text: """
      This is the loadable seq2seq trainer library that is
      in charge of training details, loss compute, and statistics.
      See train.py for a use case of this library.
  
      Note: To make this a general library, we implement *only*
            mechanism things here(i.e. what to do), and leave the strategy
            things to users(i.e. how to do it). Also see train.py(one of the
            users of this library) for the strategy things we do.
  """
  
  import onmt.inputters as inputters
  import onmt.utils
  
  from onmt.utils.logging import logger
  
  
  def build_trainer(opt, device_id, model, fields, optim, data_type, model_saver=None):
      """
      Simplify `Trainer` creation based on user `opt`s*
  
      Args:
          opt (:obj:`Namespace`): user options (usually from argument parsing)
          model (:obj:`onmt.models.NMTModel`): the model to train
          fields (dict): dict of fields
          optim (:obj:`onmt.utils.Optimizer`): optimizer used during training
          data_type (str): string describing the type of data
              e.g. "text", "img", "audio"
          model_saver(:obj:`onmt.models.ModelSaverBase`): the utility object
              used to save the model
      """
      train_loss = onmt.utils.loss.build_loss_compute(model, fields["tgt"], opt)
      valid_loss = onmt.utils.loss.build_loss_compute(model, fields["tgt"], opt, train=False)
  
      trunc_size = opt.truncated_decoder  # Badly named...
      shard_size = opt.max_generator_batches
      norm_method = opt.normalization
      grad_accum_count = opt.accum_count
      n_gpu = opt.world_size
      if device_id >= 0:
          gpu_rank = opt.gpu_ranks[device_id]
      else:
          gpu_rank = 0
          n_gpu = 0
      gpu_verbose_level = opt.gpu_verbose_level
  
      report_manager = onmt.utils.build_report_manager(opt)
      trainer = onmt.Trainer(opt.model_mode, model, train_loss, valid_loss, optim, trunc_size,
                             shard_size, data_type, norm_method,
                             grad_accum_count, n_gpu, gpu_rank,
                             gpu_verbose_level, report_manager,
                             model_saver=model_saver)
      return trainer
  
  
  class Trainer(object):
      """
      Class that controls the training process.
  
      Args:
              model(:py:class:`onmt.models.model.NMTModel`): translation model
                  to train
              train_loss(:obj:`onmt.utils.loss.LossComputeBase`):
                 training loss computation
              valid_loss(:obj:`onmt.utils.loss.LossComputeBase`):
                 training loss computation
              optim(:obj:`onmt.utils.optimizers.Optimizer`):
                 the optimizer responsible for update
              trunc_size(int): length of truncated back propagation through time
              shard_size(int): compute loss in shards of this size for efficiency
              data_type(string): type of the source input: [text|img|audio]
              norm_method(string): normalization methods: [sents|tokens]
              grad_accum_count(int): accumulate gradients this many times.
              report_manager(:obj:`onmt.utils.ReportMgrBase`):
                  the object that creates reports, or None
              model_saver(:obj:`onmt.models.ModelSaverBase`): the saver is
                  used to save a checkpoint.
                  Thus nothing will be saved if this parameter is None
      """
  
      def __init__(self, model_mode, model, train_loss, valid_loss, optim,
                   trunc_size=0, shard_size=32, data_type='text',
                   norm_method="sents", grad_accum_count=1, n_gpu=1, gpu_rank=1,
                   gpu_verbose_level=0, report_manager=None, model_saver=None):
          # Basic attributes.
          self.model_mode = model_mode
          self.model = model
          self.train_loss = train_loss
          self.valid_loss = valid_loss
          self.optim = optim
          self.trunc_size = trunc_size
          self.shard_size = shard_size
          self.data_type = data_type
          self.norm_method = norm_method
          self.grad_accum_count = grad_accum_count
          self.n_gpu = n_gpu
          self.gpu_rank = gpu_rank
          self.gpu_verbose_level = gpu_verbose_level
          self.report_manager = report_manager
          self.model_saver = model_saver
  
          assert grad_accum_count > 0
          if grad_accum_count > 1:
              assert(self.trunc_size == 0), \
                  """To enable accumulated gradients,
                     you must disable target sequence truncating."""
  
          # Set model in training mode.
          self.model.train()
  
      def train(self, train_iter_fct, valid_iter_fct, train_steps, valid_steps):
          """
          The main training loops.
          by iterating over training data (i.e. `train_iter_fct`)
          and running validation (i.e. iterating over `valid_iter_fct`
  
          Args:
              train_iter_fct(function): a function that returns the train
                  iterator. e.g. something like
                  train_iter_fct = lambda: generator(*args, **kwargs)
              valid_iter_fct(function): same as train_iter_fct, for valid data
              train_steps(int):
              valid_steps(int):
              save_checkpoint_steps(int):
  
          Return:
              None
          """
          logger.info('Start training...')
  
          step = self.optim._step + 1
          true_batchs = []
          accum = 0
          normalization = 0
          train_iter = train_iter_fct()
  
          total_stats = onmt.utils.Statistics()
          report_stats = onmt.utils.Statistics()
          self._start_report_manager(start_time=total_stats.start_time)
  
          while step <= train_steps:
  
              reduce_counter = 0
              for i, batch in enumerate(train_iter):
                  """
                  batch:
                  [torchtext.data.batch.Batch of size 13]
                  	[.src]:('[torch.cuda.LongTensor of size 150x13 (GPU 0)]', '[torch.cuda.LongTensor of size 13 (GPU 0)]')
                  	[.knl]:('[torch.cuda.LongTensor of size 800x13 (GPU 0)]', '[torch.cuda.LongTensor of size 13 (GPU 0)]')
                  	[.src_map]:[torch.cuda.FloatTensor of size 150x13x102 (GPU 0)]
                  	[.alignment]:[torch.cuda.LongTensor of size 40x13 (GPU 0)]
                  	[.tgt]:[torch.cuda.LongTensor of size 40x13 (GPU 0)]
                  	[.indices]:[torch.cuda.LongTensor of size 13 (GPU 0)]
                  	[.src_da_label]:[torch.cuda.LongTensor of size 13x3 (GPU 0)]
                  	[.tgt_da_label]:[torch.cuda.LongTensor of size 13 (GPU 0)]
                  """
                  if self.n_gpu == 0 or (i % self.n_gpu == self.gpu_rank):
                      if self.gpu_verbose_level > 1:
                          logger.info("GpuRank %d: index: %d accum: %d"
                                      % (self.gpu_rank, i, accum))
  
                      true_batchs.append(batch)
  
                      if self.norm_method == "tokens":
                          num_tokens = batch.tgt[1:].ne(self.train_loss.padding_idx).sum()
                          normalization += num_tokens.item()
                      else:
                          normalization += batch.batch_size
                      accum += 1
                      if accum == self.grad_accum_count:
                          reduce_counter += 1
                          if self.gpu_verbose_level > 0:
                              logger.info("GpuRank %d: reduce_counter: %d \
                                          n_minibatch %d"
                                          % (self.gpu_rank, reduce_counter,
                                             len(true_batchs)))
                          if self.n_gpu > 1:
                              normalization = sum(onmt.utils.distributed
                                                  .all_gather_list
                                                  (normalization))
  
                          self._gradient_accumulation(true_batchs, normalization, total_stats, report_stats)
  
                          report_stats = self._maybe_report_training(
                              step, train_steps,
                              self.optim.learning_rate,
                              report_stats)
  
                          true_batchs = []
                          accum = 0
                          normalization = 0
                          if (step % valid_steps == 0):
                              if self.gpu_verbose_level > 0:
                                  logger.info('GpuRank %d: validate step %d'
                                              % (self.gpu_rank, step))
                              valid_iter = valid_iter_fct()
                              valid_stats = self.validate(valid_iter)
                              if self.gpu_verbose_level > 0:
                                  logger.info('GpuRank %d: gather valid stat \
                                              step %d' % (self.gpu_rank, step))
                              valid_stats = self._maybe_gather_stats(valid_stats)
                              if self.gpu_verbose_level > 0:
                                  logger.info('GpuRank %d: report stat step %d'
                                              % (self.gpu_rank, step))
                              self._report_step(self.optim.learning_rate,
                                                step, valid_stats=valid_stats)
  
                          if self.gpu_rank == 0:
                              self._maybe_save(step)
                          step += 1
                          if step > train_steps:
                              break
              if self.gpu_verbose_level > 0:
                  logger.info('GpuRank %d: we completed an epoch \
                              at step %d' % (self.gpu_rank, step))
              train_iter = train_iter_fct()
  
          return total_stats
  
      def validate(self, valid_iter):
          """ Validate model.
              valid_iter: validate data iterator
          Returns:
              :obj:`nmt.Statistics`: validation loss statistics
          """
          # Set model in validating mode.
          self.model.eval()
  
          stats = onmt.utils.Statistics()
  
          for batch in valid_iter:
              src = inputters.make_features(batch, 'src', self.data_type)
              if self.data_type == 'text':
                  _, src_lengths = batch.src
              elif self.data_type == 'audio':
                  src_lengths = batch.src_lengths
              else:
                  src_lengths = None
  
              knl = inputters.make_features(batch, 'knl', self.data_type)
              _, knl_lengths = batch.knl
  
              tgt = inputters.make_features(batch, 'tgt')
  
              # F-prop through the model.
              # TODO: apply to all_acts
              if self.model_mode in ['default', 'top_act']:
                  first_outputs, first_attns, second_outputs, second_attns = self.model(
                      knl, src, tgt, src_lengths, knl_lengths,
                      src_da_label=(batch.src_da_label[:, 0], batch.src_da_label[:, 1], batch.src_da_label[:, 2]),
                      tgt_da_label=(batch.tgt_da_label,)
                  )
              elif self.model_mode in ['all_acts']:
                  first_outputs, first_attns, second_outputs, second_attns = self.model(
                      knl, src, tgt, src_lengths, knl_lengths,
                      src_da_label=(
                          batch.src_da_label[:, 0], batch.src_da_label[:, 1], batch.src_da_label[:, 2], batch.src_da_label[:, 3],
                          batch.src_da_label[:, 4], batch.src_da_label[:, 5], batch.src_da_label[:, 6], batch.src_da_label[:, 7],
                          batch.src_da_label[:, 8], batch.src_da_label[:, 9], batch.src_da_label[:, 10], batch.src_da_label[:, 11]
                      ),
                      tgt_da_label=(batch.tgt_da_label,)
                  )
  
              # Compute loss.
              #batch_stats1 = self.valid_loss.monolithic_compute_loss(
              #    batch, first_outputs, first_attns)
              batch_stats2 = self.valid_loss.monolithic_compute_loss(
                  batch, second_outputs, second_attns)
  
              # Update statistics.
              #stats.update(batch_stats1)
              stats.update(batch_stats2)
  
          # Set model back to training mode.
          self.model.train()
  
          return stats
  
      def _gradient_accumulation(self, true_batchs, normalization, total_stats, report_stats):
          if self.grad_accum_count > 1:
              self.model.zero_grad()
  
          for batch in true_batchs:
              target_size = batch.tgt.size(0)
              # Truncated BPTT: reminder not compatible with accum > 1
              if self.trunc_size:
                  trunc_size = self.trunc_size
              else:
                  trunc_size = target_size
  
              # dec_state = None
              #print("onmt.trainer.py: {}".format(batch))
              src = inputters.make_features(batch, 'src', self.data_type, model_mode='default')
              #print("[onmt.trainer.py] src.shape: {}".format(src.shape))
              if self.data_type == 'text':
                  _, src_lengths = batch.src
                  report_stats.n_src_words += src_lengths.sum().item()
              elif self.data_type == 'audio':
                  src_lengths = batch.src_lengths
              else:
                  src_lengths = None
  
              knl = inputters.make_features(batch, 'knl', self.data_type)
              _, knl_lengths = batch.knl
  
              tgt_outer = inputters.make_features(batch, 'tgt')
  
              for j in range(0, target_size-1, trunc_size):
                  # 1. Create truncated target.
                  tgt = tgt_outer[j: j + trunc_size]
  
                  # 2. F-prop all but generator.
                  if self.grad_accum_count == 1:
                      self.model.zero_grad()
                  # TODO: embed dialogue act label
                  if self.model_mode in ['default', 'top_act']:
                      first_outputs, first_attns, second_outputs, second_attns = \
                          self.model(
                              knl, src, tgt, src_lengths, knl_lengths,
                              src_da_label=(batch.src_da_label[:, 0], batch.src_da_label[:, 1], batch.src_da_label[:, 2]),
                              tgt_da_label=(batch.tgt_da_label,)
                          )
                  elif self.model_mode in ['all_acts']:
                      first_outputs, first_attns, second_outputs, second_attns = \
                          self.model(
                              knl, src, tgt, src_lengths, knl_lengths,
                              src_da_label=(
                                  batch.src_da_label[:, 0], batch.src_da_label[:, 1], batch.src_da_label[:, 2], batch.src_da_label[:, 3],
                                  batch.src_da_label[:, 4], batch.src_da_label[:, 5], batch.src_da_label[:, 6], batch.src_da_label[:, 7],
                                  batch.src_da_label[:, 8], batch.src_da_label[:, 9], batch.src_da_label[:, 10], batch.src_da_label[:, 11]
                              ),
                              tgt_da_label=(batch.tgt_da_label,)
                          )
                  # 3. Compute loss in shards for memory efficiency.
                  batch_stats1 = self.train_loss.sharded_compute_loss(
                      batch, first_outputs, first_attns, j,
                      trunc_size, self.shard_size, normalization)
                  batch_stats2 = self.train_loss.sharded_compute_loss(
                      batch, second_outputs, second_attns, j,
                      trunc_size, self.shard_size, normalization)
  
                  total_stats.update(batch_stats1)
                  report_stats.update(batch_stats1)
                  total_stats.update(batch_stats2)
                  report_stats.update(batch_stats2)
  
                  # 4. Update the parameters and statistics.
                  if self.grad_accum_count == 1:
                      # Multi GPU gradient gather
                      if self.n_gpu > 1:
                          grads = [p.grad.data for p in self.model.parameters()
                                   if p.requires_grad
                                   and p.grad is not None]
                          onmt.utils.distributed.all_reduce_and_rescale_tensors(
                              grads, float(1))
                      self.optim.step()
  
                  # If truncated, don't backprop fully.
                  # TO CHECK
                  # if dec_state is not None:
                  #    dec_state.detach()
                  if self.model.decoder.state is not None:
                      self.model.decoder.detach_state()
  
          # in case of multi step gradient accumulation,
          # update only after accum batches
          if self.grad_accum_count > 1:
              if self.n_gpu > 1:
                  grads = [p.grad.data for p in self.model.parameters()
                           if p.requires_grad
                           and p.grad is not None]
                  onmt.utils.distributed.all_reduce_and_rescale_tensors(
                      grads, float(1))
              self.optim.step()
  
      def _start_report_manager(self, start_time=None):
          """
          Simple function to start report manager (if any)
          """
          if self.report_manager is not None:
              if start_time is None:
                  self.report_manager.start()
              else:
                  self.report_manager.start_time = start_time
  
      def _maybe_gather_stats(self, stat):
          """
          Gather statistics in multi-processes cases
  
          Args:
              stat(:obj:onmt.utils.Statistics): a Statistics object to gather
                  or None (it returns None in this case)
  
          Returns:
              stat: the updated (or unchanged) stat object
          """
          if stat is not None and self.n_gpu > 1:
              return onmt.utils.Statistics.all_gather_stats(stat)
          return stat
  
      def _maybe_report_training(self, step, num_steps, learning_rate,
                                 report_stats):
          """
          Simple function to report training stats (if report_manager is set)
          see `onmt.utils.ReportManagerBase.report_training` for doc
          """
          if self.report_manager is not None:
              return self.report_manager.report_training(
                  step, num_steps, learning_rate, report_stats,
                  multigpu=self.n_gpu > 1)
  
      def _report_step(self, learning_rate, step, train_stats=None,
                       valid_stats=None):
          """
          Simple function to report stats (if report_manager is set)
          see `onmt.utils.ReportManagerBase.report_step` for doc
          """
          if self.report_manager is not None:
              return self.report_manager.report_step(
                  learning_rate, step, train_stats=train_stats,
                  valid_stats=valid_stats)
  
      def _maybe_save(self, step):
          """
          Save the model if a model saver is set
          """
          if self.model_saver is not None:
              self.model_saver.maybe_save(step)
  