text: import torch
  import pickle
  import argparse
  import os
  from tqdm import trange, tqdm
  import torch
  import torchtext
  from torchtext import data
  from torchtext import datasets
  from torch import nn
  import torch.nn.functional as F
  import math
  
  from models import SimpleLSTMModel, AttentionRNN
  
  from models.components.binarization import (
      Binarize,
  )
  
  from eval_args import get_arg_parser
  import constants
  from vocab import Vocabulary, load_vocab
  import dataset as d
  import utils
  
  def eval_bleu(
      train_loader: d.BatchedIterator,
      valid_loader: d.BatchedIterator,
      model: nn.Module,
      en_vocab: Vocabulary,
      fr_vocab: Vocabulary,
      device: str,
      multi_gpu: bool,
      eval_fast: bool,
      output_file: str,
  ) -> None:
      model = model.to(device)
  
      if output_file is not None:
          output_file = open(output_file, 'w')
  
      if multi_gpu and device == 'cuda':
         print('Using multi gpu training')
         model = torch.nn.DataParallel(model, device_ids=[0, 1]).cuda()
      
      bleus = []
      count = 0
      with tqdm(train_loader, total=len(train_loader)) as pbar:
          for i, data in enumerate(pbar):
              if i == 0:
                  continue
              src, src_lengths = data.src
              trg, trg_lengths = data.trg
  
              if eval_fast:
                  predicted = model.generate_max(src, src_lengths, 100, device)
              else:
                  predicted = model.slow_generate(src, src_lengths, 100, device)
              # predicted = (torch.Tensor(src.size(0), 100).uniform_() * (len(fr_vocab) - 1)).long()
              # predicted = predicted * 
              # predicted = model.generate_beam(src, src_lengths, 100, 5, device)
              pred_arr = utils.torchtext_convert_to_str(predicted.cpu().numpy(), fr_vocab)[0]
              out_arr = utils.torchtext_convert_to_str(trg.cpu().numpy(), fr_vocab)[0]
              pred_slim_arr = utils.get_raw_sentence(pred_arr)
              out_slim_arr = utils.get_raw_sentence(out_arr)
              curr_bleu = utils.compute_bleu(pred_slim_arr, out_slim_arr)
              bleus.append(curr_bleu)
  
              if output_file is not None:
                  src_arr = utils.torchtext_convert_to_str(src.cpu().numpy(), en_vocab)[0]
                  src_slim_arr = utils.get_raw_sentence(src_arr)
                  output = ' '.join(pred_slim_arr)
                  actual_out = ' '.join(out_slim_arr)
                  src = ' '.join(src_slim_arr)
                  
                  entry_str ='''
  {DELIM}
      BLEU = {BLEU}
      src = {src}
      target = {target}
      predicted = {pred} 
  '''
                  entry_str = entry_str.format(
                      DELIM=utils.create_entry_delim(),
                      BLEU=curr_bleu * 100,
                      src=src,
                      target=actual_out,
                      pred=output,
                  )
  
                  output_file.write(entry_str)
              count += 1
              pbar.set_postfix(
                  curr_bleu=curr_bleu * 100,
                  avg_bleu=(sum(bleus) / len(bleus) * 100)
              )
              pbar.refresh()
  
      if output_file is not None:
          output_file.write(
              utils.create_entry_delim() + "\n"
          )
  
          output_file.write(
              'Average BLEU: {}\n'.format((sum(bleus) / len(bleus) * 100))
          )
          output_file.close()
  
  def main() -> None:
      parser = get_arg_parser()
      args = parser.parse_args()
      device = "cuda" if torch.cuda.is_available() and args.cuda else "cpu"
      print('using device {}'.format(device))
  
  
      print('loading datasets...')
      src = data.Field(include_lengths=True,
                 init_token='<sos>', eos_token='<eos>', batch_first=True, fix_length=200)
      trg = data.Field(include_lengths=True,
                 init_token='<sos>', eos_token='<eos>', batch_first=True)
      
      if args.dataset == 'WMT':
          mt_train = datasets.TranslationDataset(
              path=constants.WMT14_EN_FR_SMALL_TRAIN,
              exts=('.en', '.fr'),
              fields=(src, trg)
          )
          src_vocab, trg_vocab = utils.load_torchtext_wmt_small_vocab()
          src.vocab = src_vocab
  
          trg.vocab = trg_vocab
  
          mt_valid = None
      else:
          if args.dataset == 'Multi30k':
              mt_train, mt_valid, mt_test = datasets.Multi30k.splits(
                  exts=('.en', '.de'),
                  fields=(src, trg),
              )
          elif args.dataset == 'IWSLT':
              mt_train, mt_valid, mt_test = datasets.IWSLT.splits(
                  exts=('.en', '.de'),
                  fields=(src, trg), 
              )
          else:
              raise Exception("Uknown dataset: {}".format(args.dataset))
  
          print('loading vocabulary...')
  
          # mt_dev shares the fields, so it shares their vocab objects
          src.build_vocab(
              mt_train,
              min_freq=args.torchtext_unk,
              max_size=args.torchtext_src_max_vocab,
          )
  
          trg.build_vocab(
              mt_train,
              max_size=args.torchtext_trg_max_vocab,
          )
          print('loaded vocabulary')
      
      # determine the correct dataset to evaluate
      eval_dataset = mt_train if args.eval_train else mt_valid
      eval_dataset = mt_test if args.eval_test else eval_dataset
  
      train_loader = data.BucketIterator(
          dataset=eval_dataset,
          batch_size=1,
          sort_key=lambda x: len(x.src), # data.interleave_keys(len(x.src), len(x.trg)),
          sort_within_batch=True,
          device=device
      )
  
      print('model type: {}'.format(args.model_type))
      model = utils.build_model(parser, src.vocab, trg.vocab)
      model.load_state_dict(torch.load(args.load_path))
      model = model.eval()
      if args.binarize:
          print('binarizing model')
          binarized_model = Binarize(model)
          binarized_model.binarization()
  
  
      print('using model...')
      print(model)
  
      eval_bleu(
          train_loader=train_loader,
          valid_loader=None, # valid_loader,
          model=model,
          en_vocab=src.vocab,
          fr_vocab=trg.vocab,
          device=device,
          multi_gpu=args.multi_gpu,
          eval_fast=args.eval_fast,
          output_file=args.output_file,
      )
  
  if __name__ == "__main__":
      main()