# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

"""Project given image to the latent space of pretrained network pickle."""

import copy
import os
from time import perf_counter

import click
import imageio
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F

import dnnlib
import legacy


def project(
    G,
    target: torch.Tensor,  # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
    *,
    num_steps=1000,
    w_avg_samples=10000,
    initial_learning_rate=0.1,
    initial_noise_factor=0.05,
    lr_rampdown_length=0.25,
    lr_rampup_length=0.05,
    noise_ramp_length=0.75,
    regularize_noise_weight=1e5,
    verbose=False,
    device: torch.device,
):
    assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution)

    def logprint(*args):
        if verbose:
            print(*args)

    G = copy.deepcopy(G).eval().requires_grad_(False).to(device)  # type: ignore

    # Compute w stats.
    logprint(f"Computing W midpoint and stddev using {w_avg_samples} samples...")
    z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
    w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None)  # [N, L, C]
    w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32)  # [N, 1, C]
    w_avg = np.mean(w_samples, axis=0, keepdims=True)  # [1, 1, C]
    w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5

    # Setup noise inputs.
    noise_bufs = {
        name: buf
        for (name, buf) in G.synthesis.named_buffers()
        if "noise_const" in name
    }

    # Load VGG16 feature detector.
    url = "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt"
    with dnnlib.util.open_url(url) as f:
        vgg16 = torch.jit.load(f).eval().to(device)

    # Features for target image.
    target_images = target.unsqueeze(0).to(device).to(torch.float32)
    if target_images.shape[2] > 256:
        target_images = F.interpolate(target_images, size=(256, 256), mode="area")
    target_features = vgg16(target_images, resize_images=False, return_lpips=True)

    w_opt = torch.tensor(
        w_avg, dtype=torch.float32, device=device, requires_grad=True
    )  # pylint: disable=not-callable
    w_out = torch.zeros(
        [num_steps] + list(w_opt.shape[1:]), dtype=torch.float32, device=device
    )
    optimizer = torch.optim.Adam(
        [w_opt] + list(noise_bufs.values()),
        betas=(0.9, 0.999),
        lr=initial_learning_rate,
    )

    # Init noise.
    for buf in noise_bufs.values():
        buf[:] = torch.randn_like(buf)
        buf.requires_grad = True

    for step in range(num_steps):
        # Learning rate schedule.
        t = step / num_steps
        w_noise_scale = (
            w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2
        )
        lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
        lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
        lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
        lr = initial_learning_rate * lr_ramp
        for param_group in optimizer.param_groups:
            param_group["lr"] = lr

        # Synth images from opt_w.
        w_noise = torch.randn_like(w_opt) * w_noise_scale
        ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1])
        synth_images = G.synthesis(ws, noise_mode="const")

        # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
        synth_images = (synth_images + 1) * (255 / 2)
        if synth_images.shape[2] > 256:
            synth_images = F.interpolate(synth_images, size=(256, 256), mode="area")

        # Features for synth images.
        synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
        dist = (target_features - synth_features).square().sum()

        # Noise regularization.
        reg_loss = 0.0
        for v in noise_bufs.values():
            noise = v[None, None, :, :]  # must be [1,1,H,W] for F.avg_pool2d()
            while True:
                reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2
                reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2
                if noise.shape[2] <= 8:
                    break
                noise = F.avg_pool2d(noise, kernel_size=2)
        loss = dist + reg_loss * regularize_noise_weight

        # Step
        optimizer.zero_grad(set_to_none=True)
        loss.backward()
        optimizer.step()
        logprint(
            f"step {step+1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}"
        )

        # Save projected W for each optimization step.
        w_out[step] = w_opt.detach()[0]

        # Normalize noise.
        with torch.no_grad():
            for buf in noise_bufs.values():
                buf -= buf.mean()
                buf *= buf.square().mean().rsqrt()

    return w_out.repeat([1, G.mapping.num_ws, 1])


# ----------------------------------------------------------------------------


@click.command()
@click.option("--network", "network_pkl", help="Network pickle filename", required=True)
@click.option(
    "--target",
    "target_fname",
    help="Target image file to project to",
    required=True,
    metavar="FILE",
)
@click.option(
    "--num-steps",
    help="Number of optimization steps",
    type=int,
    default=1000,
    show_default=True,
)
@click.option("--seed", help="Random seed", type=int, default=303, show_default=True)
@click.option(
    "--save-video",
    help="Save an mp4 video of optimization progress",
    type=bool,
    default=True,
    show_default=True,
)
@click.option(
    "--outdir", help="Where to save the output images", required=True, metavar="DIR"
)
def run_projection(
    network_pkl: str,
    target_fname: str,
    outdir: str,
    save_video: bool,
    seed: int,
    num_steps: int,
):
    """Project given image to the latent space of pretrained network pickle.

    Examples:

    \b
    python projector.py --outdir=out --target=~/mytargetimg.png \\
        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl
    """
    np.random.seed(seed)
    torch.manual_seed(seed)

    # Load networks.
    print('Loading networks from "%s"...' % network_pkl)
    device = torch.device("cuda")
    with dnnlib.util.open_url(network_pkl) as fp:
        G = (
            legacy.load_network_pkl(fp)["G_ema"].requires_grad_(False).to(device)
        )  # type: ignore

    # Load target image.
    target_pil = PIL.Image.open(target_fname).convert("RGB")
    w, h = target_pil.size
    s = min(w, h)
    target_pil = target_pil.crop(
        ((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2)
    )
    target_pil = target_pil.resize(
        (G.img_resolution, G.img_resolution), PIL.Image.LANCZOS
    )
    target_uint8 = np.array(target_pil, dtype=np.uint8)

    # Optimize projection.
    start_time = perf_counter()
    projected_w_steps = project(
        G,
        target=torch.tensor(
            target_uint8.transpose([2, 0, 1]), device=device
        ),  # pylint: disable=not-callable
        num_steps=num_steps,
        device=device,
        verbose=True,
    )
    print(f"Elapsed: {(perf_counter()-start_time):.1f} s")

    # Render debug output: optional video and projected image and W vector.
    os.makedirs(outdir, exist_ok=True)
    if save_video:
        video = imageio.get_writer(
            f"{outdir}/proj.mp4", mode="I", fps=10, codec="libx264", bitrate="16M"
        )
        print(f'Saving optimization progress video "{outdir}/proj.mp4"')
        for projected_w in projected_w_steps:
            synth_image = G.synthesis(projected_w.unsqueeze(0), noise_mode="const")
            synth_image = (synth_image + 1) * (255 / 2)
            synth_image = (
                synth_image.permute(0, 2, 3, 1)
                .clamp(0, 255)
                .to(torch.uint8)[0]
                .cpu()
                .numpy()
            )
            video.append_data(np.concatenate([target_uint8, synth_image], axis=1))
        video.close()

    # Save final projected frame and W vector.
    target_pil.save(f"{outdir}/target.png")
    projected_w = projected_w_steps[-1]
    synth_image = G.synthesis(projected_w.unsqueeze(0), noise_mode="const")
    synth_image = (synth_image + 1) * (255 / 2)
    synth_image = (
        synth_image.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
    )
    PIL.Image.fromarray(synth_image, "RGB").save(f"{outdir}/proj.png")
    np.savez(f"{outdir}/projected_w.npz", w=projected_w.unsqueeze(0).cpu().numpy())


# ----------------------------------------------------------------------------

if __name__ == "__main__":
    run_projection()  # pylint: disable=no-value-for-parameter

# ----------------------------------------------------------------------------