r"""LPIPS Model.

Created by: https://github.com/richzhang/PerceptualSimilarity.

Modified by: Jiadi Mo (https://github.com/JiadiMo)

"""

from collections import namedtuple

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

from pyiqa.archs.arch_util import load_pretrained_network
from pyiqa.utils.registry import ARCH_REGISTRY

default_model_urls = {
    # key "url" is the default
    "0.0_alex": "https://github.com/chaofengc/IQA-Toolbox-Python/releases/download/v0.1-weights/LPIPS_v0.0_alex-18720f55.pth",
    "0.0_vgg": "https://github.com/chaofengc/IQA-Toolbox-Python/releases/download/v0.1-weights/LPIPS_v0.0_vgg-b9e42362.pth",
    "0.0_squeeze": "https://github.com/chaofengc/IQA-Toolbox-Python/releases/download/v0.1-weights/LPIPS_v0.0_squeeze-c27abd3a.pth",
    "0.1_alex": "https://github.com/chaofengc/IQA-Toolbox-Python/releases/download/v0.1-weights/LPIPS_v0.1_alex-df73285e.pth",
    "0.1_vgg": "https://github.com/chaofengc/IQA-Toolbox-Python/releases/download/v0.1-weights/LPIPS_v0.1_vgg-a78928a0.pth",
    "0.1_squeeze": "https://github.com/chaofengc/IQA-PyTorch/releases/download/v0.1-weights/LPIPS_v0.1_squeeze-4a5350f2.pth",
}


def upsample(in_tens, out_HW=(64, 64)):  # assumes scale factor is same for H and W
    return nn.Upsample(size=out_HW, mode="bilinear", align_corners=False)(in_tens)


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


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


@ARCH_REGISTRY.register()
class LPIPS(nn.Module):
    """LPIPS model.
    Args:
        lpips (Boolean) : Whether to use linear layers on top of base/trunk network.
        pretrained (Boolean): Whether means linear layers are calibrated with human
            perceptual judgments.
        pnet_rand (Boolean): Whether to randomly initialized trunk.
        net (String): ['alex','vgg','squeeze'] are the base/trunk networks available.
        version (String): choose the version ['v0.1'] is the default and latest;
            ['v0.0'] contained a normalization bug.
        pretrained_model_path (String): Petrained model path.

        The following parameters should only be changed if training the network:

        eval_mode (Boolean): choose the mode; True is for test mode (default).
        pnet_tune (Boolean): Whether to tune the base/trunk network.
        use_dropout (Boolean): Whether to use dropout when training linear layers.

    Reference:
        Zhang, Richard, et al. "The unreasonable effectiveness of deep features as
        a perceptual metric." Proceedings of the IEEE conference on computer vision
        and pattern recognition. 2018.

    """

    def __init__(
        self,
        pretrained=True,
        net="alex",
        version="0.1",
        lpips=True,
        spatial=False,
        pnet_rand=False,
        pnet_tune=False,
        use_dropout=True,
        pretrained_model_path=None,
        eval_mode=True,
        **kwargs,
    ):

        super(LPIPS, self).__init__()

        self.pnet_type = net
        self.pnet_tune = pnet_tune
        self.pnet_rand = pnet_rand
        self.spatial = spatial
        self.lpips = lpips  # false means baseline of just averaging all layers
        self.version = version
        self.scaling_layer = ScalingLayer()

        if self.pnet_type in ["vgg", "vgg16"]:
            net_type = vgg16
            self.chns = [64, 128, 256, 512, 512]
        elif self.pnet_type == "alex":
            net_type = alexnet
            self.chns = [64, 192, 384, 256, 256]
        elif self.pnet_type == "squeeze":
            net_type = squeezenet
            self.chns = [64, 128, 256, 384, 384, 512, 512]
        self.L = len(self.chns)

        self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)

        if lpips:
            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.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
            if self.pnet_type == "squeeze":  # 7 layers for squeezenet
                self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)
                self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)
                self.lins += [self.lin5, self.lin6]
            self.lins = nn.ModuleList(self.lins)

            if pretrained_model_path is not None:
                load_pretrained_network(self, pretrained_model_path, False)
            elif pretrained:
                load_pretrained_network(
                    self, default_model_urls[f"{version}_{net}"], False
                )

        if eval_mode:
            self.eval()

    def forward(self, in1, in0, retPerLayer=False, normalize=True):
        r"""Computation IQA using LPIPS.
        Args:
            in1: An input tensor. Shape :math:`(N, C, H, W)`.
            in0: A reference tensor. Shape :math:`(N, C, H, W)`.
            retPerLayer (Boolean): return result contains ressult of
                each layer or not. Default: False.
            normalize (Boolean): Whether to normalize image data range
                in [0,1] to [-1,1]. Default: True.

        Returns:
            Quality score.

        """
        if (
            normalize
        ):  # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1]
            in0 = 2 * in0 - 1
            in1 = 2 * in1 - 1

        # v0.0 - original release had a bug, where input was not scaled
        in0_input, in1_input = (
            (self.scaling_layer(in0), self.scaling_layer(in1))
            if self.version == "0.1"
            else (in0, in1)
        )
        outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input)
        feats0, feats1, diffs = {}, {}, {}

        for kk in range(self.L):
            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(
                outs1[kk]
            )
            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2

        if self.lpips:
            if self.spatial:
                res = [
                    upsample(self.lins[kk](diffs[kk]), out_HW=in0.shape[2:])
                    for kk in range(self.L)
                ]
            else:
                res = [
                    spatial_average(self.lins[kk](diffs[kk]), keepdim=True)
                    for kk in range(self.L)
                ]
        else:
            if self.spatial:
                res = [
                    upsample(diffs[kk].sum(dim=1, keepdim=True), out_HW=in0.shape[2:])
                    for kk in range(self.L)
                ]
            else:
                res = [
                    spatial_average(diffs[kk].sum(dim=1, keepdim=True), keepdim=True)
                    for kk in range(self.L)
                ]

        val = 0
        for i in range(self.L):
            val += res[i]

        if retPerLayer:
            return (val, res)
        else:
            return val.squeeze()


class ScalingLayer(nn.Module):
    def __init__(self):
        super(ScalingLayer, self).__init__()
        self.register_buffer(
            "shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None]
        )
        self.register_buffer(
            "scale", torch.Tensor([0.458, 0.448, 0.450])[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)

    def forward(self, x):
        return self.model(x)


class squeezenet(torch.nn.Module):
    def __init__(self, requires_grad=False, pretrained=True):
        super(squeezenet, self).__init__()
        pretrained_features = models.squeezenet1_1(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.slice6 = torch.nn.Sequential()
        self.slice7 = torch.nn.Sequential()
        self.N_slices = 7
        for x in range(2):
            self.slice1.add_module(str(x), pretrained_features[x])
        for x in range(2, 5):
            self.slice2.add_module(str(x), pretrained_features[x])
        for x in range(5, 8):
            self.slice3.add_module(str(x), pretrained_features[x])
        for x in range(8, 10):
            self.slice4.add_module(str(x), pretrained_features[x])
        for x in range(10, 11):
            self.slice5.add_module(str(x), pretrained_features[x])
        for x in range(11, 12):
            self.slice6.add_module(str(x), pretrained_features[x])
        for x in range(12, 13):
            self.slice7.add_module(str(x), 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 = h
        h = self.slice2(h)
        h_relu2 = h
        h = self.slice3(h)
        h_relu3 = h
        h = self.slice4(h)
        h_relu4 = h
        h = self.slice5(h)
        h_relu5 = h
        h = self.slice6(h)
        h_relu6 = h
        h = self.slice7(h)
        h_relu7 = h
        vgg_outputs = namedtuple(
            "SqueezeOutputs",
            ["relu1", "relu2", "relu3", "relu4", "relu5", "relu6", "relu7"],
        )
        out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7)

        return out


class alexnet(torch.nn.Module):
    def __init__(self, requires_grad=False, pretrained=True):
        super(alexnet, self).__init__()
        alexnet_pretrained_features = models.alexnet(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(2):
            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
        for x in range(2, 5):
            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
        for x in range(5, 8):
            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
        for x in range(8, 10):
            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
        for x in range(10, 12):
            self.slice5.add_module(str(x), alexnet_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 = h
        h = self.slice2(h)
        h_relu2 = h
        h = self.slice3(h)
        h_relu3 = h
        h = self.slice4(h)
        h_relu4 = h
        h = self.slice5(h)
        h_relu5 = h
        alexnet_outputs = namedtuple(
            "AlexnetOutputs", ["relu1", "relu2", "relu3", "relu4", "relu5"]
        )
        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)

        return out


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


class resnet(torch.nn.Module):
    def __init__(self, requires_grad=False, pretrained=True, num=18):
        super(resnet, self).__init__()
        if num == 18:
            self.net = models.resnet18(pretrained=pretrained)
        elif num == 34:
            self.net = models.resnet34(pretrained=pretrained)
        elif num == 50:
            self.net = models.resnet50(pretrained=pretrained)
        elif num == 101:
            self.net = models.resnet101(pretrained=pretrained)
        elif num == 152:
            self.net = models.resnet152(pretrained=pretrained)
        self.N_slices = 5

        self.conv1 = self.net.conv1
        self.bn1 = self.net.bn1
        self.relu = self.net.relu
        self.maxpool = self.net.maxpool
        self.layer1 = self.net.layer1
        self.layer2 = self.net.layer2
        self.layer3 = self.net.layer3
        self.layer4 = self.net.layer4

    def forward(self, X):
        h = self.conv1(X)
        h = self.bn1(h)
        h = self.relu(h)
        h_relu1 = h
        h = self.maxpool(h)
        h = self.layer1(h)
        h_conv2 = h
        h = self.layer2(h)
        h_conv3 = h
        h = self.layer3(h)
        h_conv4 = h
        h = self.layer4(h)
        h_conv5 = h

        outputs = namedtuple("Outputs", ["relu1", "conv2", "conv3", "conv4", "conv5"])
        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)

        return out
