import math
import multiprocessing
from contextlib import contextmanager, ExitStack

import torch
import torch.nn.functional as F
from kornia.filters import filter2D
from linear_attention_transformer import ImageLinearAttention
from torch import nn
from torch.autograd import grad as torch_grad

assert torch.cuda.is_available(), 'You need to have an Nvidia GPU with CUDA installed.'

num_cores = multiprocessing.cpu_count()

# constants
EPS = 1e-8


class NanException(Exception):
    pass


class EMA():
    def __init__(self, beta):
        super().__init__()
        self.beta = beta

    def update_average(self, old, new):
        if not exists(old):
            return new
        return old * self.beta + (1 - self.beta) * new


class Flatten(nn.Module):
    def forward(self, x):
        return x.reshape(x.shape[0], -1)


class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x):
        return self.fn(x) + x


class Rezero(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
        self.g = nn.Parameter(torch.zeros(1))

    def forward(self, x):
        return self.fn(x) * self.g


class PermuteToFrom(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x):
        x = x.permute(0, 2, 3, 1)
        out, loss = self.fn(x)
        out = out.permute(0, 3, 1, 2)
        return out, loss


class Blur(nn.Module):
    def __init__(self):
        super().__init__()
        f = torch.Tensor([1, 2, 1])
        self.register_buffer('f', f)

    def forward(self, x):
        f = self.f
        f = f[None, None, :] * f[None, :, None]
        return filter2D(x, f, normalized=True)


# one layer of self-attention and feedforward, for images

attn_and_ff = lambda chan: nn.Sequential(*[
    Residual(Rezero(ImageLinearAttention(chan, norm_queries=True))),
    Residual(Rezero(nn.Sequential(nn.Conv2d(chan, chan * 2, 1), leaky_relu(), nn.Conv2d(chan * 2, chan, 1))))
])


# helpers

def exists(val):
    return val is not None


@contextmanager
def null_context():
    yield


def combine_contexts(contexts):
    @contextmanager
    def multi_contexts():
        with ExitStack() as stack:
            yield [stack.enter_context(ctx()) for ctx in contexts]

    return multi_contexts


def default(value, d):
    return value if exists(value) else d


def cycle(iterable):
    while True:
        for i in iterable:
            yield i


def cast_list(el):
    return el if isinstance(el, list) else [el]


def is_empty(t):
    if isinstance(t, torch.Tensor):
        return t.nelement() == 0
    return not exists(t)


def raise_if_nan(t):
    if torch.isnan(t):
        raise NanException


def gradient_accumulate_contexts(gradient_accumulate_every, is_ddp, ddps):
    if is_ddp:
        num_no_syncs = gradient_accumulate_every - 1
        head = [combine_contexts(map(lambda ddp: ddp.no_sync, ddps))] * num_no_syncs
        tail = [null_context]
        contexts = head + tail
    else:
        contexts = [null_context] * gradient_accumulate_every

    for context in contexts:
        with context():
            yield


def loss_backwards(fp16, loss, optimizer, loss_id, **kwargs):
    if fp16:
        with amp.scale_loss(loss, optimizer, loss_id) as scaled_loss:
            scaled_loss.backward(**kwargs)
    else:
        loss.backward(**kwargs)

def calc_pl_lengths(styles, images):
    device = images.device
    num_pixels = images.shape[2] * images.shape[3]
    pl_noise = torch.randn(images.shape, device=device) / math.sqrt(num_pixels)
    outputs = (images * pl_noise).sum()

    pl_grads = torch_grad(outputs=outputs, inputs=styles,
                          grad_outputs=torch.ones(outputs.shape, device=device),
                          create_graph=True, retain_graph=True, only_inputs=True)[0]

    return (pl_grads ** 2).sum(dim=2).mean(dim=1).sqrt()


def image_noise(n, im_size, device):
    return torch.FloatTensor(n, im_size, im_size, 1).uniform_(0., 1.).cuda(device)


def leaky_relu(p=0.2):
    return nn.LeakyReLU(p, inplace=True)


def evaluate_in_chunks(max_batch_size, model, *args):
    split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
    chunked_outputs = [model(*i) for i in split_args]
    if len(chunked_outputs) == 1:
        return chunked_outputs[0]
    return torch.cat(chunked_outputs, dim=0)


def set_requires_grad(model, bool):
    for p in model.parameters():
        p.requires_grad = bool


def slerp(val, low, high):
    low_norm = low / torch.norm(low, dim=1, keepdim=True)
    high_norm = high / torch.norm(high, dim=1, keepdim=True)
    omega = torch.acos((low_norm * high_norm).sum(1))
    so = torch.sin(omega)
    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
    return res

# stylegan2 classes

class EqualLinear(nn.Module):
    def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_dim))

        self.lr_mul = lr_mul

    def forward(self, input):
        return F.linear(input, self.weight * self.lr_mul, bias=self.bias * self.lr_mul)


class StyleVectorizer(nn.Module):
    def __init__(self, emb, depth, lr_mul=0.1):
        super().__init__()

        layers = []
        for i in range(depth):
            layers.extend([EqualLinear(emb, emb, lr_mul), leaky_relu()])

        self.net = nn.Sequential(*layers)

    def forward(self, x):
        x = F.normalize(x, dim=1)
        return self.net(x)


class RGBBlock(nn.Module):
    def __init__(self, latent_dim, input_channel, upsample, rgba=False):
        super().__init__()
        self.input_channel = input_channel
        self.to_style = nn.Linear(latent_dim, input_channel)

        out_filters = 3 if not rgba else 4
        self.conv = Conv2DMod(input_channel, out_filters, 1, demod=False)

        self.upsample = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
            Blur()
        ) if upsample else None

    def forward(self, x, prev_rgb, istyle):
        b, c, h, w = x.shape
        style = self.to_style(istyle)
        x = self.conv(x, style)

        if exists(prev_rgb):
            x = x + prev_rgb

        if exists(self.upsample):
            x = self.upsample(x)

        return x


class AdaptiveInstanceNorm(nn.Module):
    def __init__(self, in_channel, style_dim):
        super().__init__()
        from models.archs.arch_util import ConvGnLelu
        self.style2scale = ConvGnLelu(style_dim, in_channel, kernel_size=1, norm=False, activation=False, bias=True)
        self.style2bias = ConvGnLelu(style_dim, in_channel, kernel_size=1, norm=False, activation=False, bias=True, weight_init_factor=0)
        self.norm = nn.InstanceNorm2d(in_channel)

    def forward(self, input, style):
        gamma = self.style2scale(style)
        beta = self.style2bias(style)
        out = self.norm(input)
        out = gamma * out + beta
        return out


class NoiseInjection(nn.Module):
    def __init__(self, channel):
        super().__init__()
        self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))

    def forward(self, image, noise):
        return image + self.weight * noise


class EqualLR:
    def __init__(self, name):
        self.name = name

    def compute_weight(self, module):
        weight = getattr(module, self.name + '_orig')
        fan_in = weight.data.size(1) * weight.data[0][0].numel()

        return weight * math.sqrt(2 / fan_in)

    @staticmethod
    def apply(module, name):
        fn = EqualLR(name)

        weight = getattr(module, name)
        del module._parameters[name]
        module.register_parameter(name + '_orig', nn.Parameter(weight.data))
        module.register_forward_pre_hook(fn)

        return fn

    def __call__(self, module, input):
        weight = self.compute_weight(module)
        setattr(module, self.name, weight)


def equal_lr(module, name='weight'):
    EqualLR.apply(module, name)

    return module


class EqualConv2d(nn.Module):
    def __init__(self, *args, **kwargs):
        super().__init__()

        conv = nn.Conv2d(*args, **kwargs)
        conv.weight.data.normal_()
        conv.bias.data.zero_()
        self.conv = equal_lr(conv)

    def forward(self, input):
        return self.conv(input)


class Conv2DMod(nn.Module):
    def __init__(self, in_chan, out_chan, kernel, demod=True, stride=1, dilation=1, **kwargs):
        super().__init__()
        self.filters = out_chan
        self.demod = demod
        self.kernel = kernel
        self.stride = stride
        self.dilation = dilation
        self.weight = nn.Parameter(torch.randn((out_chan, in_chan, kernel, kernel)))
        nn.init.kaiming_normal_(self.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')

    def _get_same_padding(self, size, kernel, dilation, stride):
        return ((size - 1) * (stride - 1) + dilation * (kernel - 1)) // 2

    def forward(self, x, y):
        b, c, h, w = x.shape

        w1 = y[:, None, :, None, None]
        w2 = self.weight[None, :, :, :, :]
        weights = w2 * (w1 + 1)

        if self.demod:
            d = torch.rsqrt((weights ** 2).sum(dim=(2, 3, 4), keepdim=True) + EPS)
            weights = weights * d

        x = x.reshape(1, -1, h, w)

        _, _, *ws = weights.shape
        weights = weights.reshape(b * self.filters, *ws)

        padding = self._get_same_padding(h, self.kernel, self.dilation, self.stride)
        x = F.conv2d(x, weights, padding=padding, groups=b)

        x = x.reshape(-1, self.filters, h, w)
        return x


class GeneratorBlock(nn.Module):
    def __init__(self, latent_dim, input_channels, filters, upsample=True, upsample_rgb=True, rgba=False, structure_input=False):
        super().__init__()
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) if upsample else None

        self.structure_input = structure_input
        if self.structure_input:
            self.structure_conv = nn.Conv2d(3, input_channels, 3, padding=1)
            input_channels = input_channels * 2

        self.to_style1 = nn.Linear(latent_dim, input_channels)
        self.to_noise1 = nn.Linear(1, filters)
        self.conv1 = Conv2DMod(input_channels, filters, 3)

        self.to_style2 = nn.Linear(latent_dim, filters)
        self.to_noise2 = nn.Linear(1, filters)
        self.conv2 = Conv2DMod(filters, filters, 3)

        self.activation = leaky_relu()
        self.to_rgb = RGBBlock(latent_dim, filters, upsample_rgb, rgba)

    def forward(self, x, prev_rgb, istyle, inoise, structure_input=None):
        if exists(self.upsample):
            x = self.upsample(x)

        if self.structure_input:
            s = self.structure_conv(structure_input)
            x = torch.cat([x, s], dim=1)

        inoise = inoise[:, :x.shape[2], :x.shape[3], :]
        noise1 = self.to_noise1(inoise).permute((0, 3, 2, 1))
        noise2 = self.to_noise2(inoise).permute((0, 3, 2, 1))

        style1 = self.to_style1(istyle)
        x = self.conv1(x, style1)
        x = self.activation(x + noise1)

        style2 = self.to_style2(istyle)
        x = self.conv2(x, style2)
        x = self.activation(x + noise2)

        rgb = self.to_rgb(x, prev_rgb, istyle)
        return x, rgb