# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import re
from itertools import groupby

import matplotlib.pyplot as plt
import numpy as np

from mmcls.utils import load_json_log


def cal_train_time(log_dicts, args):
    """Compute the average time per training iteration."""
    for i, log_dict in enumerate(log_dicts):
        print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
        train_logs = log_dict['train']

        if 'epoch' in train_logs[0]:
            epoch_ave_times = []
            for _, logs in groupby(train_logs, lambda log: log['epoch']):
                if args.include_outliers:
                    all_time = np.array([log['time'] for log in logs])
                else:
                    all_time = np.array([log['time'] for log in logs])[1:]
                epoch_ave_times.append(all_time.mean())
            epoch_ave_times = np.array(epoch_ave_times)
            slowest_epoch = epoch_ave_times.argmax()
            fastest_epoch = epoch_ave_times.argmin()
            std_over_epoch = epoch_ave_times.std()
            print(f'slowest epoch {slowest_epoch + 1}, '
                  f'average time is {epoch_ave_times[slowest_epoch]:.4f}')
            print(f'fastest epoch {fastest_epoch + 1}, '
                  f'average time is {epoch_ave_times[fastest_epoch]:.4f}')
            print(f'time std over epochs is {std_over_epoch:.4f}')

        avg_iter_time = np.array([log['time'] for log in train_logs]).mean()
        print(f'average iter time: {avg_iter_time:.4f} s/iter')
        print()


def get_legends(args):
    """if legend is None, use {filename}_{key} as legend."""
    legend = args.legend
    if legend is None:
        legend = []
        for json_log in args.json_logs:
            for metric in args.keys:
                # remove '.json' in the end of log names
                basename = os.path.basename(json_log)[:-5]
                if basename.endswith('.log'):
                    basename = basename[:-4]
                legend.append(f'{basename}_{metric}')
    assert len(legend) == (len(args.json_logs) * len(args.keys))
    return legend


def plot_phase_train(metric, train_logs, curve_label):
    """plot phase of train curve."""
    xs = np.array([log['step'] for log in train_logs])
    ys = np.array([log[metric] for log in train_logs])

    if 'epoch' in train_logs[0]:
        scale_factor = train_logs[-1]['step'] / train_logs[-1]['epoch']
        xs = xs / scale_factor
        plt.xlabel('Epochs')
    else:
        plt.xlabel('Iters')

    plt.plot(xs, ys, label=curve_label, linewidth=0.75)


def plot_phase_val(metric, val_logs, curve_label):
    """plot phase of val curve."""
    xs = np.array([log['step'] for log in val_logs])
    ys = np.array([log[metric] for log in val_logs])

    plt.xlabel('Steps')
    plt.plot(xs, ys, label=curve_label, linewidth=0.75)


def plot_curve_helper(log_dicts, metrics, args, legend):
    """plot curves from log_dicts by metrics."""
    num_metrics = len(metrics)
    for i, log_dict in enumerate(log_dicts):
        for j, key in enumerate(metrics):
            json_log = args.json_logs[i]
            print(f'plot curve of {json_log}, metric is {key}')
            curve_label = legend[i * num_metrics + j]

            train_keys = {} if len(log_dict['train']) == 0 else set(
                log_dict['train'][0].keys()) - {'step', 'epoch'}
            val_keys = {} if len(log_dict['val']) == 0 else set(
                log_dict['val'][0].keys()) - {'step'}

            if key in val_keys:
                plot_phase_val(key, log_dict['val'], curve_label)
            elif key in train_keys:
                plot_phase_train(key, log_dict['train'], curve_label)
            else:
                raise ValueError(f'Invalid key "{key}", please choose from '
                                 f'{set.union(train_keys, val_keys)}.')
            plt.legend()


def plot_curve(log_dicts, args):
    """Plot train metric-iter graph."""
    # set backend and style
    if args.backend is not None:
        plt.switch_backend(args.backend)
    try:
        import seaborn as sns
        sns.set_style(args.style)
    except ImportError:
        print("Attention: The plot style won't be applied because 'seaborn' "
              'package is not installed, please install it if you want better '
              'show style.')

    # set plot window size
    wind_w, wind_h = args.window_size.split('*')
    wind_w, wind_h = int(wind_w), int(wind_h)
    plt.figure(figsize=(wind_w, wind_h))

    # get legends and metrics
    legends = get_legends(args)
    metrics = args.keys

    # plot curves from log_dicts by metrics
    plot_curve_helper(log_dicts, metrics, args, legends)

    # set title and show or save
    if args.title is not None:
        plt.title(args.title)
    if args.out is None:
        plt.show()
    else:
        print(f'save curve to: {args.out}')
        plt.savefig(args.out)
        plt.cla()


def add_plot_parser(subparsers):
    parser_plt = subparsers.add_parser(
        'plot_curve', help='parser for plotting curves')
    parser_plt.add_argument(
        'json_logs',
        type=str,
        nargs='+',
        help='path of train log in json format')
    parser_plt.add_argument(
        '--keys',
        type=str,
        nargs='+',
        default=['loss'],
        help='the metric that you want to plot')
    parser_plt.add_argument('--title', type=str, help='title of figure')
    parser_plt.add_argument(
        '--legend',
        type=str,
        nargs='+',
        default=None,
        help='legend of each plot')
    parser_plt.add_argument(
        '--backend', type=str, default=None, help='backend of plt')
    parser_plt.add_argument(
        '--style', type=str, default='whitegrid', help='style of plt')
    parser_plt.add_argument('--out', type=str, default=None)
    parser_plt.add_argument(
        '--window-size',
        default='12*7',
        help='size of the window to display images, in format of "$W*$H".')


def add_time_parser(subparsers):
    parser_time = subparsers.add_parser(
        'cal_train_time',
        help='parser for computing the average time per training iteration')
    parser_time.add_argument(
        'json_logs',
        type=str,
        nargs='+',
        help='path of train log in json format')
    parser_time.add_argument(
        '--include-outliers',
        action='store_true',
        help='include the first value of every epoch when computing '
        'the average time')


def parse_args():
    parser = argparse.ArgumentParser(description='Analyze Json Log')
    # currently only support plot curve and calculate average train time
    subparsers = parser.add_subparsers(dest='task', help='task parser')
    add_plot_parser(subparsers)
    add_time_parser(subparsers)
    args = parser.parse_args()

    if hasattr(args, 'window_size') and args.window_size != '':
        assert re.match(r'\d+\*\d+', args.window_size), \
            "'window-size' must be in format 'W*H'."
    return args


def main():
    args = parse_args()

    json_logs = args.json_logs
    for json_log in json_logs:
        assert json_log.endswith('.json')

    log_dicts = [load_json_log(json_log) for json_log in json_logs]

    if args.task == 'cal_train_time':
        cal_train_time(log_dicts, args)
    elif args.task == 'plot_curve':
        plot_curve(log_dicts, args)


if __name__ == '__main__':
    main()
