"""
    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.
"""

from utils.loss import build_loss_compute
from utils.logging import logger
from utils.report_manager import build_report_manager
from utils.statistics import Statistics
from utils.distributed import all_gather_list, all_reduce_and_rescale_tensors
from inputters.dataset import make_features

def build_trainer(opt, device_id, model, fields,
                  optim, 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
      model_saver(:obj:`onmt.models.ModelSaverBase`): the utility object
          used to save the model
  """
  train_loss = build_loss_compute(
    model, fields["tgt"].vocab, opt)
  valid_loss = build_loss_compute(
    model, fields["tgt"].vocab, 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 = build_report_manager(opt)
  trainer = Trainer(model, train_loss, valid_loss, optim, trunc_size,
                         shard_size, 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
      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, train_loss, valid_loss, optim,
               trunc_size=0, shard_size=32,
               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 = 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.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 = Statistics()
    report_stats = 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):
        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(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 = Statistics()

    for batch in valid_iter:
      src = make_features(batch, 'src')
      _, src_lengths = batch.src

      tgt = make_features(batch, 'tgt')

      structure = make_features(batch, 'structure')
      structure = structure.transpose(0,1)
      structure = structure.transpose(1,2)

      # F-prop through the model.
      outputs, attns = self.model(src, tgt, structure, src_lengths)

      # Compute loss.
      batch_stats = self.valid_loss.monolithic_compute_loss(
        batch, outputs, attns)

      # Update statistics.
      stats.update(batch_stats)

    # 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
          src = make_features(batch, 'src')
          _, src_lengths = batch.src

          tgt_outer = make_features(batch, 'tgt')

          structure = make_features(batch, 'structure')
          structure = structure.transpose(0, 1)
          structure = structure.transpose(1, 2)

          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()
              outputs, attns = \
                  self.model(src, tgt, structure, src_lengths)

              # 3. Compute loss in shards for memory efficiency.
              batch_stats = self.train_loss.sharded_compute_loss(
                  batch, outputs, attns, j,
                  trunc_size, self.shard_size, normalization)
              total_stats.update(batch_stats)
              report_stats.update(batch_stats)

              # 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]
                      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]
              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 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)
