import sys
import time

import numpy as np
import math

import paddle.fluid as fluid
import paddle.v2 as paddle

import utils


def network(src, dst, vocab_size, hid_size, init_low_bound, init_high_bound):
    """ network definition """
    emb_lr_x = 10.0
    gru_lr_x = 1.0
    fc_lr_x = 1.0
    emb = fluid.layers.embedding(
        input=src,
        size=[vocab_size, hid_size],
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Uniform(
                low=init_low_bound, high=init_high_bound),
            learning_rate=emb_lr_x),
        is_sparse=True)

    fc0 = fluid.layers.fc(input=emb,
                          size=hid_size * 3,
                          param_attr=fluid.ParamAttr(
                              initializer=fluid.initializer.Uniform(
                                  low=init_low_bound, high=init_high_bound),
                              learning_rate=gru_lr_x))
    gru_h0 = fluid.layers.dynamic_gru(
        input=fc0,
        size=hid_size,
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Uniform(
                low=init_low_bound, high=init_high_bound),
            learning_rate=gru_lr_x))

    fc = fluid.layers.fc(input=gru_h0,
                         size=vocab_size,
                         act='softmax',
                         param_attr=fluid.ParamAttr(
                             initializer=fluid.initializer.Uniform(
                                 low=init_low_bound, high=init_high_bound),
                             learning_rate=fc_lr_x))

    cost = fluid.layers.cross_entropy(input=fc, label=dst)
    return cost


def train(train_reader,
          vocab,
          network,
          hid_size,
          base_lr,
          batch_size,
          pass_num,
          use_cuda,
          parallel,
          model_dir,
          init_low_bound=-0.04,
          init_high_bound=0.04):
    """ train network """
    vocab_size = len(vocab)

    src_wordseq = fluid.layers.data(
        name="src_wordseq", shape=[1], dtype="int64", lod_level=1)
    dst_wordseq = fluid.layers.data(
        name="dst_wordseq", shape=[1], dtype="int64", lod_level=1)

    avg_cost = None
    if not parallel:
        cost = network(src_wordseq, dst_wordseq, vocab_size, hid_size,
                       init_low_bound, init_high_bound)
        avg_cost = fluid.layers.mean(x=cost)
    else:
        places = fluid.layers.get_places()
        pd = fluid.layers.ParallelDo(places)
        with pd.do():
            cost = network(
                pd.read_input(src_wordseq),
                pd.read_input(dst_wordseq), vocab_size, hid_size,
                init_low_bound, init_high_bound)
            pd.write_output(cost)

        cost = pd()
        avg_cost = fluid.layers.mean(x=cost)

    sgd_optimizer = fluid.optimizer.SGD(
        learning_rate=fluid.layers.exponential_decay(
            learning_rate=base_lr,
            decay_steps=2100 * 4,
            decay_rate=0.5,
            staircase=True))
    sgd_optimizer.minimize(avg_cost)

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)

    exe.run(fluid.default_startup_program())
    total_time = 0.0
    for pass_idx in xrange(pass_num):
        epoch_idx = pass_idx + 1
        print "epoch_%d start" % epoch_idx

        t0 = time.time()
        i = 0
        for data in train_reader():
            i += 1
            lod_src_wordseq = utils.to_lodtensor(
                map(lambda x: x[0], data), place)
            lod_dst_wordseq = utils.to_lodtensor(
                map(lambda x: x[1], data), place)
            ret_avg_cost = exe.run(fluid.default_main_program(),
                                   feed={
                                       "src_wordseq": lod_src_wordseq,
                                       "dst_wordseq": lod_dst_wordseq
                                   },
                                   fetch_list=[avg_cost],
                                   use_program_cache=True)
            avg_ppl = math.exp(ret_avg_cost[0])
            if i % 100 == 0:
                print "step:%d ppl:%.3f" % (i, avg_ppl)

        t1 = time.time()
        total_time += t1 - t0
        print "epoch:%d num_steps:%d time_cost(s):%f" % (epoch_idx, i,
                                                         total_time / epoch_idx)

        save_dir = "%s/epoch_%d" % (model_dir, epoch_idx)
        feed_var_names = ["src_wordseq", "dst_wordseq"]
        fetch_vars = [avg_cost]
        fluid.io.save_inference_model(save_dir, feed_var_names, fetch_vars, exe)
        print("model saved in %s" % save_dir)

    print("finish training")


def train_net():
    """ do training """
    batch_size = 20
    vocab, train_reader, test_reader = utils.prepare_data(
        batch_size=batch_size, buffer_size=1000, word_freq_threshold=0)
    train(
        train_reader=train_reader,
        vocab=vocab,
        network=network,
        hid_size=200,
        base_lr=1.0,
        batch_size=batch_size,
        pass_num=12,
        use_cuda=True,
        parallel=False,
        model_dir="model",
        init_low_bound=-0.1,
        init_high_bound=0.1)


if __name__ == "__main__":
    train_net()