#!/usr/bin/env python

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# -*- coding: utf-8 -*-

import sys
import os
import mxnet as mx
import numpy as np
import argparse
import logging
import data_helpers

logging.basicConfig(level=logging.DEBUG)

parser = argparse.ArgumentParser(description="CNN for text classification",
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--pretrained-embedding', type=bool, default=False,
                    help='use pre-trained word2vec')
parser.add_argument('--num-embed', type=int, default=300,
                    help='embedding layer size')
parser.add_argument('--gpus', type=str, default='',
                    help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu. ')
parser.add_argument('--kv-store', type=str, default='local',
                    help='key-value store type')
parser.add_argument('--num-epochs', type=int, default=200,
                    help='max num of epochs')
parser.add_argument('--batch-size', type=int, default=50,
                    help='the batch size.')
parser.add_argument('--optimizer', type=str, default='rmsprop',
                    help='the optimizer type')
parser.add_argument('--lr', type=float, default=0.0005,
                    help='initial learning rate')
parser.add_argument('--dropout', type=float, default=0.0,
                    help='dropout rate')
parser.add_argument('--disp-batches', type=int, default=50,
                    help='show progress for every n batches')
parser.add_argument('--save-period', type=int, default=10,
                    help='save checkpoint for every n epochs')

def save_model():
    if not os.path.exists("checkpoint"):
        os.mkdir("checkpoint")
    return mx.callback.do_checkpoint("checkpoint/checkpoint", args.save_period)

def data_iter(batch_size, num_embed, pre_trained_word2vec=False):
    print('Loading data...')
    if pre_trained_word2vec:
        word2vec = data_helpers.load_pretrained_word2vec('data/rt.vec')
        x, y = data_helpers.load_data_with_word2vec(word2vec)
        # reshpae for convolution input
        x = np.reshape(x, (x.shape[0], 1, x.shape[1], x.shape[2]))
        embed_size = x.shape[-1]
        sentence_size = x.shape[2]
        vocab_size = -1
    else:
        x, y, vocab, vocab_inv = data_helpers.load_data()
        embed_size = num_embed
        sentence_size = x.shape[1]
        vocab_size = len(vocab)

    # randomly shuffle data
    np.random.seed(10)
    shuffle_indices = np.random.permutation(np.arange(len(y)))
    x_shuffled = x[shuffle_indices]
    y_shuffled = y[shuffle_indices]

    # split train/valid set
    x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
    y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
    print('Train/Valid split: %d/%d' % (len(y_train), len(y_dev)))
    print('train shape:', x_train.shape)
    print('valid shape:', x_dev.shape)
    print('sentence max words', sentence_size)
    print('embedding size', embed_size)
    print('vocab size', vocab_size)

    train = mx.io.NDArrayIter(
        x_train, y_train, batch_size, shuffle=True)
    valid = mx.io.NDArrayIter(
        x_dev, y_dev, batch_size)

    return (train, valid, sentence_size, embed_size, vocab_size)

def sym_gen(batch_size, sentence_size, num_embed, vocab_size,
            num_label=2, filter_list=[3, 4, 5], num_filter=100,
            dropout=0.0, pre_trained_word2vec=False):
    input_x = mx.sym.Variable('data')
    input_y = mx.sym.Variable('softmax_label')

    # embedding layer
    if not pre_trained_word2vec:
        embed_layer = mx.sym.Embedding(data=input_x, input_dim=vocab_size, output_dim=num_embed, name='vocab_embed')
        conv_input = mx.sym.Reshape(data=embed_layer, target_shape=(batch_size, 1, sentence_size, num_embed))
    else:
        conv_input = input_x

    # create convolution + (max) pooling layer for each filter operation
    pooled_outputs = []
    for i, filter_size in enumerate(filter_list):
        convi = mx.sym.Convolution(data=conv_input, kernel=(filter_size, num_embed), num_filter=num_filter)
        relui = mx.sym.Activation(data=convi, act_type='relu')
        pooli = mx.sym.Pooling(data=relui, pool_type='max', kernel=(sentence_size - filter_size + 1, 1), stride=(1,1))
        pooled_outputs.append(pooli)

    # combine all pooled outputs
    total_filters = num_filter * len(filter_list)
    concat = mx.sym.Concat(*pooled_outputs, dim=1)
    h_pool = mx.sym.Reshape(data=concat, target_shape=(batch_size, total_filters))

    # dropout layer
    if dropout > 0.0:
        h_drop = mx.sym.Dropout(data=h_pool, p=dropout)
    else:
        h_drop = h_pool

    # fully connected
    cls_weight = mx.sym.Variable('cls_weight')
    cls_bias = mx.sym.Variable('cls_bias')

    fc = mx.sym.FullyConnected(data=h_drop, weight=cls_weight, bias=cls_bias, num_hidden=num_label)

    # softmax output
    sm = mx.sym.SoftmaxOutput(data=fc, label=input_y, name='softmax')

    return sm, ('data',), ('softmax_label',)

def train(symbol, train_iter, valid_iter, data_names, label_names):
    devs = mx.cpu() if args.gpus is None or args.gpus is '' else [
        mx.gpu(int(i)) for i in args.gpus.split(',')]
    module = mx.mod.Module(symbol, data_names=data_names, label_names=label_names, context=devs)
    module.fit(train_data = train_iter,
            eval_data = valid_iter,
            eval_metric = 'acc',
            kvstore = args.kv_store,
            optimizer = args.optimizer,
            optimizer_params = { 'learning_rate': args.lr },
            initializer = mx.initializer.Uniform(0.1),
            num_epoch = args.num_epochs,
            batch_end_callback = mx.callback.Speedometer(args.batch_size, args.disp_batches),
            epoch_end_callback = save_model())

if __name__ == '__main__':
    # parse args
    args = parser.parse_args()

    # data iter
    train_iter, valid_iter, sentence_size, embed_size, vocab_size = data_iter(args.batch_size,
                                                                args.num_embed,
                                                                args.pretrained_embedding)
    # network symbol
    symbol, data_names, label_names = sym_gen(args.batch_size,
                                            sentence_size,
                                            embed_size,
                                            vocab_size,
                                            num_label=2, filter_list=[3, 4, 5], num_filter=100,
                                            dropout=args.dropout, pre_trained_word2vec=args.pretrained_embedding)
    # train cnn model
    train(symbol, train_iter, valid_iter, data_names, label_names)
