# Copyright 2019 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.
# ==============================================================================
"""BERT classification or regression finetuning runner in TF 2.x."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools
import json
import math
import os

# Import libraries
from absl import app
from absl import flags
from absl import logging
import gin
import tensorflow as tf
from official.modeling import performance
from official.nlp import optimization
from official.nlp.bert import bert_models
from official.nlp.bert import common_flags
from official.nlp.bert import configs as bert_configs
from official.nlp.bert import input_pipeline
from official.nlp.bert import model_saving_utils
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils

flags.DEFINE_enum(
    'mode', 'train_and_eval', ['train_and_eval', 'export_only', 'predict'],
    'One of {"train_and_eval", "export_only", "predict"}. `train_and_eval`: '
    'trains the model and evaluates in the meantime. '
    '`export_only`: will take the latest checkpoint inside '
    'model_dir and export a `SavedModel`. `predict`: takes a checkpoint and '
    'restores the model to output predictions on the test set.')
flags.DEFINE_string('train_data_path', None,
                    'Path to training data for BERT classifier.')
flags.DEFINE_string('eval_data_path', None,
                    'Path to evaluation data for BERT classifier.')
flags.DEFINE_string(
    'input_meta_data_path', None,
    'Path to file that contains meta data about input '
    'to be used for training and evaluation.')
flags.DEFINE_string('predict_checkpoint_path', None,
                    'Path to the checkpoint for predictions.')
flags.DEFINE_integer(
    'num_eval_per_epoch', 1,
    'Number of evaluations per epoch. The purpose of this flag is to provide '
    'more granular evaluation scores and checkpoints. For example, if original '
    'data has N samples and num_eval_per_epoch is n, then each epoch will be '
    'evaluated every N/n samples.')
flags.DEFINE_integer('train_batch_size', 32, 'Batch size for training.')
flags.DEFINE_integer('eval_batch_size', 32, 'Batch size for evaluation.')

common_flags.define_common_bert_flags()

FLAGS = flags.FLAGS

LABEL_TYPES_MAP = {'int': tf.int64, 'float': tf.float32}


def get_loss_fn(num_classes):
  """Gets the classification loss function."""

  def classification_loss_fn(labels, logits):
    """Classification loss."""
    labels = tf.squeeze(labels)
    log_probs = tf.nn.log_softmax(logits, axis=-1)
    one_hot_labels = tf.one_hot(
        tf.cast(labels, dtype=tf.int32), depth=num_classes, dtype=tf.float32)
    per_example_loss = -tf.reduce_sum(
        tf.cast(one_hot_labels, dtype=tf.float32) * log_probs, axis=-1)
    return tf.reduce_mean(per_example_loss)

  return classification_loss_fn


def get_dataset_fn(input_file_pattern,
                   max_seq_length,
                   global_batch_size,
                   is_training,
                   label_type=tf.int64,
                   include_sample_weights=False):
  """Gets a closure to create a dataset."""

  def _dataset_fn(ctx=None):
    """Returns tf.data.Dataset for distributed BERT pretraining."""
    batch_size = ctx.get_per_replica_batch_size(
        global_batch_size) if ctx else global_batch_size
    dataset = input_pipeline.create_classifier_dataset(
        tf.io.gfile.glob(input_file_pattern),
        max_seq_length,
        batch_size,
        is_training=is_training,
        input_pipeline_context=ctx,
        label_type=label_type,
        include_sample_weights=include_sample_weights)
    return dataset

  return _dataset_fn


def run_bert_classifier(strategy,
                        bert_config,
                        input_meta_data,
                        model_dir,
                        epochs,
                        steps_per_epoch,
                        steps_per_loop,
                        eval_steps,
                        warmup_steps,
                        initial_lr,
                        init_checkpoint,
                        train_input_fn,
                        eval_input_fn,
                        training_callbacks=True,
                        custom_callbacks=None,
                        custom_metrics=None):
  """Run BERT classifier training using low-level API."""
  max_seq_length = input_meta_data['max_seq_length']
  num_classes = input_meta_data.get('num_labels', 1)
  is_regression = num_classes == 1

  def _get_classifier_model():
    """Gets a classifier model."""
    classifier_model, core_model = (
        bert_models.classifier_model(
            bert_config,
            num_classes,
            max_seq_length,
            hub_module_url=FLAGS.hub_module_url,
            hub_module_trainable=FLAGS.hub_module_trainable))
    optimizer = optimization.create_optimizer(initial_lr,
                                              steps_per_epoch * epochs,
                                              warmup_steps, FLAGS.end_lr,
                                              FLAGS.optimizer_type)
    classifier_model.optimizer = performance.configure_optimizer(
        optimizer,
        use_float16=common_flags.use_float16(),
        use_graph_rewrite=common_flags.use_graph_rewrite())
    return classifier_model, core_model

  # tf.keras.losses objects accept optional sample_weight arguments (eg. coming
  # from the dataset) to compute weighted loss, as used for the regression
  # tasks. The classification tasks, using the custom get_loss_fn don't accept
  # sample weights though.
  loss_fn = (tf.keras.losses.MeanSquaredError() if is_regression
             else get_loss_fn(num_classes))

  # Defines evaluation metrics function, which will create metrics in the
  # correct device and strategy scope.
  if custom_metrics:
    metric_fn = custom_metrics
  elif is_regression:
    metric_fn = functools.partial(
        tf.keras.metrics.MeanSquaredError,
        'mean_squared_error',
        dtype=tf.float32)
  else:
    metric_fn = functools.partial(
        tf.keras.metrics.SparseCategoricalAccuracy,
        'accuracy',
        dtype=tf.float32)

  # Start training using Keras compile/fit API.
  logging.info('Training using TF 2.x Keras compile/fit API with '
               'distribution strategy.')
  return run_keras_compile_fit(
      model_dir,
      strategy,
      _get_classifier_model,
      train_input_fn,
      eval_input_fn,
      loss_fn,
      metric_fn,
      init_checkpoint,
      epochs,
      steps_per_epoch,
      steps_per_loop,
      eval_steps,
      training_callbacks=training_callbacks,
      custom_callbacks=custom_callbacks)


def run_keras_compile_fit(model_dir,
                          strategy,
                          model_fn,
                          train_input_fn,
                          eval_input_fn,
                          loss_fn,
                          metric_fn,
                          init_checkpoint,
                          epochs,
                          steps_per_epoch,
                          steps_per_loop,
                          eval_steps,
                          training_callbacks=True,
                          custom_callbacks=None):
  """Runs BERT classifier model using Keras compile/fit API."""

  with strategy.scope():
    training_dataset = train_input_fn()
    evaluation_dataset = eval_input_fn() if eval_input_fn else None
    bert_model, sub_model = model_fn()
    optimizer = bert_model.optimizer

    if init_checkpoint:
      checkpoint = tf.train.Checkpoint(model=sub_model)
      checkpoint.restore(init_checkpoint).assert_existing_objects_matched()

    if not isinstance(metric_fn, (list, tuple)):
      metric_fn = [metric_fn]
    bert_model.compile(
        optimizer=optimizer,
        loss=loss_fn,
        metrics=[fn() for fn in metric_fn],
        experimental_steps_per_execution=steps_per_loop)

    summary_dir = os.path.join(model_dir, 'summaries')
    summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
    checkpoint = tf.train.Checkpoint(model=bert_model, optimizer=optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        directory=model_dir,
        max_to_keep=None,
        step_counter=optimizer.iterations,
        checkpoint_interval=0)
    checkpoint_callback = keras_utils.SimpleCheckpoint(checkpoint_manager)

    if training_callbacks:
      if custom_callbacks is not None:
        custom_callbacks += [summary_callback, checkpoint_callback]
      else:
        custom_callbacks = [summary_callback, checkpoint_callback]

    history = bert_model.fit(
        x=training_dataset,
        validation_data=evaluation_dataset,
        steps_per_epoch=steps_per_epoch,
        epochs=epochs,
        validation_steps=eval_steps,
        callbacks=custom_callbacks)
    stats = {'total_training_steps': steps_per_epoch * epochs}
    if 'loss' in history.history:
      stats['train_loss'] = history.history['loss'][-1]
    if 'val_accuracy' in history.history:
      stats['eval_metrics'] = history.history['val_accuracy'][-1]
    return bert_model, stats


def get_predictions_and_labels(strategy,
                               trained_model,
                               eval_input_fn,
                               return_probs=False):
  """Obtains predictions of trained model on evaluation data.

  Note that list of labels is returned along with the predictions because the
  order changes on distributing dataset over TPU pods.

  Args:
    strategy: Distribution strategy.
    trained_model: Trained model with preloaded weights.
    eval_input_fn: Input function for evaluation data.
    return_probs: Whether to return probabilities of classes.

  Returns:
    predictions: List of predictions.
    labels: List of gold labels corresponding to predictions.
  """

  @tf.function
  def test_step(iterator):
    """Computes predictions on distributed devices."""

    def _test_step_fn(inputs):
      """Replicated predictions."""
      inputs, labels = inputs
      logits = trained_model(inputs, training=False)
      probabilities = tf.nn.softmax(logits)
      return probabilities, labels

    outputs, labels = strategy.run(_test_step_fn, args=(next(iterator),))
    # outputs: current batch logits as a tuple of shard logits
    outputs = tf.nest.map_structure(strategy.experimental_local_results,
                                    outputs)
    labels = tf.nest.map_structure(strategy.experimental_local_results, labels)
    return outputs, labels

  def _run_evaluation(test_iterator):
    """Runs evaluation steps."""
    preds, golds = list(), list()
    try:
      with tf.experimental.async_scope():
        while True:
          probabilities, labels = test_step(test_iterator)
          for cur_probs, cur_labels in zip(probabilities, labels):
            if return_probs:
              preds.extend(cur_probs.numpy().tolist())
            else:
              preds.extend(tf.math.argmax(cur_probs, axis=1).numpy())
            golds.extend(cur_labels.numpy().tolist())
    except (StopIteration, tf.errors.OutOfRangeError):
      tf.experimental.async_clear_error()
    return preds, golds

  test_iter = iter(
      strategy.experimental_distribute_datasets_from_function(eval_input_fn))
  predictions, labels = _run_evaluation(test_iter)

  return predictions, labels


def export_classifier(model_export_path, input_meta_data, bert_config,
                      model_dir):
  """Exports a trained model as a `SavedModel` for inference.

  Args:
    model_export_path: a string specifying the path to the SavedModel directory.
    input_meta_data: dictionary containing meta data about input and model.
    bert_config: Bert configuration file to define core bert layers.
    model_dir: The directory where the model weights and training/evaluation
      summaries are stored.

  Raises:
    Export path is not specified, got an empty string or None.
  """
  if not model_export_path:
    raise ValueError('Export path is not specified: %s' % model_export_path)
  if not model_dir:
    raise ValueError('Export path is not specified: %s' % model_dir)

  # Export uses float32 for now, even if training uses mixed precision.
  tf.keras.mixed_precision.experimental.set_policy('float32')
  classifier_model = bert_models.classifier_model(
      bert_config,
      input_meta_data.get('num_labels', 1),
      hub_module_url=FLAGS.hub_module_url,
      hub_module_trainable=False)[0]

  model_saving_utils.export_bert_model(
      model_export_path, model=classifier_model, checkpoint_dir=model_dir)


def run_bert(strategy,
             input_meta_data,
             model_config,
             train_input_fn=None,
             eval_input_fn=None,
             init_checkpoint=None,
             custom_callbacks=None,
             custom_metrics=None):
  """Run BERT training."""
  # Enables XLA in Session Config. Should not be set for TPU.
  keras_utils.set_session_config(FLAGS.enable_xla)
  performance.set_mixed_precision_policy(common_flags.dtype())

  epochs = FLAGS.num_train_epochs * FLAGS.num_eval_per_epoch
  train_data_size = (
      input_meta_data['train_data_size'] // FLAGS.num_eval_per_epoch)
  steps_per_epoch = int(train_data_size / FLAGS.train_batch_size)
  warmup_steps = int(epochs * train_data_size * 0.1 / FLAGS.train_batch_size)
  eval_steps = int(
      math.ceil(input_meta_data['eval_data_size'] / FLAGS.eval_batch_size))

  if not strategy:
    raise ValueError('Distribution strategy has not been specified.')

  if not custom_callbacks:
    custom_callbacks = []

  if FLAGS.log_steps:
    custom_callbacks.append(
        keras_utils.TimeHistory(
            batch_size=FLAGS.train_batch_size,
            log_steps=FLAGS.log_steps,
            logdir=FLAGS.model_dir))

  trained_model, _ = run_bert_classifier(
      strategy,
      model_config,
      input_meta_data,
      FLAGS.model_dir,
      epochs,
      steps_per_epoch,
      FLAGS.steps_per_loop,
      eval_steps,
      warmup_steps,
      FLAGS.learning_rate,
      init_checkpoint or FLAGS.init_checkpoint,
      train_input_fn,
      eval_input_fn,
      custom_callbacks=custom_callbacks,
      custom_metrics=custom_metrics)

  if FLAGS.model_export_path:
    model_saving_utils.export_bert_model(
        FLAGS.model_export_path, model=trained_model)
  return trained_model


def custom_main(custom_callbacks=None, custom_metrics=None):
  """Run classification or regression.

  Args:
    custom_callbacks: list of tf.keras.Callbacks passed to training loop.
    custom_metrics: list of metrics passed to the training loop.
  """
  gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_param)

  with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
    input_meta_data = json.loads(reader.read().decode('utf-8'))
  label_type = LABEL_TYPES_MAP[input_meta_data.get('label_type', 'int')]
  include_sample_weights = input_meta_data.get('has_sample_weights', False)

  if not FLAGS.model_dir:
    FLAGS.model_dir = '/tmp/bert20/'

  bert_config = bert_configs.BertConfig.from_json_file(FLAGS.bert_config_file)

  if FLAGS.mode == 'export_only':
    export_classifier(FLAGS.model_export_path, input_meta_data, bert_config,
                      FLAGS.model_dir)
    return

  strategy = distribution_utils.get_distribution_strategy(
      distribution_strategy=FLAGS.distribution_strategy,
      num_gpus=FLAGS.num_gpus,
      tpu_address=FLAGS.tpu)
  eval_input_fn = get_dataset_fn(
      FLAGS.eval_data_path,
      input_meta_data['max_seq_length'],
      FLAGS.eval_batch_size,
      is_training=False,
      label_type=label_type,
      include_sample_weights=include_sample_weights)

  if FLAGS.mode == 'predict':
    with strategy.scope():
      classifier_model = bert_models.classifier_model(
          bert_config, input_meta_data['num_labels'])[0]
      checkpoint = tf.train.Checkpoint(model=classifier_model)
      latest_checkpoint_file = (
          FLAGS.predict_checkpoint_path or
          tf.train.latest_checkpoint(FLAGS.model_dir))
      assert latest_checkpoint_file
      logging.info('Checkpoint file %s found and restoring from '
                   'checkpoint', latest_checkpoint_file)
      checkpoint.restore(
          latest_checkpoint_file).assert_existing_objects_matched()
      preds, _ = get_predictions_and_labels(
          strategy, classifier_model, eval_input_fn, return_probs=True)
    output_predict_file = os.path.join(FLAGS.model_dir, 'test_results.tsv')
    with tf.io.gfile.GFile(output_predict_file, 'w') as writer:
      logging.info('***** Predict results *****')
      for probabilities in preds:
        output_line = '\t'.join(
            str(class_probability)
            for class_probability in probabilities) + '\n'
        writer.write(output_line)
    return

  if FLAGS.mode != 'train_and_eval':
    raise ValueError('Unsupported mode is specified: %s' % FLAGS.mode)
  train_input_fn = get_dataset_fn(
      FLAGS.train_data_path,
      input_meta_data['max_seq_length'],
      FLAGS.train_batch_size,
      is_training=True,
      label_type=label_type,
      include_sample_weights=include_sample_weights)
  run_bert(
      strategy,
      input_meta_data,
      bert_config,
      train_input_fn,
      eval_input_fn,
      custom_callbacks=custom_callbacks,
      custom_metrics=custom_metrics)


def main(_):
  custom_main(custom_callbacks=None, custom_metrics=None)


if __name__ == '__main__':
  flags.mark_flag_as_required('bert_config_file')
  flags.mark_flag_as_required('input_meta_data_path')
  flags.mark_flag_as_required('model_dir')
  app.run(main)
