# Copyright (c) Open-MMLab. All rights reserved.
import datetime
import os.path as osp
from collections import OrderedDict

import torch
import torch.distributed as dist

import mmcv
from ..hook import HOOKS
from .base import LoggerHook


@HOOKS.register_module()
class TextLoggerHook(LoggerHook):
    """Logger hook in text.

    In this logger hook, the information will be printed on terminal and
    saved in json file.

    Args:
        by_epoch (bool): Whether EpochBasedRunner is used.
        interval (int): Logging interval (every k iterations).
        ignore_last (bool): Ignore the log of last iterations in each epoch
            if less than `interval`.
        reset_flag (bool): Whether to clear the output buffer after logging.
        interval_exp_name (int): Logging interval for experiment name. This
            feature is to help users conveniently get the experiment
            information from screen or log file. Default: 1000.
    """

    def __init__(self,
                 by_epoch=True,
                 interval=10,
                 ignore_last=True,
                 reset_flag=False,
                 interval_exp_name=1000):
        super(TextLoggerHook, self).__init__(interval, ignore_last, reset_flag)
        self.by_epoch = by_epoch
        self.time_sec_tot = 0
        self.interval_exp_name = interval_exp_name

    def before_run(self, runner):
        super(TextLoggerHook, self).before_run(runner)
        self.start_iter = runner.iter
        self.json_log_path = osp.join(runner.work_dir,
                                      f'{runner.timestamp}.log.json')
        if runner.meta is not None:
            self._dump_log(runner.meta, runner)

    def _get_max_memory(self, runner):
        mem = torch.cuda.max_memory_allocated()
        mem_mb = torch.tensor([mem / (1024 * 1024)],
                              dtype=torch.int,
                              device=torch.device('cuda'))
        if runner.world_size > 1:
            dist.reduce(mem_mb, 0, op=dist.ReduceOp.MAX)
        return mem_mb.item()

    def _log_info(self, log_dict, runner):
        # print exp name for users to distinguish experiments
        # at every ``interval_exp_name`` iterations and the end of each epoch
        if runner.meta is not None and 'exp_name' in runner.meta:
            if (self.every_n_inner_iters(runner, self.interval_exp_name)) or (
                    self.by_epoch and self.end_of_epoch(runner)):
                exp_info = f'Exp name: {runner.meta["exp_name"]}'
                runner.logger.info(exp_info)

        if runner.mode == 'train':
            if isinstance(log_dict['lr'], dict):
                lr_str = []
                for k, val in log_dict['lr'].items():
                    lr_str.append(f'lr_{k}: {val:.3e}')
                lr_str = ' '.join(lr_str)
            else:
                lr_str = f'lr: {log_dict["lr"]:.3e}'

            # by epoch: Epoch [4][100/1000]
            # by iter:  Iter [100/100000]
            if self.by_epoch:
                log_str = f'Epoch [{log_dict["epoch"]}]' \
                          f'[{log_dict["iter"]}/{len(runner.data_loader)}]\t'
            else:
                log_str = f'Iter [{log_dict["iter"]}/{runner.max_iters}]\t'
            log_str += f'{lr_str}, '

            if 'time' in log_dict.keys():
                self.time_sec_tot += (log_dict['time'] * self.interval)
                time_sec_avg = self.time_sec_tot / (
                    runner.iter - self.start_iter + 1)
                eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1)
                eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
                log_str += f'eta: {eta_str}, '
                log_str += f'time: {log_dict["time"]:.3f}, ' \
                           f'data_time: {log_dict["data_time"]:.3f}, '
                # statistic memory
                if torch.cuda.is_available():
                    log_str += f'memory: {log_dict["memory"]}, '
        else:
            if self.by_epoch:
                log_str = f'Epoch({log_dict["mode"]}) ' \
                        f'[{log_dict["epoch"] - 1}][{log_dict["iter"]}]\t'
            else:
                log_str = f'Iter({log_dict["mode"]}) [{log_dict["iter"]}]\t'

        log_items = []
        for name, val in log_dict.items():
            # TODO: resolve this hack
            # these items have been in log_str
            if name in [
                    'mode', 'Epoch', 'iter', 'lr', 'time', 'data_time',
                    'memory', 'epoch'
            ]:
                continue
            if isinstance(val, float):
                val = f'{val:.4f}'
            log_items.append(f'{name}: {val}')
        log_str += ', '.join(log_items)

        runner.logger.info(log_str)

    def _dump_log(self, log_dict, runner):
        # dump log in json format
        json_log = OrderedDict()
        for k, v in log_dict.items():
            json_log[k] = self._round_float(v)
        # only append log at last line
        if runner.rank == 0:
            with open(self.json_log_path, 'a+') as f:
                mmcv.dump(json_log, f, file_format='json')
                f.write('\n')

    def _round_float(self, items):
        if isinstance(items, list):
            return [self._round_float(item) for item in items]
        elif isinstance(items, float):
            return round(items, 5)
        else:
            return items

    def log(self, runner):
        log_dict = OrderedDict()
        # training mode if the output contains the key "time"
        mode = 'train' if 'time' in runner.log_buffer.output else 'val'
        log_dict['mode'] = mode
        log_dict['epoch'] = runner.epoch + 1
        log_dict['iter'] = runner.inner_iter + 1
        # only record lr of the first param group
        cur_lr = runner.current_lr()
        if isinstance(cur_lr, list):
            log_dict['lr'] = cur_lr[0]
        else:
            assert isinstance(cur_lr, dict)
            log_dict['lr'] = {}
            for k, lr_ in cur_lr.items():
                assert isinstance(lr_, list)
                log_dict['lr'].update({k: lr_[0]})

        if mode == 'train':
            log_dict['time'] = runner.log_buffer.output['time']
            log_dict['data_time'] = runner.log_buffer.output['data_time']

            # statistic memory
            if torch.cuda.is_available():
                log_dict['memory'] = self._get_max_memory(runner)
        for name, val in runner.log_buffer.output.items():
            if name in ['time', 'data_time']:
                continue
            log_dict[name] = val

        self._log_info(log_dict, runner)
        self._dump_log(log_dict, runner)
