"""Checkpoint utilities."""

import copy
import os
import tempfile

import tensorflow as tf

from opennmt.utils import misc


class Checkpoint(object):
  """Wrapper around TensorFlow checkpoints utilities."""

  def __init__(self, model, optimizer=None, model_dir=None, keep_checkpoint_max=8):
    """Initializes the wrapper.

    Args:
      model: A :class:`opennmt.models.model.Model` to save.
      optimizer: The optimizer instance.
      model_dir: The directory where checkpoints will be saved. If not set, a
        temporary directory will be used.
      keep_checkpoint_max: The maximum number of checkpoints to keep.
    """
    if model_dir is None:
      model_dir = tempfile.mkdtemp()
    trackables = {}
    trackables["model"] = model
    if optimizer is not None:
      trackables["optimizer"] = optimizer
    self._model = model
    self._optimizer = optimizer
    self._model_dir = model_dir
    self._checkpoint = tf.train.Checkpoint(**trackables)
    self._checkpoint_manager = tf.train.CheckpointManager(
        self._checkpoint, model_dir, keep_checkpoint_max)

  @property
  def model(self):
    """The managed model."""
    return self._model

  @property
  def optimizer(self):
    """The managed optimizer."""
    return self._optimizer

  @property
  def model_dir(self):
    """The model directory."""
    return self._model_dir

  @property
  def last_saved_step(self):
    """The last training step that was saved."""
    latest_checkpoint = self._checkpoint_manager.latest_checkpoint
    if latest_checkpoint is None:
      return None
    return _get_step_from_checkpoint_prefix(latest_checkpoint)

  def save(self, step=None):
    """Saves a checkpoint.

    Args:
      step: The step to save for. If ``None``, get the value from ``optimizer.iterations``.

    Returns:
      The path to the saved checkpoint.
    """
    if step is None:
      step = self._optimizer.iterations
    path = self._checkpoint_manager.save(checkpoint_number=step)
    tf.get_logger().info("Saved checkpoint %s", path)
    return path

  def restore(self, checkpoint_path=None, weights_only=False):
    """Restores a checkpoint.

    Args:
      checkpoint_path: Path a checkpoint to restore. If not set, the latest
        checkpoint from :obj:`model_dir` will be restored.
      weights_only: Only restore model weights.

    Returns:
      Path to the restored checkpoint.
    """
    if weights_only:
      checkpoint = tf.train.Checkpoint(model=self._model)
    else:
      checkpoint = self._checkpoint
    if checkpoint_path is not None:
      if tf.io.gfile.isdir(checkpoint_path):
        checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
    elif self._checkpoint_manager.latest_checkpoint is not None:
      checkpoint_path = self._checkpoint_manager.latest_checkpoint
    if checkpoint_path is None:
      tf.get_logger().warning("No checkpoint to restore in %s", self._model_dir)
      return None
    if is_v1_checkpoint(checkpoint_path):
      tf.get_logger().info("Upgrading V1 checkpoint...")
      # Work with copies of model and optimizer as the downstream task might
      # need to create the variable differently (e.g. under a distribution
      # strategy scope).
      tmp_model = misc.clone_layer(self._model)
      tmp_optimizer = copy.deepcopy(self._optimizer) if self._optimizer is not None else None
      tmp_model.create_variables(optimizer=tmp_optimizer)
      step = _restore_v1_checkpoint(
          checkpoint_path, tmp_model, optimizer=tmp_optimizer)
      # Save an updated checkpoint in the model directory and restore this one instead.
      tmp_checkpoint = Checkpoint(
          tmp_model, optimizer=tmp_optimizer, model_dir=self._model_dir)
      checkpoint_path = tmp_checkpoint.save(step)
      return self.restore(checkpoint_path=checkpoint_path, weights_only=weights_only)
    load_status = checkpoint.restore(checkpoint_path)
    if weights_only:
      load_status.expect_partial()
    tf.get_logger().info("Restored checkpoint %s", checkpoint_path)
    return checkpoint_path


def _get_step_from_checkpoint_prefix(prefix):
  """Extracts the training step from the checkpoint file prefix."""
  return int(prefix.split("-")[-1])

def is_v1_checkpoint(checkpoint_path):
  """Returns ``True`` if the checkpoint at :obj:`checkpoint_path` has been
  trained with OpenNMT-tf v1.
  """
  if tf.io.gfile.isdir(checkpoint_path):
    checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
  return os.path.basename(checkpoint_path).startswith("model")

def get_checkpoint_variables(checkpoint_path):
  """Returns variables included in a checkpoint.

  Args:
    checkpoint_path: Path to the checkpoint.

  Returns:
    A dictionary mapping variables name to value.
  """
  reader = tf.train.load_checkpoint(checkpoint_path)
  return {
      name:reader.get_tensor(name)
      for name in reader.get_variable_to_shape_map().keys()}

def average_checkpoints(model_dir,
                        output_dir,
                        trackables,
                        max_count=8,
                        model_key="model"):
  """Averages object-based checkpoints.

  Args:
    model_dir: The directory containing checkpoints.
    output_dir: The directory that will contain the averaged checkpoint.
    trackables: A dictionary containing the trackable objects included in the
      checkpoint.
    max_count: The maximum number of checkpoints to average.
    model_key: The key in :obj:`trackables` that references the model.

  Returns:
    The path to the directory containing the averaged checkpoint.

  Raises:
    ValueError: if :obj:`output_dir` is the same as :obj:`model_dir`.
    ValueError: if a model is not found in :obj:`trackables` or is not already
      built.
    ValueError: if no checkpoints are found in :obj:`model_dir`.
  """
  if model_dir == output_dir:
    raise ValueError("Model and output directory must be different")
  model = trackables.get(model_key)
  if model is None:
    raise ValueError("%s not found in trackables %s" % (model_key, trackables))
  if not model.built:
    raise ValueError("The model should be built before calling this function")

  checkpoint = tf.train.Checkpoint(**trackables)
  checkpoint_manager = tf.train.CheckpointManager(checkpoint, model_dir, max_to_keep=None)

  checkpoints_path = checkpoint_manager.checkpoints
  if not checkpoints_path:
    raise ValueError("No checkpoints found in %s" % model_dir)
  if len(checkpoints_path) > max_count:
    checkpoints_path = checkpoints_path[-max_count:]
  num_checkpoints = len(checkpoints_path)
  last_step = _get_step_from_checkpoint_prefix(checkpoints_path[-1])

  # Get a map from variable names in the checkpoint to variables in the model.
  _, names_to_variables = misc.get_variables_name_mapping(model, root_key=model_key)

  tf.get_logger().info("Averaging %d checkpoints...", num_checkpoints)
  for i, checkpoint_path in enumerate(reversed(checkpoints_path)):
    tf.get_logger().info("Reading checkpoint %s...", checkpoint_path)
    if i == 0:
      checkpoint.restore(checkpoint_path).assert_existing_objects_matched()
      for variable in model.variables:
        variable.assign(variable / num_checkpoints)
    else:
      reader = tf.train.load_checkpoint(checkpoint_path)
      for path in reader.get_variable_to_shape_map().keys():
        if not path.startswith(model_key) or ".OPTIMIZER_SLOT" in path:
          continue
        variable = names_to_variables[path]
        value = reader.get_tensor(path)
        variable.assign_add(value / num_checkpoints)

  new_checkpoint_manager = tf.train.CheckpointManager(checkpoint, output_dir, max_to_keep=None)
  new_checkpoint_manager.save(checkpoint_number=last_step)
  return output_dir


_V1_OPTIM_SCOPE = "optim"
_V1_SLOTS_MAPPING = {
    "Adam": "m",
    "Adam_1": "v"
}


def _restore_v1_checkpoint(checkpoint_path, model, optimizer=None):
  v1_variables = get_checkpoint_variables(checkpoint_path)
  v1_structure = _variables_to_structure(v1_variables)
  step = v1_structure["global_step"]
  if optimizer is not None:
    optimizer.iterations.assign(step)
    if _V1_OPTIM_SCOPE in v1_structure:
      slots = v1_structure[_V1_OPTIM_SCOPE]
      del v1_structure[_V1_OPTIM_SCOPE]
      v1_structure = _merge_optimizer_slots(v1_structure, slots)
  mapping = model.map_v1_weights(v1_structure)
  existing_variables = set(variable.experimental_ref() for variable in model.variables)
  mapped_variables = set(variable.experimental_ref() for variable, _ in mapping)
  missing_mapping = existing_variables.difference(mapped_variables)
  if missing_mapping:
    raise ValueError("The following variables were not mapped: %s" % (
        ", ".join(var.name for var in missing_mapping)))
  # Assign each variable and possibly the optimizer slots.
  for v2_variable, v1_variable in mapping:
    if isinstance(v1_variable, tuple):
      v1_variable, v1_slots = v1_variable
    else:
      v1_slots = None
    v2_variable.assign(v1_variable)
    if v1_slots is not None:
      for slot_name, value in v1_slots.items():
        v2_slot = optimizer.get_slot(v2_variable, slot_name)
        v2_slot.assign(value)
  return step

def _variables_to_structure(variables):
  """Represents variables a nested dictionary with scope names as keys."""
  structure = {}
  for name, value in variables.items():
    fields = name.split("/")
    cur = structure
    for i, key in enumerate(fields):
      if key not in cur:
        if i + 1 == len(fields):
          cur[key] = value
          break
        cur[key] = {}
      cur = cur[key]
  return structure

def _merge_optimizer_slots(variables, slots):
  """Replaces leaves in the variables structure by tuples of
  (variable, dict of optimizer slots).
  """
  if isinstance(variables, dict):
    merged = {}
    for key, value in variables.items():
      if key not in slots:
        merged[key] = copy.deepcopy(value)
      else:
        merged[key] = _merge_optimizer_slots(value, slots[key])
    return merged
  else:
    new_slots = {}
    for name, value in slots.items():
      name = _V1_SLOTS_MAPPING.get(name)
      if name is None:
        # Just ignore the optimizer slots if their name is not listed.
        return variables
      new_slots[name] = value
    return (variables, new_slots)
