# Modified from ``https://github.com/wyhsirius/LIA``
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math

import torch
import torch.nn as nn
from torch.nn import functional as F


def custom_qr(input_tensor):
    original_dtype = input_tensor.dtype
    if original_dtype == torch.bfloat16:
        q, r = torch.linalg.qr(input_tensor.to(torch.float32))
        return q.to(original_dtype), r.to(original_dtype)
    return torch.linalg.qr(input_tensor)


def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
    return F.leaky_relu(input + bias, negative_slope) * scale


def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
    _, minor, in_h, in_w = input.shape
    kernel_h, kernel_w = kernel.shape

    out = input.view(-1, minor, in_h, 1, in_w, 1)
    out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
    out = out.view(-1, minor, in_h * up_y, in_w * up_x)

    out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
    out = out[
        :,
        :,
        max(-pad_y0, 0) : out.shape[2] - max(-pad_y1, 0),
        max(-pad_x0, 0) : out.shape[3] - max(-pad_x1, 0),
    ]

    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    return out[:, :, ::down_y, ::down_x]


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])


def make_kernel(k):
    k = torch.tensor(k, dtype=torch.float32)
    if k.ndim == 1:
        k = k[None, :] * k[:, None]
    k /= k.sum()
    return k


class FusedLeakyReLU(nn.Module):
    def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
        super().__init__()
        self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input):
        out = fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
        return out


class Blur(nn.Module):
    def __init__(self, kernel, pad, upsample_factor=1):
        super().__init__()

        kernel = make_kernel(kernel)

        if upsample_factor > 1:
            kernel = kernel * (upsample_factor**2)

        self.register_buffer("kernel", kernel)

        self.pad = pad

    def forward(self, input):
        return upfirdn2d(input, self.kernel, pad=self.pad)


class ScaledLeakyReLU(nn.Module):
    def __init__(self, negative_slope=0.2):
        super().__init__()

        self.negative_slope = negative_slope

    def forward(self, input):
        return F.leaky_relu(input, negative_slope=self.negative_slope)


class EqualConv2d(nn.Module):
    def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True):
        super().__init__()

        self.weight = nn.Parameter(torch.randn(out_channel, in_channel, kernel_size, kernel_size))
        self.scale = 1 / math.sqrt(in_channel * kernel_size**2)

        self.stride = stride
        self.padding = padding

        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channel))
        else:
            self.bias = None

    def forward(self, input):
        return F.conv2d(input, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding)

    def __repr__(self):
        return f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]}, {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})"


class EqualLinear(nn.Module):
    def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None):
        super().__init__()

        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))

        if bias:
            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
        else:
            self.bias = None

        self.activation = activation

        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
        self.lr_mul = lr_mul

    def forward(self, input):
        if self.activation:
            out = F.linear(input, self.weight * self.scale)
            out = fused_leaky_relu(out, self.bias * self.lr_mul)
        else:
            out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)

        return out

    def __repr__(self):
        return f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"


class ConvLayer(nn.Sequential):
    def __init__(
        self,
        in_channel,
        out_channel,
        kernel_size,
        downsample=False,
        blur_kernel=[1, 3, 3, 1],
        bias=True,
        activate=True,
    ):
        layers = []

        if downsample:
            factor = 2
            p = (len(blur_kernel) - factor) + (kernel_size - 1)
            pad0 = (p + 1) // 2
            pad1 = p // 2

            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))

            stride = 2
            self.padding = 0

        else:
            stride = 1
            self.padding = kernel_size // 2

        layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=self.padding, stride=stride, bias=bias and not activate))

        if activate:
            if bias:
                layers.append(FusedLeakyReLU(out_channel))
            else:
                layers.append(ScaledLeakyReLU(0.2))

        super().__init__(*layers)


class ResBlock(nn.Module):
    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
        super().__init__()

        self.conv1 = ConvLayer(in_channel, in_channel, 3)
        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)

        self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False)

    def forward(self, input):
        out = self.conv1(input)
        out = self.conv2(out)

        skip = self.skip(input)
        out = (out + skip) / math.sqrt(2)

        return out


class EncoderApp(nn.Module):
    def __init__(self, size, w_dim=512):
        super(EncoderApp, self).__init__()

        channels = {4: 512, 8: 512, 16: 512, 32: 512, 64: 256, 128: 128, 256: 64, 512: 32, 1024: 16}

        self.w_dim = w_dim
        log_size = int(math.log(size, 2))

        self.convs = nn.ModuleList()
        self.convs.append(ConvLayer(3, channels[size], 1))

        in_channel = channels[size]
        for i in range(log_size, 2, -1):
            out_channel = channels[2 ** (i - 1)]
            self.convs.append(ResBlock(in_channel, out_channel))
            in_channel = out_channel

        self.convs.append(EqualConv2d(in_channel, self.w_dim, 4, padding=0, bias=False))

    def forward(self, x):
        res = []
        h = x
        for conv in self.convs:
            h = conv(h)
            res.append(h)

        return res[-1].squeeze(-1).squeeze(-1), res[::-1][2:]


class Encoder(nn.Module):
    def __init__(self, size, dim=512, dim_motion=20):
        super(Encoder, self).__init__()

        # appearance netmork
        self.net_app = EncoderApp(size, dim)

        # motion network
        fc = [EqualLinear(dim, dim)]
        for i in range(3):
            fc.append(EqualLinear(dim, dim))

        fc.append(EqualLinear(dim, dim_motion))
        self.fc = nn.Sequential(*fc)

    def enc_app(self, x):
        h_source = self.net_app(x)
        return h_source

    def enc_motion(self, x):
        h, _ = self.net_app(x)
        h_motion = self.fc(h)
        return h_motion


class Direction(nn.Module):
    def __init__(self, motion_dim):
        super(Direction, self).__init__()
        self.weight = nn.Parameter(torch.randn(512, motion_dim))

    def forward(self, input):
        weight = self.weight + 1e-8
        Q, R = custom_qr(weight)
        if input is None:
            return Q
        else:
            input_diag = torch.diag_embed(input)  # alpha, diagonal matrix
            out = torch.matmul(input_diag, Q.T)
            out = torch.sum(out, dim=1)
            return out


class Synthesis(nn.Module):
    def __init__(self, motion_dim):
        super(Synthesis, self).__init__()
        self.direction = Direction(motion_dim)


class Generator(nn.Module):
    def __init__(self, size, style_dim=512, motion_dim=20):
        super().__init__()

        self.enc = Encoder(size, style_dim, motion_dim)
        self.dec = Synthesis(motion_dim)

    def get_motion(self, img):
        motion_feat = self.enc.enc_motion(img)
        # motion_feat = torch.utils.checkpoint.checkpoint((self.enc.enc_motion), img, use_reentrant=True)
        with torch.amp.autocast("cuda", dtype=torch.float32):
            motion = self.dec.direction(motion_feat)
        return motion
