"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""

import torch
import torch.nn as nn
from torchvision import models
from collections import namedtuple
import torch.nn.functional as F


class LPIPS(nn.Module):
    # Learned perceptual metric
    def __init__(self, use_dropout=True):
        super().__init__()
        self.scaling_layer = ScalingLayer()
        self.scales = [1, 0.5, 0.25, 0.125] # multi-scale perceptual
        self.pyramid = ImagePyramide(self.scales, 3)
        self.chns = [64, 128, 256, 512, 512]  # vg16 features
        self.net = vgg16(pretrained=True, requires_grad=False)
        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
        self.load_from_pretrained()
        for param in self.parameters():
            param.requires_grad = False

    def load_from_pretrained(self, name="vgg_lpips"):
        ckpt = get_ckpt_path(name, "lpips")
        self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
        print("loaded pretrained LPIPS loss from {}".format(ckpt))

    @classmethod
    def from_pretrained(cls, name="vgg_lpips"):
        if name is not "vgg_lpips":
            raise NotImplementedError
        model = cls()
        ckpt = get_ckpt_path(name)
        model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
        return model

    def forward(self, input, target):
        value_total = 0
        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
        pyramide_real = self.pyramid(input)   ## real image
        pyramide_generated = self.pyramid(target)  ## prediction image
        for scale in self.scales:
            in0_input = self.scaling_layer(pyramide_generated['prediction_' + str(scale)])
            in1_input = self.scaling_layer(pyramide_real['prediction_' + str(scale)])
            outs0, outs1 = self.net(in0_input), self.net(in1_input)
            feats0, feats1, diffs = {}, {}, {}
            for kk in range(len(self.chns)):
                feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
                diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
            res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
            for l in range(len(self.chns)):
                value_total += res[l]
        return value_total

class ImagePyramide(torch.nn.Module):
    """
    Create image pyramide for computing pyramide perceptual loss.
    """
    def __init__(self, scales, num_channels=3):
        super(ImagePyramide, self).__init__()
        downs = {}
        for scale in scales:
            downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale)
        self.downs = nn.ModuleDict(downs)

    def forward(self, x):
        out_dict = {}
        for scale, down_module in self.downs.items():
            out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x)
        return out_dict
    
class AntiAliasInterpolation2d(nn.Module):
    """
    Band-limited downsampling, for better preservation of the input signal.
    """
    def __init__(self, channels, scale):
        super(AntiAliasInterpolation2d, self).__init__()
        sigma = (1 / scale - 1) / 2
        kernel_size = 2 * round(sigma * 4) + 1
        self.ka = kernel_size // 2
        self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka

        kernel_size = [kernel_size, kernel_size]
        sigma = [sigma, sigma]
        # The gaussian kernel is the product of the
        # gaussian function of each dimension.
        kernel = 1
        meshgrids = torch.meshgrid(
            [
                torch.arange(size, dtype=torch.float32)
                for size in kernel_size
                ]
        )
        for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
            mean = (size - 1) / 2
            kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))

        # Make sure sum of values in gaussian kernel equals 1.
        kernel = kernel / torch.sum(kernel)
        # Reshape to depthwise convolutional weight
        kernel = kernel.view(1, 1, *kernel.size())
        kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))

        self.register_buffer('weight', kernel)
        self.groups = channels
        self.scale = scale
        inv_scale = 1 / scale
        self.int_inv_scale = int(inv_scale)

    def forward(self, input):
        if self.scale == 1.0:
            return input

        out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))
        out = F.conv2d(out, weight=self.weight, groups=self.groups)
        out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale]

        return out

    
class ScalingLayer(nn.Module):
    def __init__(self):
        super(ScalingLayer, self).__init__()
        self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
        self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])

    def forward(self, inp):
        return (inp - self.shift) / self.scale


class NetLinLayer(nn.Module):
    """ A single linear layer which does a 1x1 conv """

    def __init__(self, chn_in, chn_out=1, use_dropout=False):
        super(NetLinLayer, self).__init__()
        layers = [nn.Dropout(), ] if (use_dropout) else []
        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
        self.model = nn.Sequential(*layers)


class vgg16(torch.nn.Module):
    def __init__(self, requires_grad=False, pretrained=True):
        super(vgg16, self).__init__()
        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        self.N_slices = 5
        for x in range(4):
            self.slice1.add_module(str(x), vgg_pretrained_features[x])
        for x in range(4, 9):
            self.slice2.add_module(str(x), vgg_pretrained_features[x])
        for x in range(9, 16):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(16, 23):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(23, 30):
            self.slice5.add_module(str(x), vgg_pretrained_features[x])
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, X):
        h = self.slice1(X)
        h_relu1_2 = h
        h = self.slice2(h)
        h_relu2_2 = h
        h = self.slice3(h)
        h_relu3_3 = h
        h = self.slice4(h)
        h_relu4_3 = h
        h = self.slice5(h)
        h_relu5_3 = h
        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
        return out


def normalize_tensor(x, eps=1e-10):
    norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
    return x / (norm_factor + eps)


def spatial_average(x, keepdim=True):
    return x.mean([2, 3], keepdim=keepdim)


import os, hashlib
import requests
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
