import hashlib
import os

import requests
import torch
import torch.nn as nn
from tqdm import tqdm

URL_MAP = {
    'vgg_lpips':
    'https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1'
}

CKPT_MAP = {'vgg_lpips': 'vgg.pth'}

MD5_MAP = {'vgg_lpips': 'd507d7349b931f0638a25a48a722f98a'}


def download(url, local_path, chunk_size=1024):
    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
    with requests.get(url, stream=True) as r:
        total_size = int(r.headers.get('content-length', 0))
        with tqdm(total=total_size, unit='B', unit_scale=True) as pbar:
            with open(local_path, 'wb') as f:
                for data in r.iter_content(chunk_size=chunk_size):
                    if data:
                        f.write(data)
                        pbar.update(chunk_size)


def md5_hash(path):
    with open(path, 'rb') as f:
        content = f.read()
    return hashlib.md5(content).hexdigest()


def get_ckpt_path(name, root, check=False):
    assert name in URL_MAP
    path = os.path.join(root, CKPT_MAP[name])
    if not os.path.exists(path) or (check
                                    and not md5_hash(path) == MD5_MAP[name]):
        print('Downloading {} model from {} to {}'.format(
            name, URL_MAP[name], path))
        download(URL_MAP[name], path)
        md5 = md5_hash(path)
        assert md5 == MD5_MAP[name], md5
    return path


class ActNorm(nn.Module):

    def __init__(self,
                 num_features,
                 logdet=False,
                 affine=True,
                 allow_reverse_init=False):
        assert affine
        super().__init__()
        self.logdet = logdet
        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
        self.allow_reverse_init = allow_reverse_init

        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))

    def initialize(self, input):
        with torch.no_grad():
            flatten = input.permute(1, 0, 2,
                                    3).contiguous().view(input.shape[1], -1)
            mean = flatten.mean(1).unsqueeze(1).unsqueeze(2).unsqueeze(
                3).permute(1, 0, 2, 3)
            std = flatten.std(1).unsqueeze(1).unsqueeze(2).unsqueeze(
                3).permute(1, 0, 2, 3)

            self.loc.data.copy_(-mean)
            self.scale.data.copy_(1 / (std + 1e-6))

    def forward(self, input, reverse=False):
        if reverse:
            return self.reverse(input)
        if len(input.shape) == 2:
            input = input[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        _, _, height, width = input.shape

        if self.training and self.initialized.item() == 0:
            self.initialize(input)
            self.initialized.fill_(1)

        h = self.scale * (input + self.loc)

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)

        if self.logdet:
            log_abs = torch.log(torch.abs(self.scale))
            logdet = height * width * torch.sum(log_abs)
            logdet = logdet * torch.ones(input.shape[0]).to(input)
            return h, logdet

        return h

    def reverse(self, output):
        if self.training and self.initialized.item() == 0:
            if not self.allow_reverse_init:
                raise RuntimeError(
                    'Initializing ActNorm in reverse direction is '
                    'disabled by default. Use allow_reverse_init=True to enable.'
                )
            else:
                self.initialize(output)
                self.initialized.fill_(1)

        if len(output.shape) == 2:
            output = output[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        h = output / self.scale - self.loc

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)
        return h
