"""
Credits to https://github.com/CompVis/taming-transformers
"""

from collections import namedtuple
import hashlib
import os
from pathlib import Path
import requests

import torch
import torch.nn as nn
from torchvision import models
from tqdm import tqdm


class LPIPS(nn.Module):
    # Learned perceptual metric
    def __init__(self, use_dropout: bool = True):
        super().__init__()
        self.scaling_layer = ScalingLayer()
        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) -> None:
        ckpt = get_ckpt_path(name="vgg_lpips", root=Path.home() / ".cache/iris/tokenizer_pretrained_vgg")  # Download VGG if necessary
        self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
        outs0, outs1 = self.net(in0_input), self.net(in1_input)
        feats0, feats1, diffs = {}, {}, {}
        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
        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))]
        val = res[0]
        for i in range(1, len(self.chns)):
            val += res[i]
        return val


class ScalingLayer(nn.Module):
    def __init__(self) -> None:
        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: torch.Tensor) -> torch.Tensor:
        return (inp - self.shift) / self.scale


class NetLinLayer(nn.Module):
    """ A single linear layer which does a 1x1 conv """
    def __init__(self, chn_in: int, chn_out: int = 1, use_dropout: bool = False) -> None:
        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: bool = False, pretrained: bool = True) -> None:
        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: torch.Tensor) -> torch.Tensor:
        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: torch.Tensor, eps: float = 1e-10) -> torch.Tensor:
    norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
    return x / (norm_factor + eps)


def spatial_average(x: torch.Tensor, keepdim: bool = True) -> torch.Tensor:
    return x.mean([2, 3], keepdim=keepdim)


# ********************************************************************
# *************** Utilities to download pretrained vgg ***************
# ********************************************************************


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: str, local_path: str, chunk_size: int = 1024) -> None:
    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: str) -> str:
    with open(path, "rb") as f:
        content = f.read()
    return hashlib.md5(content).hexdigest()


def get_ckpt_path(name: str, root: str, check: bool = False) -> str:
    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