# -*- coding: utf-8 -*-
#
# train.py
#
# Copyright 2020 Amazon.com, Inc. or its affiliates. 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 os
import logging
import time

from .dataloader import EvalDataset, TrainDataset, NewBidirectionalOneShotIterator
from .dataloader import get_dataset

from .utils import get_compatible_batch_size, save_model, CommonArgParser

backend = os.environ.get('DGLBACKEND', 'pytorch')
if backend.lower() == 'mxnet':
    import multiprocessing as mp
    from .train_mxnet import load_model
    from .train_mxnet import train
    from .train_mxnet import test
else:
    import torch.multiprocessing as mp
    from .train_pytorch import load_model
    from .train_pytorch import train, train_mp
    from .train_pytorch import test, test_mp

class ArgParser(CommonArgParser):
    def __init__(self):
        super(ArgParser, self).__init__()

        self.add_argument('--gpu', type=int, default=[-1], nargs='+', 
                          help='A list of gpu ids, e.g. 0 1 2 4')
        self.add_argument('--mix_cpu_gpu', action='store_true',
                          help='Training a knowledge graph embedding model with both CPUs and GPUs.'\
                                  'The embeddings are stored in CPU memory and the training is performed in GPUs.'\
                                  'This is usually used for training a large knowledge graph embeddings.')
        self.add_argument('--valid', action='store_true',
                          help='Evaluate the model on the validation set in the training.')
        self.add_argument('--rel_part', action='store_true',
                          help='Enable relation partitioning for multi-GPU training.')
        self.add_argument('--async_update', action='store_true',
                          help='Allow asynchronous update on node embedding for multi-GPU training.'\
                                  'This overlaps CPU and GPU computation to speed up.')


def get_logger(args):
    if not os.path.exists(args.save_path):
        os.mkdir(args.save_path)

    folder = '{}_{}_'.format(args.model_name, args.dataset)
    n = len([x for x in os.listdir(args.save_path) if x.startswith(folder)])
    folder += str(n)
    args.save_path = os.path.join(args.save_path, folder)

    if not os.path.exists(args.save_path):
        os.makedirs(args.save_path)
    log_file = os.path.join(args.save_path, 'train.log')

    logging.basicConfig(
        format='%(asctime)s %(levelname)-8s %(message)s',
        level=logging.INFO,
        datefmt='%Y-%m-%d %H:%M:%S',
        filename=log_file,
        filemode='w'
    )

    logger = logging.getLogger(__name__)
    print("Logs are being recorded at: {}".format(log_file))
    return logger


def main():
    args = ArgParser().parse_args()
    logger = get_logger(args)

    init_time_start = time.time()
    # load dataset and samplers
    dataset = get_dataset(args.data_path, args.dataset, args.format, args.data_files)

    if args.neg_sample_size_eval < 0:
        args.neg_sample_size_eval = dataset.n_entities
    args.batch_size = get_compatible_batch_size(args.batch_size, args.neg_sample_size)
    args.batch_size_eval = get_compatible_batch_size(args.batch_size_eval, args.neg_sample_size_eval)
    # We should turn on mix CPU-GPU training for multi-GPU training.
    if len(args.gpu) > 1:
        args.mix_cpu_gpu = True
        if args.num_proc < len(args.gpu):
            args.num_proc = len(args.gpu)
    # We need to ensure that the number of processes should match the number of GPUs.
    if len(args.gpu) > 1 and args.num_proc > 1:
        assert args.num_proc % len(args.gpu) == 0, \
                'The number of processes needs to be divisible by the number of GPUs'
    # For multiprocessing training, we need to ensure that training processes are synchronized periodically.
    if args.num_proc > 1:
        args.force_sync_interval = 1000

    args.eval_filter = not args.no_eval_filter
    if args.neg_deg_sample_eval:
        assert not args.eval_filter, "if negative sampling based on degree, we can't filter positive edges."

    args.soft_rel_part = args.mix_cpu_gpu and args.rel_part
    train_data = TrainDataset(dataset, args, ranks=args.num_proc)
    # if there is no cross partition relaiton, we fall back to strict_rel_part
    args.strict_rel_part = args.mix_cpu_gpu and (train_data.cross_part == False)
    args.num_workers = 8 # fix num_worker to 8

    if args.num_proc > 1:
        train_samplers = []
        for i in range(args.num_proc):
            train_sampler_head = train_data.create_sampler(args.batch_size,
                                                           args.neg_sample_size,
                                                           args.neg_sample_size,
                                                           mode='head',
                                                           num_workers=args.num_workers,
                                                           shuffle=True,
                                                           exclude_positive=False,
                                                           rank=i)
            train_sampler_tail = train_data.create_sampler(args.batch_size,
                                                           args.neg_sample_size,
                                                           args.neg_sample_size,
                                                           mode='tail',
                                                           num_workers=args.num_workers,
                                                           shuffle=True,
                                                           exclude_positive=False,
                                                           rank=i)
            train_samplers.append(NewBidirectionalOneShotIterator(train_sampler_head, train_sampler_tail,
                                                                  args.neg_sample_size, args.neg_sample_size,
                                                                  True, dataset.n_entities))

        train_sampler = NewBidirectionalOneShotIterator(train_sampler_head, train_sampler_tail,
                                                        args.neg_sample_size, args.neg_sample_size,
                                                       True, dataset.n_entities)
    else: # This is used for debug
        train_sampler_head = train_data.create_sampler(args.batch_size,
                                                       args.neg_sample_size,
                                                       args.neg_sample_size,
                                                       mode='head',
                                                       num_workers=args.num_workers,
                                                       shuffle=True,
                                                       exclude_positive=False)
        train_sampler_tail = train_data.create_sampler(args.batch_size,
                                                       args.neg_sample_size,
                                                       args.neg_sample_size,
                                                       mode='tail',
                                                       num_workers=args.num_workers,
                                                       shuffle=True,
                                                       exclude_positive=False)
        train_sampler = NewBidirectionalOneShotIterator(train_sampler_head, train_sampler_tail,
                                                        args.neg_sample_size, args.neg_sample_size,
                                                        True, dataset.n_entities)


    if args.valid or args.test:
        if len(args.gpu) > 1:
            args.num_test_proc = args.num_proc if args.num_proc < len(args.gpu) else len(args.gpu)
        else:
            args.num_test_proc = args.num_proc
        if args.valid:
            assert dataset.valid is not None, 'validation set is not provided'
        if args.test:
            assert dataset.test is not None, 'test set is not provided'
        eval_dataset = EvalDataset(dataset, args)

    if args.valid:
        if args.num_proc > 1:
            valid_sampler_heads = []
            valid_sampler_tails = []
            for i in range(args.num_proc):
                valid_sampler_head = eval_dataset.create_sampler('valid', args.batch_size_eval,
                                                                  args.neg_sample_size_eval,
                                                                  args.neg_sample_size_eval,
                                                                  args.eval_filter,
                                                                  mode='chunk-head',
                                                                  num_workers=args.num_workers,
                                                                  rank=i, ranks=args.num_proc)
                valid_sampler_tail = eval_dataset.create_sampler('valid', args.batch_size_eval,
                                                                  args.neg_sample_size_eval,
                                                                  args.neg_sample_size_eval,
                                                                  args.eval_filter,
                                                                  mode='chunk-tail',
                                                                  num_workers=args.num_workers,
                                                                  rank=i, ranks=args.num_proc)
                valid_sampler_heads.append(valid_sampler_head)
                valid_sampler_tails.append(valid_sampler_tail)
        else: # This is used for debug
            valid_sampler_head = eval_dataset.create_sampler('valid', args.batch_size_eval,
                                                             args.neg_sample_size_eval,
                                                             args.neg_sample_size_eval,
                                                             args.eval_filter,
                                                             mode='chunk-head',
                                                             num_workers=args.num_workers,
                                                             rank=0, ranks=1)
            valid_sampler_tail = eval_dataset.create_sampler('valid', args.batch_size_eval,
                                                             args.neg_sample_size_eval,
                                                             args.neg_sample_size_eval,
                                                             args.eval_filter,
                                                             mode='chunk-tail',
                                                             num_workers=args.num_workers,
                                                             rank=0, ranks=1)
    if args.test:
        if args.num_test_proc > 1:
            test_sampler_tails = []
            test_sampler_heads = []
            for i in range(args.num_test_proc):
                test_sampler_head = eval_dataset.create_sampler('test', args.batch_size_eval,
                                                                 args.neg_sample_size_eval,
                                                                 args.neg_sample_size_eval,
                                                                 args.eval_filter,
                                                                 mode='chunk-head',
                                                                 num_workers=args.num_workers,
                                                                 rank=i, ranks=args.num_test_proc)
                test_sampler_tail = eval_dataset.create_sampler('test', args.batch_size_eval,
                                                                 args.neg_sample_size_eval,
                                                                 args.neg_sample_size_eval,
                                                                 args.eval_filter,
                                                                 mode='chunk-tail',
                                                                 num_workers=args.num_workers,
                                                                 rank=i, ranks=args.num_test_proc)
                test_sampler_heads.append(test_sampler_head)
                test_sampler_tails.append(test_sampler_tail)
        else:
            test_sampler_head = eval_dataset.create_sampler('test', args.batch_size_eval,
                                                            args.neg_sample_size_eval,
                                                            args.neg_sample_size_eval,
                                                            args.eval_filter,
                                                            mode='chunk-head',
                                                            num_workers=args.num_workers,
                                                            rank=0, ranks=1)
            test_sampler_tail = eval_dataset.create_sampler('test', args.batch_size_eval,
                                                            args.neg_sample_size_eval,
                                                            args.neg_sample_size_eval,
                                                            args.eval_filter,
                                                            mode='chunk-tail',
                                                            num_workers=args.num_workers,
                                                            rank=0, ranks=1)

    # load model
    model = load_model(logger, args, dataset.n_entities, dataset.n_relations)
    if args.num_proc > 1 or args.async_update:
        model.share_memory()

    # We need to free all memory referenced by dataset.
    eval_dataset = None
    dataset = None

    print('Total initialize time {:.3f} seconds'.format(time.time() - init_time_start))

    # train
    start = time.time()
    rel_parts = train_data.rel_parts if args.strict_rel_part or args.soft_rel_part else None
    cross_rels = train_data.cross_rels if args.soft_rel_part else None
    if args.num_proc > 1:
        procs = []
        barrier = mp.Barrier(args.num_proc)
        for i in range(args.num_proc):
            valid_sampler = [valid_sampler_heads[i], valid_sampler_tails[i]] if args.valid else None
            proc = mp.Process(target=train_mp, args=(args,
                                                     model,
                                                     train_samplers[i],
                                                     valid_sampler,
                                                     i,
                                                     rel_parts,
                                                     cross_rels,
                                                     barrier))
            procs.append(proc)
            proc.start()
        for proc in procs:
            proc.join()
    else:
        valid_samplers = [valid_sampler_head, valid_sampler_tail] if args.valid else None
        train(args, model, train_sampler, valid_samplers, rel_parts=rel_parts)

    print('training takes {} seconds'.format(time.time() - start))

    if not args.no_save_emb:
        save_model(args, model)

    # test
    if args.test:
        start = time.time()
        if args.num_test_proc > 1:
            queue = mp.Queue(args.num_test_proc)
            procs = []
            for i in range(args.num_test_proc):
                proc = mp.Process(target=test_mp, args=(args,
                                                        model,
                                                        [test_sampler_heads[i], test_sampler_tails[i]],
                                                        i,
                                                        'Test',
                                                        queue))
                procs.append(proc)
                proc.start()

            total_metrics = {}
            metrics = {}
            logs = []
            for i in range(args.num_test_proc):
                log = queue.get()
                logs = logs + log
            
            for metric in logs[0].keys():
                metrics[metric] = sum([log[metric] for log in logs]) / len(logs)
            print("-------------- Test result --------------")
            for k, v in metrics.items():
                print('Test average {} : {}'.format(k, v))
            print("-----------------------------------------")

            for proc in procs:
                proc.join()
        else:
            test(args, model, [test_sampler_head, test_sampler_tail])
        print('testing takes {:.3f} seconds'.format(time.time() - start))

if __name__ == '__main__':
    main()
