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import numpy as np
import torch

def normalize(x, dim=None, eps=1e-4):
    if dim is None:
        dim = list(range(1, x.ndim))
    norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)
    norm = torch.add(eps, norm, alpha=np.sqrt(norm.numel() / x.numel()))
    return x / norm.to(x.dtype)

class MPFourier(torch.nn.Module):
    def __init__(self, num_channels, bandwidth=1):
        super().__init__()
        self.register_buffer('freqs', 2 * np.pi * torch.randn(num_channels) * bandwidth)
        self.register_buffer('phases', 2 * np.pi * torch.rand(num_channels))

    def forward(self, x):
        y = x.to(torch.float32)
        y = y.ger(self.freqs.to(torch.float32))
        y = y + self.phases.to(torch.float32)
        y = y.cos() * np.sqrt(2)
        return y.to(x.dtype)

#----------------------------------------------------------------------------
# Magnitude-preserving convolution or fully-connected layer (Equation 47)
# with force weight normalization (Equation 66).

class MPConv(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel):
        super().__init__()
        self.out_channels = out_channels
        self.weight = torch.nn.Parameter(torch.randn(out_channels, in_channels, *kernel))

    def forward(self, x, gain=1):
        w = self.weight.to(torch.float32)
        if self.training:
            with torch.no_grad():
                self.weight.copy_(normalize(w)) # forced weight normalization
        w = normalize(w) # traditional weight normalization
        w = w * (gain / np.sqrt(w[0].numel())) # magnitude-preserving scaling
        w = w.to(x.dtype)
        assert w.ndim == 2
        return x @ w.t()




class EDM2NetworkWrapper(torch.nn.Module):
    def __init__(self,
        net,
        use_fp16        = True, # Run the model at FP16 precision?
        sigma_data      = 0.5,  # Expected standard deviation of the training data.
        logvar_channels = 128,  # Intermediate dimensionality for uncertainty estimation.
        **unet_kwargs,          # Keyword arguments for UNet.
    ):
        super().__init__()
        self.use_fp16 = use_fp16
        self.sigma_data = sigma_data
        self.net = net
        self.logvar_fourier = MPFourier(logvar_channels)
        self.logvar_linear = MPConv(logvar_channels, 1, kernel=[])

    def forward(self, x, sigma, force_fp32=False, return_logvar=False, **kwargs):
        x = x.to(torch.float32)
        sigma = sigma.to(torch.float32).reshape(-1,1,1)
        dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32

        # Preconditioning weights.
        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
        c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2).sqrt()
        c_in = 1 / (self.sigma_data ** 2 + sigma ** 2).sqrt()
        c_noise = sigma.flatten().log() / 4
        # Run the model.
        x_in = (c_in * x).to(dtype)
        F_x = self.net(x_in, c_noise, **kwargs)
        D_x = c_skip * x + c_out * F_x.to(torch.float32)

        # Estimate uncertainty if requested.
        if return_logvar:
            logvar = self.logvar_linear(self.logvar_fourier(c_noise)).reshape(-1,1,1)
            return D_x, logvar # u(sigma) in Equation 21
        return D_x


from tqdm import tqdm
def edm_sampler(
    net, noise, cond, 
    num_steps=32, sigma_min=0.002, sigma_max=80, rho=7, 
    S_churn=0, S_min=0, S_max=float('inf'), S_noise=1,
    dtype=torch.float32, randn_like=torch.randn_like,
    guidance=1.0,
):
    # Guided denoiser.
    def denoise(x, t):
        Dx = net(x, t, cond=cond).to(dtype)
        if guidance == 1:
            return Dx
        uncond = torch.zeros_like(cond).to(dtype).to(cond.device)
        ref_Dx = net(x, t, cond=uncond).to(dtype)
        return ref_Dx.lerp(Dx, guidance)
    
    # Time step discretization.
    step_indices = torch.arange(num_steps, dtype=dtype, device=noise.device)
    t_steps = (sigma_max ** (1 / rho) + step_indices / (num_steps - 1) * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))) ** rho
    t_steps = torch.cat([t_steps, torch.zeros_like(t_steps[:1])]) # t_N = 0

    # Main sampling loop.
    x_next = noise.to(dtype) * t_steps[0]
    for i, (t_cur, t_next) in tqdm(enumerate(zip(t_steps[:-1], t_steps[1:]))): # 0, ..., N-1
        x_cur = x_next

        # Increase noise temporarily.
        if S_churn > 0 and S_min <= t_cur <= S_max:
            gamma = min(S_churn / num_steps, np.sqrt(2) - 1)
            t_hat = t_cur + gamma * t_cur
            x_hat = x_cur + (t_hat ** 2 - t_cur ** 2).sqrt() * S_noise * randn_like(x_cur)
        else:
            t_hat = t_cur
            x_hat = x_cur

        # Euler step.
        d_cur = (x_hat - denoise(x_hat, t_hat)) / t_hat
        x_next = x_hat + (t_next - t_hat) * d_cur

        # Apply 2nd order correction.
        if i < num_steps - 1:
            d_prime = (x_next - denoise(x_next, t_next)) / t_next
            x_next = x_hat + (t_next - t_hat) * (0.5 * d_cur + 0.5 * d_prime)

    return x_next


if __name__ == "__main__":
    from wavenet import WaveNet
    io_channels = 16
    cond_channels = 192
    net = WaveNet(
        in_dims=io_channels,
        n_layers=20,
        n_chans=384,
        n_hidden=cond_channels
    )
    edm_net = EDM2NetworkWrapper(net)
    print(edm_net)
    


    diffusion_input = torch.randn(1,io_channels, 100)
    cond = torch.randn(1, cond_channels, 100)


    P_std = 1.0
    P_mean = -0.4
    sigma_data = 0.4


    rnd_normal = torch.randn([diffusion_input.shape[0], 1, 1], device=diffusion_input.device)
    sigma = (rnd_normal * P_std + P_mean).exp()
    weight = (sigma ** 2 + sigma_data ** 2) / (sigma * sigma_data) ** 2
    noise = torch.randn_like(diffusion_input) * sigma
    denoised, logvar = edm_net(diffusion_input + noise, sigma, cond=cond, return_logvar=True)
    loss = (weight / logvar.exp()) * ((denoised - diffusion_input) ** 2) + logvar
    loss = loss.mean()
    print(loss)


    noise = torch.randn([1, io_channels, 100]).to(cond.device)
    fakes = edm_sampler(edm_net, noise, cond, num_steps=32)
    print(fakes.shape)


	import numpy as np
	import torch

	def normalize(x, dim=None, eps=1e-4):
	if dim is None:
	dim = list(range(1, x.ndim))
	norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)
	norm = torch.add(eps, norm, alpha=np.sqrt(norm.numel() / x.numel()))
	return x / norm.to(x.dtype)

	class MPFourier(torch.nn.Module):
	def __init__(self, num_channels, bandwidth=1):
	super().__init__()
	self.register_buffer('freqs', 2 * np.pi * torch.randn(num_channels) * bandwidth)
	self.register_buffer('phases', 2 * np.pi * torch.rand(num_channels))

	def forward(self, x):
	y = x.to(torch.float32)
	y = y.ger(self.freqs.to(torch.float32))
	y = y + self.phases.to(torch.float32)
	y = y.cos() * np.sqrt(2)
	return y.to(x.dtype)

	#----------------------------------------------------------------------------
	# Magnitude-preserving convolution or fully-connected layer (Equation 47)
	# with force weight normalization (Equation 66).

	class MPConv(torch.nn.Module):
	def __init__(self, in_channels, out_channels, kernel):
	super().__init__()
	self.out_channels = out_channels
	self.weight = torch.nn.Parameter(torch.randn(out_channels, in_channels, *kernel))

	def forward(self, x, gain=1):
	w = self.weight.to(torch.float32)
	if self.training:
	with torch.no_grad():
	self.weight.copy_(normalize(w)) # forced weight normalization
	w = normalize(w) # traditional weight normalization
	w = w * (gain / np.sqrt(w[0].numel())) # magnitude-preserving scaling
	w = w.to(x.dtype)
	assert w.ndim == 2
	return x @ w.t()




	class EDM2NetworkWrapper(torch.nn.Module):
	def __init__(self,
	net,
	use_fp16 = True, # Run the model at FP16 precision?
	sigma_data = 0.5, # Expected standard deviation of the training data.
	logvar_channels = 128, # Intermediate dimensionality for uncertainty estimation.
	**unet_kwargs, # Keyword arguments for UNet.
	):
	super().__init__()
	self.use_fp16 = use_fp16
	self.sigma_data = sigma_data
	self.net = net
	self.logvar_fourier = MPFourier(logvar_channels)
	self.logvar_linear = MPConv(logvar_channels, 1, kernel=[])

	def forward(self, x, sigma, force_fp32=False, return_logvar=False, **kwargs):
	x = x.to(torch.float32)
	sigma = sigma.to(torch.float32).reshape(-1,1,1)
	dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32

	# Preconditioning weights.
	c_skip = self.sigma_data 2 / (sigma 2 + self.sigma_data ** 2)
	c_out = sigma * self.sigma_data / (sigma 2 + self.sigma_data 2).sqrt()
	c_in = 1 / (self.sigma_data 2 + sigma 2).sqrt()
	c_noise = sigma.flatten().log() / 4
	# Run the model.
	x_in = (c_in * x).to(dtype)
	F_x = self.net(x_in, c_noise, **kwargs)
	D_x = c_skip * x + c_out * F_x.to(torch.float32)

	# Estimate uncertainty if requested.
	if return_logvar:
	logvar = self.logvar_linear(self.logvar_fourier(c_noise)).reshape(-1,1,1)
	return D_x, logvar # u(sigma) in Equation 21
	return D_x


	from tqdm import tqdm
	def edm_sampler(
	net, noise, cond,
	num_steps=32, sigma_min=0.002, sigma_max=80, rho=7,
	S_churn=0, S_min=0, S_max=float('inf'), S_noise=1,
	dtype=torch.float32, randn_like=torch.randn_like,
	guidance=1.0,
	):
	# Guided denoiser.
	def denoise(x, t):
	Dx = net(x, t, cond=cond).to(dtype)
	if guidance == 1:
	return Dx
	uncond = torch.zeros_like(cond).to(dtype).to(cond.device)
	ref_Dx = net(x, t, cond=uncond).to(dtype)
	return ref_Dx.lerp(Dx, guidance)

	# Time step discretization.
	step_indices = torch.arange(num_steps, dtype=dtype, device=noise.device)
	t_steps = (sigma_max ** (1 / rho) + step_indices / (num_steps - 1) * (sigma_min (1 / rho) - sigma_max (1 / rho))) ** rho
	t_steps = torch.cat([t_steps, torch.zeros_like(t_steps[:1])]) # t_N = 0

	# Main sampling loop.
	x_next = noise.to(dtype) * t_steps[0]
	for i, (t_cur, t_next) in tqdm(enumerate(zip(t_steps[:-1], t_steps[1:]))): # 0, ..., N-1
	x_cur = x_next

	# Increase noise temporarily.
	if S_churn > 0 and S_min <= t_cur <= S_max:
	gamma = min(S_churn / num_steps, np.sqrt(2) - 1)
	t_hat = t_cur + gamma * t_cur
	x_hat = x_cur + (t_hat 2 - t_cur 2).sqrt() * S_noise * randn_like(x_cur)
	else:
	t_hat = t_cur
	x_hat = x_cur

	# Euler step.
	d_cur = (x_hat - denoise(x_hat, t_hat)) / t_hat
	x_next = x_hat + (t_next - t_hat) * d_cur

	# Apply 2nd order correction.
	if i < num_steps - 1:
	d_prime = (x_next - denoise(x_next, t_next)) / t_next
	x_next = x_hat + (t_next - t_hat) * (0.5 * d_cur + 0.5 * d_prime)

	return x_next


	if __name__ == "__main__":
	from wavenet import WaveNet
	io_channels = 16
	cond_channels = 192
	net = WaveNet(
	in_dims=io_channels,
	n_layers=20,
	n_chans=384,
	n_hidden=cond_channels
	)
	edm_net = EDM2NetworkWrapper(net)
	print(edm_net)



	diffusion_input = torch.randn(1,io_channels, 100)
	cond = torch.randn(1, cond_channels, 100)


	P_std = 1.0
	P_mean = -0.4
	sigma_data = 0.4


	rnd_normal = torch.randn([diffusion_input.shape[0], 1, 1], device=diffusion_input.device)
	sigma = (rnd_normal * P_std + P_mean).exp()
	weight = (sigma 2 + sigma_data 2) / (sigma * sigma_data) ** 2
	noise = torch.randn_like(diffusion_input) * sigma
	denoised, logvar = edm_net(diffusion_input + noise, sigma, cond=cond, return_logvar=True)
	loss = (weight / logvar.exp()) * ((denoised - diffusion_input) ** 2) + logvar
	loss = loss.mean()
	print(loss)


	noise = torch.randn([1, io_channels, 100]).to(cond.device)
	fakes = edm_sampler(edm_net, noise, cond, num_steps=32)
	print(fakes.shape)