# Copyright (c) 2019 PaddlePaddle 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.

import logging
import os

import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.io import DataLoader
from paddle.static import InputSpec as Input

from utils.configure import PDConfig
from utils.check import check_gpu, check_version

from reader import create_data_loader
from transformer import Transformer, CrossEntropyCriterion


class TrainCallback(paddle.callbacks.ProgBarLogger):
    def __init__(self,
                 args,
                 verbose=2,
                 train_steps_fn=None,
                 eval_steps_fn=None):
        # TODO(guosheng): save according to step
        super(TrainCallback, self).__init__(args.print_step, verbose)
        # the best cross-entropy value with label smoothing
        loss_normalizer = -(
            (1. - args.label_smooth_eps) * np.log(
                (1. - args.label_smooth_eps)) + args.label_smooth_eps *
            np.log(args.label_smooth_eps / (args.trg_vocab_size - 1) + 1e-20))
        self.loss_normalizer = loss_normalizer
        self.train_steps_fn = train_steps_fn
        self.eval_steps_fn = eval_steps_fn

    def on_train_begin(self, logs=None):
        super(TrainCallback, self).on_train_begin(logs)
        self.train_metrics += ["normalized loss", "ppl"]

    def on_train_batch_begin(self, step, logs=None):
        if step == 0 and self.train_steps_fn:
            self.train_progbar._num = self.train_steps_fn()

    def on_train_batch_end(self, step, logs=None):
        logs["normalized loss"] = logs["loss"][0] - self.loss_normalizer
        logs["ppl"] = np.exp(min(logs["loss"][0], 100))
        super(TrainCallback, self).on_train_batch_end(step, logs)

    def on_eval_begin(self, logs=None):
        super(TrainCallback, self).on_eval_begin(logs)
        self.eval_metrics = list(
            self.eval_metrics) + ["normalized loss", "ppl"]

    def on_eval_batch_begin(self, step, logs=None):
        if step == 0 and self.eval_steps_fn:
            self.eval_progbar._num = self.eval_steps_fn()

    def on_eval_batch_end(self, step, logs=None):
        logs["normalized loss"] = logs["loss"][0] - self.loss_normalizer
        logs["ppl"] = np.exp(min(logs["loss"][0], 100))
        super(TrainCallback, self).on_eval_batch_end(step, logs)


def do_train(args):
    device = paddle.set_device("gpu" if args.use_cuda else "cpu")
    fluid.enable_dygraph(device) if args.eager_run else None

    # set seed for CE
    random_seed = eval(str(args.random_seed))
    if random_seed is not None:
        fluid.default_main_program().random_seed = random_seed
        fluid.default_startup_program().random_seed = random_seed

    # define inputs
    inputs = [
        Input(
            [None, None], "int64", name="src_word"),
        Input(
            [None, None], "int64", name="src_pos"),
        Input(
            [None, args.n_head, None, None],
            "float32",
            name="src_slf_attn_bias"),
        Input(
            [None, None], "int64", name="trg_word"),
        Input(
            [None, None], "int64", name="trg_pos"),
        Input(
            [None, args.n_head, None, None],
            "float32",
            name="trg_slf_attn_bias"),
        Input(
            [None, args.n_head, None, None],
            "float32",
            name="trg_src_attn_bias"),
    ]
    labels = [
        Input(
            [None, 1], "int64", name="label"),
        Input(
            [None, 1], "float32", name="weight"),
    ]

    # def dataloader
    (train_loader, train_steps_fn), (
        eval_loader, eval_steps_fn) = create_data_loader(args, device)

    # define model
    model = paddle.Model(
        Transformer(args.src_vocab_size, args.trg_vocab_size,
                    args.max_length + 1, args.n_layer, args.n_head, args.d_key,
                    args.d_value, args.d_model, args.d_inner_hid,
                    args.prepostprocess_dropout, args.attention_dropout,
                    args.relu_dropout, args.preprocess_cmd,
                    args.postprocess_cmd, args.weight_sharing, args.bos_idx,
                    args.eos_idx), inputs, labels)

    model.prepare(
        fluid.optimizer.Adam(
            learning_rate=fluid.layers.noam_decay(
                args.d_model,
                args.warmup_steps,
                learning_rate=args.learning_rate),
            beta1=args.beta1,
            beta2=args.beta2,
            epsilon=float(args.eps),
            parameter_list=model.parameters()),
        CrossEntropyCriterion(args.label_smooth_eps))

    ## init from some checkpoint, to resume the previous training
    if args.init_from_checkpoint:
        model.load(args.init_from_checkpoint)
    ## init from some pretrain models, to better solve the current task
    if args.init_from_pretrain_model:
        model.load(args.init_from_pretrain_model, reset_optimizer=True)

    # model train
    model.fit(train_data=train_loader,
              eval_data=eval_loader,
              epochs=args.epoch,
              eval_freq=1,
              save_freq=1,
              save_dir=args.save_model,
              callbacks=[
                  TrainCallback(
                      args,
                      train_steps_fn=train_steps_fn,
                      eval_steps_fn=eval_steps_fn)
              ])


if __name__ == "__main__":
    args = PDConfig(yaml_file="./transformer.yaml")
    args.build()
    args.Print()
    check_gpu(args.use_cuda)
    check_version()

    do_train(args)