# Copyright (c) 2020 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import yaml
import time
import shutil
import paddle
import paddle.distributed as dist
from tqdm import tqdm
from argparse import ArgumentParser, RawDescriptionHelpFormatter

from ppocr.utils.stats import TrainingStats
from ppocr.utils.save_load import save_model
from ppocr.utils.utility import print_dict
from ppocr.utils.logging import get_logger
import numpy as np


class ArgsParser(ArgumentParser):
    def __init__(self):
        super(ArgsParser, self).__init__(
            formatter_class=RawDescriptionHelpFormatter)
        self.add_argument("-c", "--config", help="configuration file to use")
        self.add_argument(
            "-o", "--opt", nargs='+', help="set configuration options")

    def parse_args(self, argv=None):

        # ArgumentParser.parse_args : 获取命令行运行时设置的参数
        # 传递配置文件地址
        # -c configs/rec/rec_mv3_none_bilstm_ctc.yml
        args = super(ArgsParser, self).parse_args(argv)
        assert args.config is not None, \
            "Please specify --config=configure_file_path."

        # 解析配置文件参数，返回参数字典
        args.opt = self._parse_opt(args.opt)
        return args

    # 解析配置文件参数，返回参数字典
    def _parse_opt(self, opts):
        config = {}
        if not opts:
            return config
        for s in opts:
            s = s.strip()
            k, v = s.split('=')
            config[k] = yaml.load(v, Loader=yaml.Loader)
        return config


class AttrDict(dict):
    """Single level attribute dict, NOT recursive"""

    def __init__(self, **kwargs):
        super(AttrDict, self).__init__()
        super(AttrDict, self).update(kwargs)

    def __getattr__(self, key):
        if key in self:
            return self[key]
        raise AttributeError("object has no attribute '{}'".format(key))


global_config = AttrDict()

default_config = {'Global': {'debug': False, }}


def load_config(file_path):
    """
    Load config from yml/yaml file.
    Args:
        file_path (str): Path of the config file to be loaded.
    Returns: global config
    """
    # 将默认配置合并到全局配置字典中
    merge_config(default_config)

    # 判断后缀为yaml
    _, ext = os.path.splitext(file_path)
    assert ext in ['.yml', '.yaml'], "only support yaml files for now"

    # 合并配置文件配置到全局配置字典中
    # yaml文件读取后是多层字典
    # {'Global': {'use_gpu': True, 'epoch_num': 72, 'log_smooth_window': 20}
    merge_config(yaml.load(open(file_path, 'rb'), Loader=yaml.Loader))
    return global_config


def merge_config(config):
    """
    Merge config into global config.
    Args:
        config (dict): Config to be merged.
    Returns: global config
    """
    for key, value in config.items():
        if "." not in key:
            if isinstance(value, dict) and key in global_config:
                global_config[key].update(value)
            else:
                global_config[key] = value
        else:
            sub_keys = key.split('.')
            assert (
                sub_keys[0] in global_config
            ), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
                global_config.keys(), sub_keys[0])
            cur = global_config[sub_keys[0]]
            for idx, sub_key in enumerate(sub_keys[1:]):
                if idx == len(sub_keys) - 2:
                    cur[sub_key] = value
                else:
                    cur = cur[sub_key]


def check_gpu(use_gpu):
    """
    Log error and exit when set use_gpu=true in paddlepaddle
    cpu version.
    """
    err = "Config use_gpu cannot be set as true while you are " \
          "using paddlepaddle cpu version ! \nPlease try: \n" \
          "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
          "\t2. Set use_gpu as false in config file to run " \
          "model on CPU"

    try:
        if use_gpu and not paddle.is_compiled_with_cuda():
            print(err)
            sys.exit(1)
    except Exception as e:
        pass


def train(config,
          train_dataloader,
          valid_dataloader,
          device,
          model,
          loss_class,
          optimizer,
          lr_scheduler,
          post_process_class,
          eval_class,
          pre_best_model_dict,
          logger,
          vdl_writer=None):
    cal_metric_during_train = config['Global'].get('cal_metric_during_train',
                                                   False)
    log_smooth_window = config['Global']['log_smooth_window']
    epoch_num = config['Global']['epoch_num']
    print_batch_step = config['Global']['print_batch_step']
    eval_batch_step = config['Global']['eval_batch_step']

    global_step = 0
    start_eval_step = 0
    if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
        start_eval_step = eval_batch_step[0]
        eval_batch_step = eval_batch_step[1]
        if len(valid_dataloader) == 0:
            logger.info(
                'No Images in eval dataset, evaluation during training will be disabled'
            )
            start_eval_step = 1e111
        logger.info(
            "During the training process, after the {}th iteration, an evaluation is run every {} iterations".
            format(start_eval_step, eval_batch_step))
    save_epoch_step = config['Global']['save_epoch_step']
    save_model_dir = config['Global']['save_model_dir']
    if not os.path.exists(save_model_dir):
        os.makedirs(save_model_dir)
    main_indicator = eval_class.main_indicator
    best_model_dict = {main_indicator: 0}
    best_model_dict.update(pre_best_model_dict)
    train_stats = TrainingStats(log_smooth_window, ['lr'])
    model_average = False
    model.train()

    use_srn = config['Architecture']['algorithm'] == "SRN"

    if 'start_epoch' in best_model_dict:
        start_epoch = best_model_dict['start_epoch']
        start_epoch = 1
    else:
        start_epoch = 1

    global_acc = 0
    # 迭代训练轮数
    for epoch in range(start_epoch, epoch_num + 1):
        print("=========================================================")
        # print("=========================================================")

        # train_dataloader = build_dataloader(
        #     config, 'Train', device, logger, seed=epoch)

        train_batch_cost = 0.0
        train_reader_cost = 0.0
        batch_sum = 0
        batch_start = time.time()
        for idx, batch in enumerate(train_dataloader):
            train_reader_cost += time.time() - batch_start
            if idx >= len(train_dataloader):
                break
            lr = optimizer.get_lr()
            images = batch[0]
            example_image = images[0]
            # 预测值为(128, 80, 6625)
            # 128为单个batch大小，即一个batch中有多少图片
            # 80为
            # 6625为中文字典大小，即有多少个中文字符
            if use_srn:
                others = batch[-4:]
                preds = model(images, others)
                model_average = True
            else:
                # print("==========================")
                # print(model)
                preds = model(images)
            loss = loss_class(preds, batch)
            avg_loss = loss['loss']
            avg_loss.backward()
            optimizer.step()
            optimizer.clear_grad()

            train_batch_cost += time.time() - batch_start
            batch_sum += len(images)

            if not isinstance(lr_scheduler, float):
                lr_scheduler.step()

            # logger and visualdl
            # 更新了loss和lr到logs字典
            stats = {k: v.numpy().mean() for k, v in loss.items()}
            stats['lr'] = lr
            train_stats.update(stats)

            # only rec and cls need
            # 计算Metric，其中包含accuracy，更新accuracy到logs字典
            if cal_metric_during_train:
                batch = [item.numpy() for item in batch]
                # 将预测值(128, 80, 6625) 传入CTC解码
                post_result = post_process_class(preds, batch[1])
                # print("predict", post_result[0])
                # print("real   ", post_result[1])
                # 初始化评估对象，输入
                eval_class(post_result, batch)
                # 计算Metric
                metric = eval_class.get_metric()
                train_stats.update(metric)

            if vdl_writer is not None and dist.get_rank() == 0:
                for k, v in train_stats.get().items():
                    vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
                vdl_writer.add_scalar('TRAIN/lr', lr, global_step)

            if dist.get_rank(
            ) == 0 and global_step > 0 and global_step % print_batch_step == 0:
                logs = train_stats.log()
                strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
                    epoch, epoch_num, global_step, logs, train_reader_cost /
                    print_batch_step, train_batch_cost / print_batch_step,
                    batch_sum, batch_sum / train_batch_cost)
                logger.info(strs)
                train_batch_cost = 0.0
                train_reader_cost = 0.0
                batch_sum = 0

            # if global_step % 500 == 0:
            #     print("save best accuracy model!")
            #     # save_model(
            #     #     model,
            #     #     optimizer,
            #     #     save_model_dir,
            #     #     logger,
            #     #     is_best=True,
            #     #     prefix='best_accuracy',
            #     #     best_model_dict=best_model_dict,
            #     #     epoch=epoch)
            #     paddle.jit.save(
            #         layer=model,
            #         path=config['Global']['save_model_dir']+'best_accuracy'
            #         # input_spec=[example_image]
            #     )

            current_acc = train_stats.get().get('acc')
            if global_acc < float(current_acc):
                if global_step > int(config['Train']['loader']['batch_size_per_card']):
                    print("save best accuracy model!", global_acc, "->", current_acc)
                    paddle.jit.save(
                        layer=model,
                        path=config['Global']['save_model_dir']+'best_accuracy'
                    )
                    global_acc = current_acc

            if global_step % 25000 == 1:
                print("save iters model!")
                # save_model(
                #     model,
                #     optimizer,
                #     save_model_dir,
                #     logger,
                #     is_best=False,
                #     prefix='iter_{}'.format(global_step),
                #     best_model_dict=best_model_dict,
                #     epoch=epoch)
                paddle.jit.save(
                    layer=model,
                    path=config['Global']['save_model_dir']+'iter_{}'.format(global_step)
                    # input_spec=[example_image]
                )

            # 训练过程中测试阶段
            # eval
            if global_step > start_eval_step and \
                    (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
                # 算法为SRN时平均
                if model_average:
                    Model_Average = paddle.incubate.optimizer.ModelAverage(
                        0.15,
                        parameters=model.parameters(),
                        min_average_window=10000,
                        max_average_window=15625)
                    Model_Average.apply()

                cur_metric = eval(
                    model,
                    valid_dataloader,
                    post_process_class,
                    eval_class,
                    use_srn=use_srn)
                cur_metric_str = 'cur metric, {}'.format(', '.join(
                    ['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
                logger.info("acc: " + str(cur_metric.get("acc")) +
                            "   norm_edit_dis: " + str(cur_metric.get("norm_edit_dis")) +
                            "   fps: " + str(cur_metric.get("fps")))

                # logger metric
                if vdl_writer is not None:
                    for k, v in cur_metric.items():
                        if isinstance(v, (float, int)):
                            vdl_writer.add_scalar('EVAL/{}'.format(k),
                                                  cur_metric[k], global_step)
                if cur_metric[main_indicator] >= best_model_dict[
                        main_indicator]:
                    best_model_dict.update(cur_metric)
                    best_model_dict['best_epoch'] = epoch
                    # save_model(
                    #     model,
                    #     optimizer,
                    #     save_model_dir,
                    #     logger,
                    #     is_best=True,
                    #     prefix='best_accuracy',
                    #     best_model_dict=best_model_dict,
                    #     epoch=epoch)
                    paddle.jit.save(
                        layer=model,
                        path=config['Global']['save_model_dir']+'best_accuracy'
                        # input_spec=[example_image]
                    )
                best_str = 'best metric, {}'.format(', '.join([
                    '{}: {}'.format(k, v) for k, v in best_model_dict.items()
                ]))
                # logger.info(best_str)
                # logger best metric
                if vdl_writer is not None:
                    vdl_writer.add_scalar('EVAL/best_{}'.format(main_indicator),
                                          best_model_dict[main_indicator],
                                          global_step)
            global_step += 1
            optimizer.clear_grad()
            batch_start = time.time()

        if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
            # save_model(
            #     model,
            #     optimizer,
            #     save_model_dir,
            #     logger,
            #     is_best=False,
            #     prefix='iter_epoch_{}'.format(epoch),
            #     best_model_dict=best_model_dict,
            #     epoch=epoch)
            paddle.jit.save(
                layer=model,
                path=config['Global']['save_model_dir']+'iter_epoch_{}'.format(epoch)
                # input_spec=[example_image]
            )
    # best_str = 'best metric, {}'.format(', '.join(
    #     ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
    # logger.info(best_str)
    if dist.get_rank() == 0 and vdl_writer is not None:
        vdl_writer.close()

    return


def eval(model, valid_dataloader, post_process_class, eval_class,
         use_srn=False):
    model.eval()
    with paddle.no_grad():
        total_frame = 0.0
        total_time = 0.0
        pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
        for idx, batch in enumerate(valid_dataloader):
            if idx >= len(valid_dataloader):
            # if idx >= 2:
                break
            images = batch[0]
            start = time.time()

            if use_srn:
                others = batch[-4:]
                preds = model(images, others)
            else:
                preds = model(images)

            batch = [item.numpy() for item in batch]
            # Obtain usable results from post-processing methods
            post_result = post_process_class(preds, batch[1])
            total_time += time.time() - start
            # Evaluate the results of the current batch
            eval_class(post_result, batch)
            pbar.update(1)
            total_frame += len(images)
        # Get final metric，eg. acc or hmean
        metric = eval_class.get_metric()

    pbar.close()
    model.train()
    metric['fps'] = total_frame / total_time
    return metric


def preprocess(is_train=False):
    FLAGS = ArgsParser().parse_args()
    # 从配置文件读取参数，得到全局参数字典
    config = load_config(FLAGS.config)
    merge_config(FLAGS.opt)

    # check if set use_gpu=True in paddlepaddle cpu version
    # 检查gpu配置是否正确
    use_gpu = config['Global']['use_gpu']
    check_gpu(use_gpu)

    # 检查算法类型是否正确
    alg = config['Architecture']['algorithm']
    assert alg in [
        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
    ]

    device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
    device = paddle.set_device(device)

    config['Global']['distributed'] = dist.get_world_size() != 1

    # 区分训练和评估，训练需同时保存配置文件和日志文件，评估不需要
    if is_train:
        # save_config
        # ./output/rec/mv3_none_bilstm_ctc/
        save_model_dir = config['Global']['save_model_dir']
        os.makedirs(save_model_dir, exist_ok=True)
        # 保存配置文件
        with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
            yaml.dump(
                dict(config), f, default_flow_style=False, sort_keys=False)
        log_file = '{}/train.log'.format(save_model_dir)
    else:
        log_file = None
    logger = get_logger(name='root', log_file=log_file)

    # 保存visualDL 日志文件
    if config['Global']['use_visualdl']:
        from visualdl import LogWriter
        save_model_dir = config['Global']['save_model_dir']
        vdl_writer_path = '{}/vdl/'.format(save_model_dir)
        os.makedirs(vdl_writer_path, exist_ok=True)
        vdl_writer = LogWriter(logdir=vdl_writer_path)
    else:
        vdl_writer = None
    print_dict(config, logger)
    logger.info('train with paddle {} and device {}'.format(paddle.__version__,
                                                            device))
    return config, device, logger, vdl_writer