"""Binary for training translation models and decoding from them.

Running this program without --decode will download the WMT corpus into
the directory specified as --data_dir and tokenize it in a very basic way,
and then start training a model saving checkpoints to --train_dir.

Running with --decode starts an interactive loop so you can see how
the current checkpoint translates English sentences into French.

See the following papers for more information on neural translation models.
 * http://arxiv.org/abs/1409.3215
 * http://arxiv.org/abs/1409.0473
 * http://arxiv.org/pdf/1412.2007v2.pdf

 Adapted by Motoki Wu.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import os
import random
import sys
import time
import pdb

import tensorflow.python.platform

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

from tensorshake.translate import data_utils
from tensorshake.translate import seq2seq_model
from tensorshake.prepare_corpus import tokenizer

from tensorflow.python.platform import gfile

tf.app.flags.DEFINE_float("learning_rate", 0.5, "Learning rate.")
tf.app.flags.DEFINE_float("learning_rate_decay_factor", 0.99,
                          "Learning rate decays by this much.")
tf.app.flags.DEFINE_float("max_gradient_norm", 5.0,
                          "Clip gradients to this norm.")
tf.app.flags.DEFINE_integer("batch_size", 64,
                            "Batch size to use during training.")
tf.app.flags.DEFINE_integer("size", 1024, "Size of each model layer.")
tf.app.flags.DEFINE_integer("num_layers", 3, "Number of layers in the model.")
tf.app.flags.DEFINE_string("data_dir", "/tmp", "Data directory")
tf.app.flags.DEFINE_string("train_dir", "/tmp", "Training directory.")
tf.app.flags.DEFINE_integer("max_train_data_size", 0,
                            "Limit on the size of training data (0: no limit).")
tf.app.flags.DEFINE_integer("steps_per_checkpoint", 200,
                            "How many training steps to do per checkpoint.")
tf.app.flags.DEFINE_boolean("decode", False,
                            "Set to True for interactive decoding.")
tf.app.flags.DEFINE_boolean("self_test", False,
                            "Run a self-test if this is set to True.")

MODERN ~ Modern English
ORIGINAL ~ Shakespeare

TensorFlow examples goes from EN -> FR.
This script goes from MODERN -> ORIGINAL.

from tensorshake.prepare_corpus import MODERN_VOCAB_PATH, ORIGINAL_VOCAB_PATH
from tensorshake.prepare_corpus import MODERN_VOCAB_MAX, ORIGINAL_VOCAB_MAX

tf.app.flags.DEFINE_string("en_train", MODERN_TRAIN_IDS_PATH, "modern train ids path")
tf.app.flags.DEFINE_string("fr_train", ORIGINAL_TRAIN_IDS_PATH, "original train ids path")
tf.app.flags.DEFINE_string("en_dev", MODERN_DEV_IDS_PATH, "modern dev ids path")
tf.app.flags.DEFINE_string("fr_dev", ORIGINAL_DEV_IDS_PATH, "original dev ids path")
tf.app.flags.DEFINE_string("en_vocab", MODERN_VOCAB_PATH, "modern vocab path")
tf.app.flags.DEFINE_string("fr_vocab", ORIGINAL_VOCAB_PATH, "original vocab path")

tf.app.flags.DEFINE_integer("en_vocab_size", MODERN_VOCAB_MAX, "modern vocabulary size")
tf.app.flags.DEFINE_integer("fr_vocab_size", ORIGINAL_VOCAB_MAX, "original vocabulary size")

FLAGS = tf.app.flags.FLAGS

# We use a number of buckets and pad to the closest one for efficiency.
# See seq2seq_model.Seq2SeqModel for details of how they work.
_buckets = [(5, 10), (10, 15), (20, 25), (50, 50)] #, (70, 80), (180, 198)] # TODO: maybe filter out long sentences?

def read_data(source_path, target_path, max_size=None):
  """Read data from source and target files and put into buckets.

    source_path: path to the files with token-ids for the source language.
    target_path: path to the file with token-ids for the target language;
      it must be aligned with the source file: n-th line contains the desired
      output for n-th line from the source_path.
    max_size: maximum number of lines to read, all other will be ignored;
      if 0 or None, data files will be read completely (no limit).

    data_set: a list of length len(_buckets); data_set[n] contains a list of
      (source, target) pairs read from the provided data files that fit
      into the n-th bucket, i.e., such that len(source) < _buckets[n][0] and
      len(target) < _buckets[n][1]; source and target are lists of token-ids.
  data_set = [[] for _ in _buckets]
  with gfile.GFile(source_path, mode="r") as source_file:
    with gfile.GFile(target_path, mode="r") as target_file:
      source, target = source_file.readline(), target_file.readline()
      counter = 0
      while source and target and (not max_size or counter < max_size):
        counter += 1
        if counter % 100000 == 0:
          print("  reading data line %d" % counter)
        source_ids = [int(x) for x in source.split()][:50] # TODO: hmm
        target_ids = [int(x) for x in target.split()][:50]
        for bucket_id, (source_size, target_size) in enumerate(_buckets):
          if len(source_ids) < source_size and len(target_ids) < target_size:
            data_set[bucket_id].append([source_ids, target_ids])
        source, target = source_file.readline(), target_file.readline()
  return data_set

def create_model(session, forward_only):
  """Create translation model and initialize or load parameters in session."""
  print("en_vocab_size", FLAGS.en_vocab_size)
  print("fr_vocab_size", FLAGS.fr_vocab_size)
  model = seq2seq_model.Seq2SeqModel(
      FLAGS.en_vocab_size, FLAGS.fr_vocab_size, _buckets,
      FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
      FLAGS.learning_rate, FLAGS.learning_rate_decay_factor,
  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and gfile.Exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model.saver.restore(session, ckpt.model_checkpoint_path)
    print("Created model with fresh parameters.")
    # session.run(tf.variables.initialize_all_variables())
  return model

def train():
  """Train a en->fr translation model using WMT data."""
  # Prepare WMT data.
  # print("Preparing WMT data in %s" % FLAGS.data_dir)
  # en_train, fr_train, en_dev, fr_dev, _, _ = data_utils.prepare_wmt_data(
      # FLAGS.data_dir, FLAGS.en_vocab_size, FLAGS.fr_vocab_size)

  en_train = FLAGS.en_train
  fr_train = FLAGS.fr_train
  en_dev = FLAGS.en_dev
  fr_dev = FLAGS.fr_dev

  print("en_train", en_train)
  print("fr_train", fr_train)
  print("en_dev", en_dev)
  print("fr_dev", fr_dev)

  with tf.Session() as sess:
    # Create model.
    print("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
    model = create_model(sess, False)

    # Read data into buckets and compute their sizes.
    print ("Reading development and training data (limit: %d)."
           % FLAGS.max_train_data_size)
    dev_set = read_data(en_dev, fr_dev)
    train_set = read_data(en_train, fr_train, FLAGS.max_train_data_size)
    train_bucket_sizes = [len(train_set[b]) for b in xrange(len(_buckets))]
    train_total_size = float(sum(train_bucket_sizes))

    # A bucket scale is a list of increasing numbers from 0 to 1 that we'll use
    # to select a bucket. Length of [scale[i], scale[i+1]] is proportional to
    # the size if i-th training bucket, as used later.
    train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
                           for i in xrange(len(train_bucket_sizes))]

    # This is the training loop.
    step_time, loss = 0.0, 0.0
    current_step = 0
    previous_losses = []
    while True:
      # Choose a bucket according to data distribution. We pick a random number
      # in [0, 1] and use the corresponding interval in train_buckets_scale.
      random_number_01 = np.random.random_sample()
      bucket_id = min([i for i in xrange(len(train_buckets_scale))
                       if train_buckets_scale[i] > random_number_01])

      # Get a batch and make a step.
      start_time = time.time()
      encoder_inputs, decoder_inputs, target_weights = model.get_batch(
          train_set, bucket_id)
      # print("encoder_inputs", "-"*80)
      # print(encoder_inputs)
      # print("decoder_inputs", "-"*80)
      # print(decoder_inputs)
      # print("bucket_id", bucket_id)
      _, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
                                   target_weights, bucket_id, False)
      step_time += (time.time() - start_time) / FLAGS.steps_per_checkpoint
      loss += step_loss / FLAGS.steps_per_checkpoint
      current_step += 1
      # print("loss", loss)

      # Once in a while, we save checkpoint, print statistics, and run evals.
      if current_step % FLAGS.steps_per_checkpoint == 0:
        # Print statistics for the previous epoch.
        perplexity = math.exp(loss) if loss < 300 else float('inf')
        print ("global step %d learning rate %.4f step-time %.2f perplexity "
               "%.2f" % (model.global_step.eval(), model.learning_rate.eval(),
                         step_time, perplexity))
        # Decrease learning rate if no improvement was seen over last 3 times.
        if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
        # Save checkpoint and zero timer and loss.
        checkpoint_path = os.path.join(FLAGS.train_dir, "translate.ckpt")
        model.saver.save(sess, checkpoint_path, global_step=model.global_step)
        step_time, loss = 0.0, 0.0
        # Run evals on development set and print their perplexity.
        for bucket_id in xrange(len(_buckets)):
          encoder_inputs, decoder_inputs, target_weights = model.get_batch(
              dev_set, bucket_id)
          _, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
                                       target_weights, bucket_id, True)
          eval_ppx = math.exp(eval_loss) if eval_loss < 300 else float('inf')
          print("  eval: bucket %d perplexity %.2f" % (bucket_id, eval_ppx))

def decode():
  with tf.Session() as sess:
    # Create model and load parameters.
    model = create_model(sess, True)
    model.batch_size = 1  # We decode one sentence at a time.

    # Load vocabularies.
    # en_vocab_path = os.path.join(FLAGS.data_dir,
                                 # "vocab%d.en" % FLAGS.en_vocab_size)
    # fr_vocab_path = os.path.join(FLAGS.data_dir,
                                 # "vocab%d.fr" % FLAGS.fr_vocab_size)
    en_vocab_path = FLAGS.en_vocab
    fr_vocab_path = FLAGS.fr_vocab
    print("en_vocab_path", FLAGS.en_vocab)
    print("fr_vocab_path", FLAGS.fr_vocab)

    en_vocab, _ = data_utils.initialize_vocabulary(en_vocab_path)
    _, rev_fr_vocab = data_utils.initialize_vocabulary(fr_vocab_path)

    # Decode from standard input.
    sys.stdout.write("> ")
    sentence = sys.stdin.readline()
    while sentence:
      # Get token-ids for the input sentence.
      token_ids = data_utils.sentence_to_token_ids(sentence, en_vocab, tokenizer=tokenizer)
      # Which bucket does it belong to?
      bucket_id = min([b for b in xrange(len(_buckets))
                       if _buckets[b][0] > len(token_ids)])
      # Get a 1-element batch to feed the sentence to the model.
      encoder_inputs, decoder_inputs, target_weights = model.get_batch(
          {bucket_id: [(token_ids, [])]}, bucket_id)
      # Get output logits for the sentence.
      _, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs,
                                       target_weights, bucket_id, True)
      # TODO: change greedy decoder (either sampled or beam search)
      # This is a greedy decoder - outputs are just argmaxes of output_logits.
      # pdb.set_trace()
      outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]
      # If there is an EOS symbol in outputs, cut them at that point.
      if data_utils.EOS_ID in outputs:
        outputs = outputs[:outputs.index(data_utils.EOS_ID)]
      # Print out French sentence corresponding to outputs.
      print(" ".join([rev_fr_vocab[output] for output in outputs]))
      print("> ", end="")
      sentence = sys.stdin.readline()

def self_test():
  """Test the translation model."""
  with tf.Session() as sess:
    print("Self-test for neural translation model.")
    # Create model with vocabularies of 10, 2 small buckets, 2 layers of 32.
    model = seq2seq_model.Seq2SeqModel(10, 10, [(3, 3), (6, 6)], 32, 2,
                                       5.0, 32, 0.3, 0.99, num_samples=8)
    # sess.run(tf.variables.initialize_all_variables())

    # Fake data set for both the (3, 3) and (6, 6) bucket.
    data_set = ([([1, 1], [2, 2]), ([3, 3], [4]), ([5], [6])],
                [([1, 1, 1, 1, 1], [2, 2, 2, 2, 2]), ([3, 3, 3], [5, 6])])
    for _ in xrange(5):  # Train the fake model for 5 steps.
      bucket_id = random.choice([0, 1])
      encoder_inputs, decoder_inputs, target_weights = model.get_batch(
          data_set, bucket_id)
      model.step(sess, encoder_inputs, decoder_inputs, target_weights,
                 bucket_id, False)

def main(_):
  if FLAGS.self_test:
  elif FLAGS.decode:

if __name__ == "__main__":