed_tensor.view(*new_shape)
  
              return beamed_tensor
  
          def collate_active_info(
                  src_seq, src_enc, inst_idx_to_position_map, active_inst_idx_list):
              # Sentences which are still active are collected,
              # so the decoder will not run on completed sentences.
              n_prev_active_inst = len(inst_idx_to_position_map)
              active_inst_idx = [inst_idx_to_position_map[k] for k in active_inst_idx_list]
              active_inst_idx = torch.LongTensor(active_inst_idx).to(self.device)
  
              active_src_seq = collect_active_part(src_seq, active_inst_idx, n_prev_active_inst, n_bm)
              active_src_enc = collect_active_part(src_enc, active_inst_idx, n_prev_active_inst, n_bm)
              active_inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(active_inst_idx_list)
  
              return active_src_seq, active_src_enc, active_inst_idx_to_position_map
  
          def beam_decode_step(
                  inst_dec_beams, len_dec_seq, src_seq, enc_output, inst_idx_to_position_map, n_bm):
              ''' Decode and update beam status, and then return active beam idx '''
  
              def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq):
                  dec_partial_seq = [b.get_current_state() for b in inst_dec_beams if not b.done]
                  dec_partial_seq = torch.stack(dec_partial_seq).to(self.device)
                  dec_partial_seq = dec_partial_seq.view(-1, len_dec_seq)
                  return dec_partial_seq
  
              def prepare_beam_dec_pos(len_dec_seq, n_active_inst, n_bm):
                  dec_partial_pos = torch.arange(1, len_dec_seq + 1, dtype=torch.long, device=self.device)
                  dec_partial_pos = dec_partial_pos.unsqueeze(0).repeat(n_active_inst * n_bm, 1)
                  return dec_partial_pos
  
              def predict_word(dec_seq, dec_pos, src_seq, enc_output, n_active_inst, n_bm):
                  dec_output, *_ = self.model.decoder(dec_seq, dec_pos, src_seq, enc_output)
                  dec_output = dec_output[:, -1, :]  # Pick the last step: (bh * bm) * d_h
                  word_prob = F.log_softmax(self.model.tgt_word_prj(dec_output), dim=1)
                  word_prob = word_prob.view(n_active_inst, n_bm, -1)
  
                  return word_prob
  
              def collect_active_inst_idx_list(inst_beams, word_prob, inst_idx_to_position_map):
                  active_inst_idx_list = []
                  for inst_idx, inst_position in inst_idx_to_position_map.items():
                      is_inst_complete = inst_beams[inst_idx].advance(word_prob[inst_position])
                      if not is_inst_complete:
                          active_inst_idx_list += [inst_idx]
  
                  return active_inst_idx_list
  
              pdb.set_trace()
              n_active_inst = len(inst_idx_to_position_map)
  
              dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq)
              dec_pos = prepare_beam_dec_pos(len_dec_seq, n_active_inst, n_bm)
              word_prob = predict_word(dec_seq, dec_pos, src_seq, enc_output, n_active_inst, n_bm)
          
              # Update the beam with predicted word prob information and collect incomplete instances
              active_inst_idx_list = collect_active_inst_idx_list(
                  inst_dec_beams, word_prob, inst_idx_to_position_map)
  
              return active_inst_idx_list
  
          def collect_hypothesis_and_scores(inst_dec_beams, n_best):
              all_hyp, all_scores = [], []
              for inst_idx in range(len(inst_dec_beams)):
                  scores, tail_idxs = inst_dec_beams[inst_idx].sort_scores()
                  all_scores += [scores[:n_best]]
  
                  hyps = [inst_dec_beams[inst_idx].get_hypothesis(i) for i in tail_idxs[:n_best]]
                  all_hyp += [hyps]
              return all_hyp, all_scores
  
          with torch.no_grad():
              #-- Encode
              src_seq, src_pos = src_seq.to(self.device), src_pos.to(self.device)
              src_enc, *_ = self.model.encoder(src_seq, src_pos)
  
              #-- Repeat data for beam search
              n_bm = self.opt.beam_size
              n_inst, len_s, d_h = src_enc.size()
              src_seq = src_seq.repeat(1, n_bm).view(n_inst * n_bm, len_s)
              src_enc = src_enc.repeat(1, n_bm, 1).view(n_inst * n_bm, len_s, d_h)
  
              #-- Prepare beams
              inst_dec_beams = [Beam(n_bm, device=self.device) for _ in range(n_inst)]
  
              #-- Bookkeeping for active or not
              active_inst_idx_list = list(range(n_inst))
              inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(active_inst_idx_list)
  
              #-- Decode
              for len_dec_seq in range(1, self.model_opt.max_token_seq_len + 1):
  
                  active_inst_idx_list = beam_decode_step(
                      inst_dec_beams, len_dec_seq, src_seq, src_enc, inst_idx_to_position_map, n_bm)
  
                  if not active_inst_idx_list:
                      break  # all instances have finished their path to <EOS>
  
                  src_seq, src_enc, inst_idx_to_position_map = collate_active_info(
                      src_seq, src_enc, inst_idx_to_position_map, active_inst_idx_list)
  
          batch_hyp, batch_scores = collect_hypothesis_and_scores(inst_dec_beams, self.opt.n_best)
  
          return batch_hyp, batch_scores
  