# Copyright 2025 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Run NHNet model training and eval."""

import os

from absl import app
from absl import flags
from absl import logging
from six.moves import zip
import tensorflow as tf, tf_keras

from official.common import distribute_utils
from official.legacy.transformer import metrics as transformer_metrics
from official.modeling.hyperparams import params_dict
from official.projects.nhnet import evaluation
from official.projects.nhnet import input_pipeline
from official.projects.nhnet import models
from official.projects.nhnet import optimizer
from official.utils.misc import keras_utils

FLAGS = flags.FLAGS


def define_flags():
  """Defines command line flags used by NHNet trainer."""
  ## Required parameters
  flags.DEFINE_enum("mode", "train", ["train", "eval", "train_and_eval"],
                    "Execution mode.")
  flags.DEFINE_string("train_file_pattern", "", "Train file pattern.")
  flags.DEFINE_string("eval_file_pattern", "", "Eval file pattern.")
  flags.DEFINE_string(
      "model_dir", None,
      "The output directory where the model checkpoints will be written.")

  # Model training specific flags.
  flags.DEFINE_enum(
      "distribution_strategy", "mirrored", ["tpu", "mirrored"],
      "Distribution Strategy type to use for training. `tpu` uses TPUStrategy "
      "for running on TPUs, `mirrored` uses GPUs with single host.")
  flags.DEFINE_string("tpu", "", "TPU address to connect to.")
  flags.DEFINE_string(
      "init_checkpoint", None,
      "Initial checkpoint (usually from a pre-trained BERT model).")
  flags.DEFINE_integer("train_steps", 100000, "Max train steps")
  flags.DEFINE_integer("eval_steps", 32, "Number of eval steps per run.")
  flags.DEFINE_integer("eval_timeout", 3000, "Timeout waiting for checkpoints.")
  flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
  flags.DEFINE_integer("eval_batch_size", 4, "Total batch size for evaluation.")
  flags.DEFINE_integer(
      "steps_per_loop", 1000,
      "Number of steps per graph-mode loop. Only training step "
      "happens inside the loop.")
  flags.DEFINE_integer("checkpoint_interval", 2000, "Checkpointing interval.")
  flags.DEFINE_integer("len_title", 15, "Title length.")
  flags.DEFINE_integer("len_passage", 200, "Passage length.")
  flags.DEFINE_integer("num_encoder_layers", 12,
                       "Number of hidden layers of encoder.")
  flags.DEFINE_integer("num_decoder_layers", 12,
                       "Number of hidden layers of decoder.")
  flags.DEFINE_string("model_type", "nhnet",
                      "Model type to choose a model configuration.")
  flags.DEFINE_integer(
      "num_nhnet_articles", 5,
      "Maximum number of articles in NHNet, only used when model_type=nhnet")
  flags.DEFINE_string(
      "params_override",
      default=None,
      help=("a YAML/JSON string or a YAML file which specifies additional "
            "overrides over the default parameters"))
  # Enables MLIR-based TF/XLA bridge. This is part of a soft rollout and will
  # eventually be the Google-wide default.
  flags.DEFINE_bool("enable_mlir_bridge", True,
                    "Use MLIR TF/XLA bridge (experimental).")


# pylint: disable=protected-access


class Trainer(tf_keras.Model):
  """A training only model."""

  def __init__(self, model, params):
    super(Trainer, self).__init__()
    self.model = model
    self.params = params
    self._num_replicas_in_sync = tf.distribute.get_strategy(
    ).num_replicas_in_sync

  def call(self, inputs, mode="train"):
    return self.model(inputs, mode)

  def train_step(self, inputs):
    """The logic for one training step."""
    with tf.GradientTape() as tape:
      logits, _, _ = self(inputs, mode="train", training=True)
      targets = models.remove_sos_from_seq(inputs["target_ids"],
                                           self.params.pad_token_id)
      loss = transformer_metrics.transformer_loss(logits, targets,
                                                  self.params.label_smoothing,
                                                  self.params.vocab_size)
      # Scales the loss, which results in using the average loss across all
      # of the replicas for backprop.
      scaled_loss = loss / self._num_replicas_in_sync

    tvars = self.trainable_variables
    grads = tape.gradient(scaled_loss, tvars)
    self.optimizer.apply_gradients(list(zip(grads, tvars)))
    if isinstance(self.optimizer, tf_keras.optimizers.experimental.Optimizer):
      learning_rate = self.optimizer.learning_rate
    else:
      learning_rate = self.optimizer._decayed_lr(var_dtype=tf.float32)
    return {
        "training_loss": loss,
        "learning_rate": learning_rate,
    }


def train(params, strategy, dataset=None):
  """Runs training."""

  if not dataset:
    dataset = input_pipeline.get_input_dataset(
        FLAGS.train_file_pattern,
        FLAGS.train_batch_size,
        params,
        is_training=True,
        strategy=strategy)

  with strategy.scope():
    model = models.create_model(
        FLAGS.model_type, params, init_checkpoint=FLAGS.init_checkpoint)
    opt = optimizer.create_optimizer(params)
    trainer = Trainer(model, params)

    trainer.compile(
        optimizer=opt,
        steps_per_execution=FLAGS.steps_per_loop)
    summary_dir = os.path.join(FLAGS.model_dir, "summaries")
    summary_callback = tf_keras.callbacks.TensorBoard(
        summary_dir, update_freq=max(100, FLAGS.steps_per_loop))
    checkpoint = tf.train.Checkpoint(
        model=model, optimizer=opt, global_step=opt.iterations)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        directory=FLAGS.model_dir,
        max_to_keep=10,
        step_counter=opt.iterations,
        checkpoint_interval=FLAGS.checkpoint_interval)
    if checkpoint_manager.restore_or_initialize():
      logging.info("Training restored from the checkpoints in: %s",
                   FLAGS.model_dir)
    checkpoint_callback = keras_utils.SimpleCheckpoint(checkpoint_manager)

  # Trains the model.
  steps_per_epoch = min(FLAGS.train_steps, FLAGS.checkpoint_interval)
  epochs = FLAGS.train_steps // steps_per_epoch
  history = trainer.fit(
      x=dataset,
      steps_per_epoch=steps_per_epoch,
      epochs=epochs,
      callbacks=[summary_callback, checkpoint_callback],
      verbose=2)
  train_hist = history.history
  # Gets final loss from training.
  stats = dict(training_loss=float(train_hist["training_loss"][-1]))
  return stats


def run():
  """Runs NHNet using Keras APIs."""
  strategy = distribute_utils.get_distribution_strategy(
      distribution_strategy=FLAGS.distribution_strategy, tpu_address=FLAGS.tpu)
  if strategy:
    logging.info("***** Number of cores used : %d",
                 strategy.num_replicas_in_sync)

  params = models.get_model_params(FLAGS.model_type)
  params = params_dict.override_params_dict(
      params, FLAGS.params_override, is_strict=True)
  params.override(
      {
          "len_title":
              FLAGS.len_title,
          "len_passage":
              FLAGS.len_passage,
          "num_hidden_layers":
              FLAGS.num_encoder_layers,
          "num_decoder_layers":
              FLAGS.num_decoder_layers,
          "passage_list":
              [chr(ord("b") + i) for i in range(FLAGS.num_nhnet_articles)],
      },
      is_strict=False)
  stats = {}
  if "train" in FLAGS.mode:
    stats = train(params, strategy)
  if "eval" in FLAGS.mode:
    timeout = 0 if FLAGS.mode == "train_and_eval" else FLAGS.eval_timeout
    # Uses padded decoding for TPU. Always uses cache.
    padded_decode = isinstance(strategy, tf.distribute.TPUStrategy)
    params.override({
        "padded_decode": padded_decode,
    }, is_strict=False)
    stats = evaluation.continuous_eval(
        strategy,
        params,
        model_type=FLAGS.model_type,
        eval_file_pattern=FLAGS.eval_file_pattern,
        batch_size=FLAGS.eval_batch_size,
        eval_steps=FLAGS.eval_steps,
        model_dir=FLAGS.model_dir,
        timeout=timeout)
  return stats


def main(_):
  stats = run()
  if stats:
    logging.info("Stats:\n%s", stats)

if __name__ == "__main__":
  define_flags()
  app.run(main)
