import yaml
import glob
import os
import sys
import time
import math
from datetime import datetime
import random
import logging
from collections import OrderedDict

import natsort
import numpy as np
import cv2
import torch
from torchvision.utils import make_grid
from shutil import get_terminal_size
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp


def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 /
                         float(2 * sigma ** 2)) for x in range(window_size)])
    return gauss / gauss.sum()


def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(
        _1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = Variable(_2D_window.expand(
        channel, 1, window_size, window_size).contiguous())
    return window


def _ssim(img1, img2, window, window_size, channel, size_average=True):
    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = F.conv2d(img1 * img1, window,
                         padding=window_size // 2, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window,
                         padding=window_size // 2, groups=channel) - mu2_sq
    sigma12 = F.conv2d(img1 * img2, window,
                       padding=window_size // 2, groups=channel) - mu1_mu2

    C1 = 0.01 ** 2
    C2 = 0.03 ** 2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \
        ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1).mean(1).mean(1)


class SSIM(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True):
        super(SSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = 1
        self.window = create_window(window_size, self.channel)

    def forward(self, img1, img2):
        (_, channel, _, _) = img1.size()

        if channel == self.channel and self.window.data.type() == img1.data.type():
            window = self.window
        else:
            window = create_window(self.window_size, channel)

            if img1.is_cuda:
                window = window.cuda(img1.get_device())
            window = window.type_as(img1)

            self.window = window
            self.channel = channel

        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)


def ssim(img1, img2, window_size=11, size_average=True):
    (_, channel, _, _) = img1.size()
    window = create_window(window_size, channel)

    if img1.is_cuda:
        window = window.cuda(img1.get_device())
    window = window.type_as(img1)

    return _ssim(img1.mean(dim=0, keepdims=True), img2.mean(dim=0, keepdims=True), window, window_size, channel, size_average)


try:
    from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
    from yaml import Loader, Dumper


def OrderedYaml():
    '''yaml orderedDict support'''
    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG

    def dict_representer(dumper, data):
        return dumper.represent_dict(data.items())

    def dict_constructor(loader, node):
        return OrderedDict(loader.construct_pairs(node))

    Dumper.add_representer(OrderedDict, dict_representer)
    Loader.add_constructor(_mapping_tag, dict_constructor)
    return Loader, Dumper


####################
# miscellaneous
####################


def get_timestamp():
    return datetime.now().strftime('%y%m%d-%H%M%S')


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)


def mkdirs(paths):
    if isinstance(paths, str):
        mkdir(paths)
    else:
        for path in paths:
            mkdir(path)


def mkdir_and_rename(path):
    if os.path.exists(path):
        new_name = path + '_archived_' + get_timestamp()
        print('Path already exists. Rename it to [{:s}]'.format(new_name))
        logger = logging.getLogger('base')
        logger.info(
            'Path already exists. Rename it to [{:s}]'.format(new_name))
        os.rename(path, new_name)
    os.makedirs(path)


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


def setup_logger(logger_name, root, phase, level=logging.INFO, screen=False, tofile=False):
    '''set up logger'''
    lg = logging.getLogger(logger_name)
    formatter = logging.Formatter('%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s',
                                  datefmt='%y-%m-%d %H:%M:%S')
    lg.setLevel(level)
    if tofile:
        log_file = os.path.join(
            root, phase + '_{}.log'.format(get_timestamp()))
        fh = logging.FileHandler(log_file, mode='w')
        fh.setFormatter(formatter)
        lg.addHandler(fh)
    if screen:
        sh = logging.StreamHandler()
        sh.setFormatter(formatter)
        lg.addHandler(sh)


####################
# image convert
####################


def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
    '''
    Converts a torch Tensor into an image Numpy array
    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
    '''
    if hasattr(tensor, 'detach'):
        tensor = tensor.detach()
    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # clamp
    tensor = (tensor - min_max[0]) / \
        (min_max[1] - min_max[0])  # to range [0,1]
    n_dim = tensor.dim()
    if n_dim == 4:
        n_img = len(tensor)
        img_np = make_grid(tensor, nrow=int(
            math.sqrt(n_img)), normalize=False).numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 3:
        img_np = tensor.numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 2:
        img_np = tensor.numpy()
    else:
        raise TypeError(
            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
    if out_type == np.uint8:
        img_np = np.clip((img_np * 255.0).round(), 0, 255)
        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
    return img_np.astype(out_type)


def save_img(img, img_path, mode='RGB'):
    cv2.imwrite(img_path, img)


####################
# metric
####################


def calculate_psnr(img1, img2):
    # img1 and img2 have range [0, 255]
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))


def get_resume_paths(opt):
    resume_state_path = None
    resume_model_path = None
    ts = opt_get(opt, ['path', 'training_state'])
    if opt.get('path', {}).get('resume_state', None) == "auto" and ts is not None:
        wildcard = os.path.join(ts, "*")
        paths = natsort.natsorted(glob.glob(wildcard))
        if len(paths) > 0:
            resume_state_path = paths[-1]
            resume_model_path = resume_state_path.replace(
                'training_state', 'models').replace('.state', '_G.pth')
    else:
        resume_state_path = opt.get('path', {}).get('resume_state')
    return resume_state_path, resume_model_path


def opt_get(opt, keys, default=None):
    if opt is None:
        return default
    ret = opt
    for k in keys:
        ret = ret.get(k, None)
        if ret is None:
            return default
    return ret