# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import os
import time
import math
import subprocess
import shutil
from os.path import join
import numpy as np
from inspect import isclass
from pytz import timezone
from datetime import datetime
import inspect
import torch


def datestr():
    pacific = timezone('US/Pacific')
    now = datetime.now(pacific)
    return '{}{:02}{:02}_{:02}{:02}'.format(
        now.year, now.month, now.day, now.hour, now.minute)


def module_to_dict(module, exclude=[]):
    return dict([(x, getattr(module, x)) for x in dir(module)
                 if isclass(getattr(module, x))
                 and x not in exclude
                 and getattr(module, x) not in exclude])


class TimerBlock:
    def __init__(self, title):
        print(("{}".format(title)))

    def __enter__(self):
        self.start = time.clock()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.end = time.clock()
        self.interval = self.end - self.start

        if exc_type is not None:
            self.log("Operation failed\n")
        else:
            self.log("Operation finished\n")

    def log(self, string):
        duration = time.clock() - self.start
        units = 's'
        if duration > 60:
            duration = duration / 60.
            units = 'm'
        print(("  [{:.3f}{}] {}".format(duration, units, string)))

    def log2file(self, fid, string):
        fid = open(fid, 'a')
        fid.write("%s\n" % (string))
        fid.close()


def add_arguments_for_module(
        parser,
        module,
        argument_for_class,
        default,
        skip_params=[],
        parameter_defaults={}):
    argument_group = parser.add_argument_group(argument_for_class.capitalize())

    module_dict = module_to_dict(module)
    argument_group.add_argument(
        '--' + argument_for_class,
        type=str,
        default=default,
        choices=list(
            module_dict.keys()))

    args, unknown_args = parser.parse_known_args()
    class_obj = module_dict[vars(args)[argument_for_class]]

    argspec = inspect.getargspec(class_obj.__init__)

    defaults = argspec.defaults[::-1] if argspec.defaults else None

    args = argspec.args[::-1]
    for i, arg in enumerate(args):
        cmd_arg = '{}_{}'.format(argument_for_class, arg)
        if arg not in skip_params + ['self', 'args']:
            if arg in list(parameter_defaults.keys()):
                argument_group.add_argument(
                    '--{}'.format(cmd_arg),
                    type=type(
                        parameter_defaults[arg]),
                    default=parameter_defaults[arg])
            elif (defaults is not None and i < len(defaults)):
                argument_group.add_argument(
                    '--{}'.format(cmd_arg),
                    type=type(
                        defaults[i]),
                    default=defaults[i])
            else:
                print(("[Warning]: non-default argument '{}' "
                       "detected on class '{}'. This argument "
                       "cannot be modified via the command line"
                       .format(arg, module.__class__.__name__)))
            # We don't have a good way of dealing with
            # inferring the type of the argument
            # TODO: try creating a custom action and using ast's infer type?
            # else:
            #     argument_group.add_argument('--{}'.format(
            #     cmd_arg), required=True)


def kwargs_from_args(args, argument_for_class):
    argument_for_class = argument_for_class + '_'
    return {key[len(argument_for_class):]: value for key, value in list(vars(
        args).items()) if
            argument_for_class in key and key != argument_for_class + 'class'}


def format_dictionary_of_losses(labels, values):
    try:
        string = ', '.join([('{}: {:' +
                             ('.3f' if value >= 0.001 else '.1e') +
                             '}').format(name, value) for name, value in
                            zip(labels, values)])
    except (TypeError, ValueError) as e:
        print((list(zip(labels, values))))
        string = '[Log Error] ' + str(e)

    return string


class IteratorTimer():
    def __init__(self, iterable):
        self.iterable = iterable
        self.iterator = self.iterable.__iter__()

    def __iter__(self):
        return self

    def __len__(self):
        return len(self.iterable)

    def __next__(self):
        start = time.time()
        n = next(self.iterator)
        self.last_duration = (time.time() - start)
        return n

    next = __next__


def gpumemusage():
    gpu_mem = subprocess.check_output(
        "nvidia-smi | grep MiB | cut -f 3 -d '|'",
        shell=True).replace(
        ' ',
        '').replace(
            '\n',
            '').replace(
                'i',
        '')
    all_stat = [float(a) for a in gpu_mem.replace('/', '').split('MB')[:-1]]

    gpu_mem = ''
    for i in range(len(all_stat) / 2):
        curr, tot = all_stat[2 * i], all_stat[2 * i + 1]
        util = "%1.2f" % (100 * curr / tot) + '%'
        cmem = str(int(math.ceil(curr / 1024.))) + 'GB'
        gmem = str(int(math.ceil(tot / 1024.))) + 'GB'
        gpu_mem += util + '--' + join(cmem, gmem) + ' '
    return gpu_mem


def update_hyperparameter_schedule(args, epoch, global_iteration, optimizer):
    if args.schedule_lr_frequency > 0:
        for param_group in optimizer.param_groups:
            if (global_iteration + 1) % args.schedule_lr_frequency == 0:
                param_group['lr'] /= float(args.schedule_lr_fraction)
                param_group['lr'] = float(
                    np.maximum(param_group['lr'], 0.000001))


def save_checkpoint(state, is_best, path, prefix,
                    filename='checkpoint.pth.tar'):
    prefix_save = os.path.join(path, prefix)
    name = prefix_save + '_' + filename
    torch.save(state, name)
    if is_best:
        shutil.copyfile(name, prefix_save + '_model_best.pth.tar')