"""
Misc utilities
"""
import json
import random
import sys

import torch
import numpy as np

from uniter_model.utils.logger import LOGGER


class NoOp(object):
    """ useful for distributed training No-Ops """
    def __getattr__(self, name):
        return self.noop

    def noop(self, *args, **kwargs):
        return


def parse_with_config(parser):
    args = parser.parse_args()
    if args.config is not None:
        config_args = json.load(open(args.config))
        override_keys = {arg[2:].split('=')[0] for arg in sys.argv[1:]
                         if arg.startswith('--')}
        for k, v in config_args.items():
            if k not in override_keys:
                setattr(args, k, v)
    del args.config
    return args


VE_ENT2IDX = {
    'contradiction': 0,
    'entailment': 1,
    'neutral': 2
}

VE_IDX2ENT = {
    0: 'contradiction',
    1: 'entailment',
    2: 'neutral'
}


class Struct(object):
    def __init__(self, dict_):
        self.__dict__.update(dict_)


def set_dropout(model, drop_p):
    for name, module in model.named_modules():
        # we might want to tune dropout for smaller dataset
        if isinstance(module, torch.nn.Dropout):
            if module.p != drop_p:
                module.p = drop_p
                LOGGER.info(f'{name} set to {drop_p}')


def set_random_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)