""" Utilities """

import os

import logging

import shutil

import torch

import torchvision.datasets as dset

import numpy as np

import torch.nn as nn

device = torch.device("cuda")

def get_logger(file_path):

    """ Make python logger """

    # [!] Since tensorboardX use default logger (e.g. logging.info()), we should use custom logger

    logger = logging.getLogger('darts')

    log_format = '%(asctime)s | %(message)s'

    formatter = logging.Formatter(log_format, datefmt='%m/%d %I:%M:%S %p')

    file_handler = logging.FileHandler(file_path)

    file_handler.setFormatter(formatter)

    stream_handler = logging.StreamHandler()

    stream_handler.setFormatter(formatter)

    logger.addHandler(file_handler)

    logger.addHandler(stream_handler)

    logger.setLevel(logging.INFO)

    return logger

def param_size(model):

    """ Compute parameter size in MB """

    n_params = sum(

        np.prod(v.size()) for k, v in model.named_parameters() if not k.startswith('aux_head'))

    return n_params / 1024. / 1024.

class AverageMeter():

    """ Computes and stores the average and current value """

    def __init__(self):

        self.reset()

    def reset(self):

        """ Reset all statistics """

        self.val = 0

        self.avg = 0

        self.sum = 0

        self.count = 1 # avoid the count of some calsses in the first batch is zero

    def update(self, val, n):

        """ Update statistics """

        self.val = val

        self.sum += val * n

        self.count += n

        self.avg = self.sum / self.count

def evaluate(logits, label):

    logits = logits.astype(np.float32)

    label = label.astype(np.float32)

    inter = np.dot(logits.flatten(), label.flatten())

    union = np.sum(logits) + np.sum(label)

    dice = (2 * inter + 1e-5) / (union + 1e-5)

    return dice

def save_results(results, path):

    filename = os.path.join(path, 'final_results.txt')

    f = open(filename, 'a')

    f.write('Best dice: {:.5f}\n'.format(results))

def save_checkpoint(state, ckpt_dir, is_best=False):

    filename = os.path.join(ckpt_dir, 'checkpoint.pth.tar')

    torch.save(state, filename)

    if is_best:

        best_filename = os.path.join(ckpt_dir, 'best.pth.tar')

        shutil.copyfile(filename, best_filename)

class log_loss(nn.Module):

    def __init__(self, w_dice = 0.5, w_cross = 0.5):

        super(log_loss, self).__init__()

        self.w_dice = w_dice

        self.w_cross = w_cross

    def forward(self, logits, label, smooth = 1.):

        area_union = torch.sum(logits * label, dim = (0,2,3), keepdim = True)

        area_logits = torch.sum(logits, dim = (0,2,3), keepdim = True)

        area_label = torch.sum(label, dim = (0,2,3), keepdim = True)

        in_dice = torch.mean(torch.pow((-1) * torch.log((2 * area_union + 1e-7)/(area_logits + area_label + smooth)), 0.3))

        return in_dice