"""Base class for models."""

import abc

import tensorflow as tf

from opennmt import optimizers
from opennmt import schedules
from opennmt.utils import exporters
from opennmt.utils import losses
from opennmt.utils import misc


class Model(tf.keras.layers.Layer):
  """Base class for models."""

  def __init__(self, examples_inputter):
    super(Model, self).__init__()
    self.examples_inputter = examples_inputter
    self.params = {}
    self._frozen_layers = False

  @property
  def unsupervised(self):
    """Unsupervised model."""
    return self.labels_inputter is None

  @property
  def features_inputter(self):
    """The inputter producing features."""
    return getattr(self.examples_inputter, "features_inputter", self.examples_inputter)

  @property
  def labels_inputter(self):
    """The inputter producing labels."""
    return getattr(self.examples_inputter, "labels_inputter", None)

  @property
  def trainable_weights(self):
    if not self._frozen_layers:
      self._frozen_layers = True
      freeze_layers = self.params.get("freeze_layers")
      if freeze_layers:
        if not isinstance(freeze_layers, list):
          freeze_layers = [freeze_layers]
        for layer_path in freeze_layers:
          layer = misc.index_structure(self, layer_path)
          layer.trainable = False
        tf.get_logger().info("%d weights are frozen by the freeze_layers parameter" % (
            len(self.non_trainable_weights)))
    return super(Model, self).trainable_weights

  @property
  def ctranslate2_spec(self):
    """The equivalent CTranslate2 model specification."""
    return None

  def auto_config(self, num_replicas=1):
    """Returns automatic configuration values specific to this model.

    Args:
      num_replicas: The number of synchronous model replicas used for the
        training.

    Returns:
      A partial training configuration.
    """
    _ = num_replicas
    return {}

  def initialize(self, data_config, params=None):
    """Initializes the model from the data configuration.

    Args:
      data_config: A dictionary containing the data configuration set
        by the user (e.g. vocabularies, tokenization, pretrained embeddings,
        etc.).
      params: A dictionary of hyperparameters.
    """
    if params is None:
      params = {}
    self.params.update(params)
    dropout = self.params.get("dropout")
    if dropout is not None:
      misc.set_dropout(self, dropout)
    self.examples_inputter.initialize(data_config)

  def build(self, input_shape):
    self.examples_inputter.build(input_shape)
    self.built = True

  @abc.abstractmethod
  def call(self, features, labels=None, training=None, step=None):  # pylint: disable=arguments-differ
    """Runs the model.

    Args:
      features: A nested structure of features ``tf.Tensor``.
      labels: A nested structure of labels ``tf.Tensor``.
      training: If ``True``, run in training mode.
      step: The current training step.

    Returns:
      A tuple containing,

      - The model outputs (usually unscaled probabilities).
      - The model predictions.
    """
    raise NotImplementedError()

  def infer(self, features):
    """Runs inference on :obj:`features`.

    This is a small convenience wrapper around
    :meth:`opennmt.models.Model.call`.

    Args:
      features: A nested structure of features ``tf.Tensor``.

    Returns:
      The model predictions.
    """
    _, predictions = self(features)
    if "index" in features:
      predictions["index"] = features["index"]
    return predictions

  def evaluate(self, features, labels):
    """Evaluates :obj:`features` predictions against `labels`.

    Args:
      features: A nested structure of features ``tf.Tensor``.
      labels: A nested structure of labels ``tf.Tensor``.

    Returns:
      A tuple with the loss and the model predictions.
    """
    outputs, predictions = self(features, labels=labels)
    loss = self.compute_loss(outputs, labels, training=False)
    return loss, predictions

  def score(self, features, labels):
    """Scores labels.

    Args:
      features: A nested structure of features ``tf.Tensor``.
      labels: A nested structure of labels ``tf.Tensor``.

    Returns:
      The score results.
    """
    raise NotImplementedError("This model does not define a score function")

  @abc.abstractmethod
  def compute_loss(self, outputs, labels, training=True):
    """Computes the loss.

    Args:
      outputs: The model outputs (usually unscaled probabilities).
      labels: The dict of labels ``tf.Tensor``.
      training: If ``True``, compute the loss for training.

    Returns:
      The loss or a tuple ``(numerator, train_denominator, stats_denominator)``
      to use a different normalization for training compared to reporting (e.g.
      batch-normalized for training vs. token-normalized for reporting).
    """
    raise NotImplementedError()

  def regularize_loss(self, loss, variables=None):
    """Regularizes the loss.

    Args:
      loss: The loss.
      variables: List of variables.

    Returns:
      The regularized loss.
    """
    if variables is None:
      variables = self.trainable_variables
    regularization = self.params.get("regularization")
    if regularization is not None:
      loss += losses.regularization_penalty(
          regularization["type"], regularization["scale"], variables)
    return loss

  def get_metrics(self):
    """Returns the metrics for this model.

    Returns:
      A dictionary of ``tf.keras.metrics.Metric`` metrics.
    """
    return None

  def update_metrics(self, metrics, predictions, labels):  # pylint: disable=unused-argument
    """Computes additional metrics on the predictions.

    Args:
      metrics: A dictionary of metrics to update.
      predictions: The model predictions.
      labels: The dict of labels ``tf.Tensor``.
    """
    return

  def get_optimizer(self):
    """Returns the optimizer for this model.

    Returns:
      A ``tf.keras.optimizers.Optimizer`` instance.
    """
    params = self.params
    learning_rate = tf.constant(params["learning_rate"], dtype=tf.float32)
    if params.get("decay_type") is not None:
      schedule_params = params.get("decay_params", {})
      learning_rate = schedules.make_learning_rate_schedule(
          learning_rate,
          params["decay_type"],
          schedule_params=schedule_params,
          start_step=params.get("start_decay_steps", 0),
          minimum_learning_rate=params.get("minimum_learning_rate", 0))
    optimizer_params = params.get("optimizer_params")
    if optimizer_params is None:
      optimizer_params = {}
    optimizer = optimizers.make_optimizer(
        params["optimizer"], learning_rate, **optimizer_params)
    return optimizer

  def serve_function(self):
    """Returns a function for serving this model.

    Returns:
      A ``tf.function``.
    """
    # Set name attribute of the input TensorSpec.
    input_signature = {
        name:tf.TensorSpec.from_spec(spec, name=name)
        for name, spec in self.features_inputter.input_signature().items()}

    @tf.function(input_signature=(input_signature,))
    def _run(features):
      features = self.features_inputter.make_features(features=features.copy())
      _, predictions = self(features)
      return predictions

    return _run

  def export(self, export_dir, exporter=None):
    """Exports the model for serving.

    Args:
      export_dir: The output directory.
      exporter: A :class:`opennmt.utils.Exporter` instance. Defaults to
        :class:`opennmt.utils.SavedModelExporter`.
    """
    if exporter is None:
      exporter = exporters.SavedModelExporter()
    exporter.export(self, export_dir)

  def create_variables(self, optimizer=None):
    """Creates the model variables by running it once.

    Args:
      optimizer: If set, also create the optimizer variables.
    """
    # Create input features from the input signatures. We remove the leading
    # batch dimension as sometimes assumed by make_features methods and set
    # unspecified dimensions to 1.
    features = tf.nest.map_structure(
        lambda spec: tf.fill(
            [dim or 1 for dim in spec.shape.as_list()[1:]],
            tf.constant("a" if spec.dtype is tf.string else 1, dtype=spec.dtype)),
        self.examples_inputter.input_signature())
    features = self.examples_inputter.make_features(features=features)

    # Add the batch dimension back before calling the model.
    features, labels = tf.nest.map_structure(lambda x: tf.expand_dims(x, 0), features)
    _ = self(features, labels=labels, training=True, step=0)

    if optimizer is not None:
      _ = optimizer.iterations
      optimizer._create_hypers()  # pylint: disable=protected-access
      optimizer._create_slots(self.trainable_variables)  # pylint: disable=protected-access

  def transfer_weights(self, new_model, new_optimizer=None, optimizer=None, ignore_weights=None):
    """Transfers weights (and optionally optimizer slots) from this model to
    another.

    This default implementation assumes that :obj:`self` and :obj:`new_model`
    have exactly the same variables. Subclasses can override this method to
    transfer weights to another model type or architecture. For example,
    :class:`opennmt.models.SequenceToSequence` can transfer weights to a model
    with a different vocabulary.

    All model and optimizer variables are expected to be initialized.

    Args:
      new_model: The new model to transfer weights to.
      new_optimizer: The new optimizer.
      optimizer: The optimizer used for the current model.
      ignore_weights: Optional list of weights to not transfer.
    """
    if type(self) is not type(new_model):
      raise ValueError("Transferring weights to another model type is not supported")
    if ignore_weights is None:
      ignore_weights = set()
    ignore_weights_ref = set(weight.experimental_ref() for weight in ignore_weights)
    weights = self.weights
    new_weights = new_model.weights
    for weight, new_weight in zip(weights, new_weights):
      if new_weight.experimental_ref() not in ignore_weights_ref:
        new_weight.assign(weight)
        if new_optimizer is not None and optimizer is not None:
          for slot_name in new_optimizer.get_slot_names():
            if slot_name not in optimizer.get_slot_names():
              continue
            new_slot = new_optimizer.get_slot(new_weight, slot_name)
            slot = optimizer.get_slot(weight, slot_name)
            new_slot.assign(slot)

  def map_v1_weights(self, weights):
    """Maps current weights to V1 weights.

    Args:
      weights: A nested dictionary following the scope names used in V1. The
        leaves are tuples with the variable value and optionally the optimizer
        slots.

    Returns:
      A list of tuples associating variables and their V1 equivalent.
    """
    raise NotImplementedError("This model can not restore V1 checkpoints")

  def export_assets(self, asset_dir):
    """Exports additional assets used by this model.

    Args:
      asset_dir: The directory where assets can be written.

    Returns:
      A dictionary of additional assets.
    """
    return self.examples_inputter.export_assets(asset_dir)

  def visualize(self, log_dir):
    """Setups model visualization (e.g. word embedding projections).

    Args:
      log_dir: The log directory.
    """
    self.features_inputter.visualize(self, log_dir)
    if not self.unsupervised:
      self.labels_inputter.visualize(self, log_dir)

  def print_prediction(self, prediction, params=None, stream=None):
    """Prints the model prediction.

    Args:
      prediction: The model prediction.
      params: (optional) Dictionary of formatting parameters.
      stream: (optional) The stream to print to.
    """
    _ = params
    print(prediction, file=stream)

  def print_score(self, score, params=None, stream=None):
    """Prints the score result.

    Args:
      score: The score result (output of :meth:`opennmt.models.Model.score`).
      params: (optional) Dictionary of formatting parameters.
      stream: (optional) The stream to print to.
    """
    _ = params
    print(score, file=stream)


class SequenceGenerator(Model):
  """Base class for models generating sequences."""

  @property
  def decoder_inputter(self):
    """The inputter used on the decoder side."""
    return (
        self.labels_inputter if not self.unsupervised
        else self.features_inputter)

  def score(self, features, labels):
    outputs, _ = self(features, labels=labels)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels["ids_out"], outputs["logits"])
    weights = tf.sequence_mask(labels["length"], dtype=cross_entropy.dtype)
    masked_cross_entropy = cross_entropy * weights
    scores = tf.reduce_sum(masked_cross_entropy, axis=1)
    results = {
        "cross_entropy": cross_entropy,
        "score": scores,
        "tokens": labels["tokens"],
        "length": self.decoder_inputter.get_length(labels, ignore_special_tokens=True)
    }
    if "attention" in outputs:
      results["attention"] = outputs["attention"]
    return results

  def print_score(self, score, params=None, stream=None):
    if params is None:
      params = {}
    length = score["length"]
    tokens = score["tokens"][:length]
    sentence = self.decoder_inputter.tokenizer.detokenize(tokens)
    token_level_scores = None
    attention = None
    if params.get("with_token_level"):
      token_level_scores = score["cross_entropy"][:length]
    if "attention" in score:
      attention = score["attention"][:length]
    alignment_type = params.get("with_alignments")
    sentence = misc.format_translation_output(
        sentence,
        score=score["score"],
        token_level_scores=token_level_scores,
        attention=attention,
        alignment_type=alignment_type)
    misc.print_as_bytes(sentence, stream=stream)
