text: # -*- coding: utf-8 -*-
  # wandb login 7cd7ade39e2d850ec1cf4e914d9a148586a20900
  from torch_struct import TreeCRF, SelfCritical
  try:
      import torchtext.legacy.data as data
  except ImportError:
      import torchtext.data as data
  from torch_struct.data import ListOpsDataset, TokenBucket
  from torch_struct.networks import TreeLSTM, SpanLSTM
  import torch
  import torch.nn as nn
  import wandb
  
  
  config = {
      "method": "reinforce",
      "baseline": "mean",
      "opt": "adadelta",
      "lr_struct": 0.1,
      "lr_params": 1,
      "train_model": True,
      "var_norm": False,
      "entropy": 0.001,
      "v": 3,
      "RL_K": 5,
      "H": 100,
      "train_len": 100,
      "div_ent": 1,
  }
  
  
  NAME = "yoyo3"
  
  wandb.init(project="pytorch-struct", config=config)
  
  
  def clip(p):
      torch.nn.utils.clip_grad_norm_(parameters=p, max_norm=0.5, norm_type=float("inf"))
  
  
  def expand_spans(spans, words, K, V):
      batch, N = words.shape
      spans[:, torch.arange(N), torch.arange(N)] = 0
      new_spans = torch.zeros(K, batch, N, N, 1 + V).cuda()
      new_spans[:, :, :, :, :1] = spans.view(K, batch, N, N, 1)
      new_spans[:, :, torch.arange(N), torch.arange(N), :].fill_(0)
      new_spans[:, :, torch.arange(N), torch.arange(N), 1:] = (
          torch.nn.functional.one_hot(words, V).float().cuda().view(1, batch, N, V)
      )
      new_spans = new_spans.view(batch * K, N, N, 1 + V)
      return new_spans
  
  
  def valid_sup(valid_iter, model, tree_lstm, V):
      total = 0
      correct = 0
      Dist = TreeCRF
      for i, ex in enumerate(valid_iter):
          words, lengths = ex.word
          trees = ex.tree
          label = ex.label
          words = words.cuda()
  
          def tree_reward(spans):
              new_spans = expand_spans(spans, words, 1, V)
              g, labels, indices, topo = TreeLSTM.spans_to_dgl(new_spans)
              _, am = tree_lstm(g, labels, indices, topo, lengths).max(-1)
              return (label == am).sum(), label.shape[0]
  
          words = words.cuda()
          phi = model(words, lengths)
          dist = TreeCRF(phi, lengths)
          argmax = dist.argmax
          argmax_tree = dist.struct.from_parts(argmax.detach())[0]
          score, tota = tree_reward(argmax_tree)
          total += int(tota)
          correct += score
  
          if i == 25:
              break
      print(correct.item() / float(total), correct, total)
      return correct.item() / float(total)
  
  
  def run_train(train_iter, valid_iter, model, tree_lstm, V):
      opt_struct = torch.optim.Adadelta(list(model.parameters()), lr=config["lr_struct"])
      opt_params = torch.optim.Adadelta(
          list(tree_lstm.parameters()), lr=config["lr_params"]
      )
  
      model.train()
      tree_lstm.train()
      losses = []
      Dist = TreeCRF
      step = 0
      trees = None
      for epoch in range(100):
          print("Epoch", epoch)
  
          for i, ex in enumerate(train_iter):
              step += 1
              words, lengths = ex.word
              label = ex.label
              batch = label.shape[0]
              _, N = words.shape
              words = words.cuda()
  
              def tree_reward(spans, K):
                  new_spans = expand_spans(spans, words, K, V)
                  g, labels, indices, topo = TreeLSTM.spans_to_dgl(new_spans)
                  ret = tree_lstm(
                      g, labels, indices, topo, torch.cat([lengths for _ in range(K)])
                  )
                  ret = ret.view(K, batch, -1)
                  return -ret[:, torch.arange(batch), label].view(K, batch)
  
              sc = SelfCritical(tree_reward)
              phi = model(words, lengths)
              dist = Dist(phi)
              structs, rewards, score, max_score = sc.forward(dist, K=config["RL_K"])
  
              if config["train_model"]:
                  opt_params.zero_grad()
                  score.mean().backward()
                  clip(tree_lstm.parameters())
                  opt_params.step()
                  opt_params.zero_grad()
  
              if config["method"] == "reinforce":
                  opt_struct.zero_grad()
                  entropy = dist.entropy
                  r = dist.log_prob(structs)
                  obj = rewards.mul(r).mean(-1).mean(-1)
                  policy = (
                      obj - config["entropy"] * entropy.div(lengths.float().cuda()).mean()
                  )
                  policy.backward()
                  clip(model.parameters())
                  opt_struct.step()
              losses.append(-max_score.mean().detach())
  
              # DEBUG
              if i % 50 == 9:
                  print(torch.tensor(losses).mean(), words.shape)
                  print("Round")
                  print("Entropy", entropy.mean().item())
                  print("Reward", rewards.mean().item())
                  if i % 1000 == 9:
                      valid_loss = valid_sup(valid_iter, model, tree_lstm, V)
                      fname = "/tmp/checkpoint.%s.%0d.%0d.%s" % (
                          NAME,
                          epoch,
                          i,
                          valid_loss,
                      )
                      torch.save((model, tree_lstm), fname)
                      wandb.save(fname)
                      trees = valid_show(valid_iter, model)
                  else:
                      print(valid_loss)
  
                  wandb.log(
                      {
                          "entropy": entropy.mean(),
                          "valid_loss": valid_loss,
                          "reward": rewards.mean(),
                          "step": step,
                          "tree": trees,
                          "reward_var": rewards.var(),
                          "loss": torch.tensor(losses).mean(),
                      }
                  )
                  losses = []
  
  
  def valid_show(valid_iter, model):
      table = wandb.Table(columns=["Sent", "Predicted Tree", "True Tree"])
      Dist = TreeCRF
      for i, ex in enumerate(valid_iter):
          words, lengths = ex.word
          label = ex.label
          batch = label.shape[0]
          words = words.cuda()
          phi = model(words, lengths)
          dist = Dist(phi)
          argmax = dist.argmax
          argmax_tree = dist.struct.from_parts(argmax.detach())[0].cpu()
          for b in range(words.shape[0]):
              out = [WORD.vocab.itos[w.item()] for w in words[b]]
              sent = " ".join(out)
  
              def show(tree):
                  output = ""
                  start = {}
                  end = {}
                  for i, j, _ in tree.nonzero():
                      i = i.item()
                      j = j.item()
                      start.setdefault(i, -1)
                      end.setdefault(j, -1)
                      start[i] += 1
                      end[j] += 1
                  for i, w in enumerate(out):
                      for _ in range(start.get(i, 0)):
                          output += "( "
                      output += w + " "
                      for _ in range(end.get(i, 0)):
                          output += ") "
                  return output
  
              predict_text = show(ex.tree[b].cpu())
              true_text = show(argmax_tree[b].cpu())
              table.add_data(sent, predict_text, true_text)
          break
      return table
  
  
  WORD = None
  
  
  def main():
      global WORD
      WORD = data.Field(
          include_lengths=True, batch_first=True, eos_token=None, init_token=None
      )
      LABEL = data.Field(sequential=False, batch_first=True)
      TREE = data.RawField(postprocessing=ListOpsDataset.tree_field(WORD))
      TREE.is_target = False
      train = ListOpsDataset(
          "data/train_d20s.tsv",
          (("word", WORD), ("label", LABEL), ("tree", TREE)),
          filter_pred=lambda x: 5 < len(x.word) < config["train_len"],
      )
      WORD.build_vocab(train)
      LABEL.build_vocab(train)
      valid = ListOpsDataset(
          "data/test_d20s.tsv",
          (("word", WORD), ("label", LABEL), ("tree", TREE)),
          filter_pred=lambda x: 5 < len(x.word) < 150,
      )
  
      train_iter = TokenBucket(
          train, batch_size=1500, device="cuda:0", key=lambda x: len(x.word)
      )
      train_iter.repeat = False
      valid_iter = data.BucketIterator(
          train, batch_size=50, train=False, sort=False, device="cuda:0"
      )
  
      NT = 1
      T = len(WORD.vocab)
      V = T
  
      if True:
          tree_lstm = TreeLSTM(
              config["H"], len(WORD.vocab) + 100, len(LABEL.vocab)
          ).cuda()
          for p in tree_lstm.parameters():
              if p.dim() > 1:
                  torch.nn.init.xavier_uniform_(p)
  
          model = SpanLSTM(NT, len(WORD.vocab), config["H"]).cuda()
          for p in model.parameters():
              if p.dim() > 1:
                  torch.nn.init.xavier_uniform_(p)
  
          wandb.watch((model, tree_lstm))
          print(wandb.config)
          tree = run_train(train_iter, valid_iter, model, tree_lstm, V)
      else:
          print("loading")
          model, tree_lstm = torch.load("cp.yoyo.model")
          print(valid_sup(valid_iter, model, tree_lstm, V))
  
  
  main()
  