#!/usr/bin/env python
# Copyright 2017, 2018 Google, Inc. 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.
# ==============================================================================
"""Trains the integrated LexNET classifier."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import lexnet_common
import lexnet_model
import path_model
from sklearn import metrics
import tensorflow as tf

tf.flags.DEFINE_string(
    'dataset_dir', 'datasets',
    'Dataset base directory')

tf.flags.DEFINE_string(
    'dataset', 'tratz/fine_grained',
    'Subdirectory containing the corpus directories: '
    'subdirectory of dataset_dir')

tf.flags.DEFINE_string(
    'corpus', 'wiki/random',
    'Subdirectory containing the corpus and split: '
    'subdirectory of dataset_dir/dataset')

tf.flags.DEFINE_string(
    'embeddings_base_path', 'embeddings',
    'Embeddings base directory')

tf.flags.DEFINE_string(
    'logdir', 'logdir',
    'Directory of model output files')

tf.flags.DEFINE_string('hparams', '', 'Hyper-parameters')

tf.flags.DEFINE_string(
    'input', 'integrated',
    'The model(dist/dist-nc/path/integrated/integrated-nc')

FLAGS = tf.flags.FLAGS


def main(_):
  # Pick up any one-off hyper-parameters.
  hparams = lexnet_model.LexNETModel.default_hparams()
  hparams.corpus = FLAGS.corpus
  hparams.input = FLAGS.input
  hparams.path_embeddings_file = 'path_embeddings/%s/%s' % (
      FLAGS.dataset, FLAGS.corpus)

  input_dir = hparams.input if hparams.input != 'path' else 'path_classifier'

  # Set the number of classes
  classes_filename = os.path.join(
      FLAGS.dataset_dir, FLAGS.dataset, 'classes.txt')
  with open(classes_filename) as f_in:
    classes = f_in.read().splitlines()

  hparams.num_classes = len(classes)
  print('Model will predict into %d classes' % hparams.num_classes)

  # Get the datasets
  train_set, val_set, test_set = (
      os.path.join(
          FLAGS.dataset_dir, FLAGS.dataset, FLAGS.corpus,
          filename + '.tfrecs.gz')
      for filename in ['train', 'val', 'test'])

  print('Running with hyper-parameters: {}'.format(hparams))

  # Load the instances
  print('Loading instances...')
  opts = tf.python_io.TFRecordOptions(
      compression_type=tf.python_io.TFRecordCompressionType.GZIP)
  train_instances = list(tf.python_io.tf_record_iterator(train_set, opts))
  val_instances = list(tf.python_io.tf_record_iterator(val_set, opts))
  test_instances = list(tf.python_io.tf_record_iterator(test_set, opts))

  # Load the word embeddings
  print('Loading word embeddings...')
  relata_embeddings, path_embeddings, nc_embeddings, path_to_index = (
      None, None, None, None)
  if hparams.input in ['dist', 'dist-nc', 'integrated', 'integrated-nc']:
    relata_embeddings = lexnet_common.load_word_embeddings(
        FLAGS.embeddings_base_path, hparams.relata_embeddings_file)

  if hparams.input in ['path', 'integrated', 'integrated-nc']:
    path_embeddings, path_to_index = path_model.load_path_embeddings(
        os.path.join(FLAGS.embeddings_base_path, hparams.path_embeddings_file),
        hparams.path_dim)

  if hparams.input in ['dist-nc', 'integrated-nc']:
    nc_embeddings = lexnet_common.load_word_embeddings(
        FLAGS.embeddings_base_path, hparams.nc_embeddings_file)

  # Define the graph and the model
  with tf.Graph().as_default():
    model = lexnet_model.LexNETModel(
        hparams, relata_embeddings, path_embeddings,
        nc_embeddings, path_to_index)

    # Initialize a session and start training
    session = tf.Session()
    session.run(tf.global_variables_initializer())

    # Initalize the path mapping
    if hparams.input in ['path', 'integrated', 'integrated-nc']:
      session.run(tf.tables_initializer())
      session.run(model.initialize_path_op, {
          model.path_initial_value_t: path_embeddings
      })

    # Initialize the NC embeddings
    if hparams.input in ['dist-nc', 'integrated-nc']:
      session.run(model.initialize_nc_op, {
          model.nc_initial_value_t: nc_embeddings
      })

    # Load the labels
    print('Loading labels...')
    train_labels = model.load_labels(session, train_instances)
    val_labels = model.load_labels(session, val_instances)
    test_labels = model.load_labels(session, test_instances)

    save_path = '{logdir}/results/{dataset}/{input}/{corpus}'.format(
        logdir=FLAGS.logdir, dataset=FLAGS.dataset,
        corpus=model.hparams.corpus, input=input_dir)

    if not os.path.exists(save_path):
      os.makedirs(save_path)

    # Train the model
    print('Training the model...')
    model.fit(session, train_instances, epoch_completed,
              val_instances, val_labels, save_path)

    # Print the best performance on the validation set
    print('Best performance on the validation set: F1=%.3f' %
          epoch_completed.best_f1)

    # Evaluate on the train and validation sets
    lexnet_common.full_evaluation(model, session, train_instances, train_labels,
                                  'Train', classes)
    lexnet_common.full_evaluation(model, session, val_instances, val_labels,
                                  'Validation', classes)
    test_predictions = lexnet_common.full_evaluation(
        model, session, test_instances, test_labels, 'Test', classes)

    # Write the test predictions to a file
    predictions_file = os.path.join(save_path, 'test_predictions.tsv')
    print('Saving test predictions to %s' % save_path)
    test_pairs = model.load_pairs(session, test_instances)
    lexnet_common.write_predictions(test_pairs, test_labels, test_predictions,
                                    classes, predictions_file)


def epoch_completed(model, session, epoch, epoch_loss,
                    val_instances, val_labels, save_path):
  """Runs every time an epoch completes.

  Print the performance on the validation set, and update the saved model if
  its performance is better on the previous ones. If the performance dropped,
  tell the training to stop.

  Args:
    model: The currently trained path-based model.
    session: The current TensorFlow session.
    epoch: The epoch number.
    epoch_loss: The current epoch loss.
    val_instances: The validation set instances (evaluation between epochs).
    val_labels: The validation set labels (for evaluation between epochs).
    save_path: Where to save the model.

  Returns:
    whether the training should stop.
  """
  stop_training = False

  # Evaluate on the validation set
  val_pred = model.predict(session, val_instances)
  precision, recall, f1, _ = metrics.precision_recall_fscore_support(
      val_labels, val_pred, average='weighted')
  print(
      'Epoch: %d/%d, Loss: %f, validation set: P: %.3f, R: %.3f, F1: %.3f\n' % (
          epoch + 1, model.hparams.num_epochs, epoch_loss,
          precision, recall, f1))

  # If the F1 is much smaller than the previous one, stop training. Else, if
  # it's bigger, save the model.
  if f1 < epoch_completed.best_f1 - 0.08:
    stop_training = True

  if f1 > epoch_completed.best_f1:
    saver = tf.train.Saver()
    checkpoint_filename = os.path.join(save_path, 'best.ckpt')
    print('Saving model in: %s' % checkpoint_filename)
    saver.save(session, checkpoint_filename)
    print('Model saved in file: %s' % checkpoint_filename)
    epoch_completed.best_f1 = f1

  return stop_training

epoch_completed.best_f1 = 0

if __name__ == '__main__':
  tf.app.run(main)