# Copyright 2017-2020 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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 collections import defaultdict

import numpy as np
import torch


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = defaultdict(int)
        self.avg = defaultdict(float)
        self.sum = defaultdict(int)
        self.count = defaultdict(int)

    def update(self, n=1, **val):
        for k in val:
            self.val[k] = val[k]
            self.sum[k] += val[k] * n
            self.count[k] += n
            self.avg[k] = self.sum[k] / self.count[k]


def set_batchnorm_mode(model, train=True):
    """Allows to set batch_norm layer mode to train or eval, independendtly on the mode of the model."""

    def _set_batchnorm_mode(module):
        if isinstance(module, torch.nn.BatchNorm1d) or isinstance(
            module, torch.nn.BatchNorm2d
        ):
            if train:
                module.train()
            else:
                module.eval()

    model.apply(_set_batchnorm_mode)


def get_error(output, target):
    pred = output.argmax(dim=1)
    correct = pred.eq(target).float().sum()
    return float((1.0 - correct / output.size(0)) * 100.0)


def adjust_learning_rate(optimizer, epoch, optimizer_cfg):
    lr = optimizer_cfg.lr * (0.1 ** np.less(optimizer_cfg.schedule, epoch).sum())
    for param_group in optimizer.param_groups:
        param_group["lr"] = lr


def get_device(model: torch.nn.Module):
    return next(model.parameters()).device
