# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.

"""Generate lerp videos using pretrained network pickle."""

import os
import re
from typing import List, Optional, Tuple, Union

import click
import dnnlib
import imageio
import numpy as np
import scipy.interpolate
import torch
from tqdm import tqdm
import mrcfile

import legacy

from camera_utils import LookAtPoseSampler
from torch_utils import misc
#----------------------------------------------------------------------------

def layout_grid(img, grid_w=None, grid_h=1, float_to_uint8=True, chw_to_hwc=True, to_numpy=True):
    batch_size, channels, img_h, img_w = img.shape
    if grid_w is None:
        grid_w = batch_size // grid_h
    assert batch_size == grid_w * grid_h
    if float_to_uint8:
        img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
    img = img.reshape(grid_h, grid_w, channels, img_h, img_w)
    img = img.permute(2, 0, 3, 1, 4)
    img = img.reshape(channels, grid_h * img_h, grid_w * img_w)
    if chw_to_hwc:
        img = img.permute(1, 2, 0)
    if to_numpy:
        img = img.cpu().numpy()
    return img

def create_samples(N=256, voxel_origin=[0, 0, 0], cube_length=2.0):
    # NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
    voxel_origin = np.array(voxel_origin) - cube_length/2
    voxel_size = cube_length / (N - 1)

    overall_index = torch.arange(0, N ** 3, 1, out=torch.LongTensor())
    samples = torch.zeros(N ** 3, 3)

    # transform first 3 columns
    # to be the x, y, z index
    samples[:, 2] = overall_index % N
    samples[:, 1] = (overall_index.float() / N) % N
    samples[:, 0] = ((overall_index.float() / N) / N) % N

    # transform first 3 columns
    # to be the x, y, z coordinate
    samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]
    samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
    samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]

    num_samples = N ** 3

    return samples.unsqueeze(0), voxel_origin, voxel_size

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

def gen_interp_video(G, w_given, mp4: str, seeds, shuffle_seed=None, w_frames=60*4, kind='cubic', grid_dims=(1,1), num_keyframes=None, wraps=2, psi=1., truncation_cutoff=14, generator_type='ffhq', image_mode='image', gen_shapes=False, device=torch.device('cuda'), **video_kwargs):
    grid_w = grid_dims[0]
    grid_h = grid_dims[1]

    if num_keyframes is None:
        if len(seeds) % (grid_w*grid_h) != 0:
            raise ValueError('Number of input seeds must be divisible by grid W*H')
        num_keyframes = len(seeds) // (grid_w*grid_h)

    all_seeds = np.zeros(num_keyframes*grid_h*grid_w, dtype=np.int64)
    for idx in range(num_keyframes*grid_h*grid_w):
        all_seeds[idx] = seeds[idx % len(seeds)]

    if shuffle_seed is not None:
        rng = np.random.RandomState(seed=shuffle_seed)
        rng.shuffle(all_seeds)

    camera_lookat_point = torch.tensor(G.rendering_kwargs['avg_camera_pivot'], device=device)
    zs = torch.from_numpy(np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds])).to(device)
    cam2world_pose = LookAtPoseSampler.sample(3.14/2, 3.14/2, camera_lookat_point, radius=G.rendering_kwargs['avg_camera_radius'], device=device)
    focal_length = 4.2647 #if generator_type != 'Shapenet' else 1.7074 # shapenet has higher FOV
    intrinsics = torch.tensor([[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]], device=device)
    c = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
    c = c.repeat(len(zs), 1)

    if w_given is not None:
        ws = w_given
        if ws.shape[1] != G.backbone.mapping.num_ws:
            ws = ws.repeat([1, G.backbone.mapping.num_ws, 1])
    else:
        ws = G.mapping(z=zs, c=c, truncation_psi=psi, truncation_cutoff=truncation_cutoff)
    # _ = G.synthesis(ws[:1], c[:1]) # warm up
    ws = ws.reshape(grid_h, grid_w, num_keyframes, *ws.shape[1:])

    # Interpolation.
    grid = []
    for yi in range(grid_h):
        row = []
        for xi in range(grid_w):
            x = np.arange(-num_keyframes * wraps, num_keyframes * (wraps + 1))
            y = np.tile(ws[yi][xi].cpu().numpy(), [wraps * 2 + 1, 1, 1])
            interp = scipy.interpolate.interp1d(x, y, kind=kind, axis=0)
            row.append(interp)
        grid.append(row)

    # Render video.
    max_batch = 10000000
    voxel_resolution = 512
    video_out = imageio.get_writer(mp4, mode='I', fps=60, codec='libx264', **video_kwargs)

    if gen_shapes:
        outdir = 'interpolation_{}_{}/'.format(all_seeds[0], all_seeds[1])
        os.makedirs(outdir, exist_ok=True)
    all_poses = []
    for frame_idx in tqdm(range(num_keyframes * w_frames)):
        imgs = []
        for yi in range(grid_h):
            for xi in range(grid_w):
                pitch_range = 0.25
                yaw_range = 0.35
                cam2world_pose = LookAtPoseSampler.sample(3.14/2 + yaw_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
                                                        3.14/2 -0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
                                                        camera_lookat_point, radius=G.rendering_kwargs['avg_camera_radius'], device=device)
                all_poses.append(cam2world_pose.squeeze().cpu().numpy())
                focal_length = 4.2647 if generator_type != 'Shapenet' else 1.7074 # shapenet has higher FOV
                intrinsics = torch.tensor([[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]], device=device)
                c = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)

                interp = grid[yi][xi]
                w = torch.from_numpy(interp(frame_idx / w_frames)).to(device)

                entangle = 'camera'
                if entangle == 'conditioning':
                    c_forward = torch.cat([LookAtPoseSampler.sample(3.14/2,
                                                                    3.14/2,
                                                                    camera_lookat_point,
                                                                    radius=G.rendering_kwargs['avg_camera_radius'], device=device).reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
                    w_c = G.mapping(z=zs[0:1], c=c[0:1], truncation_psi=psi, truncation_cutoff=truncation_cutoff)
                    img = G.synthesis(ws=w_c, c=c_forward, noise_mode='const')[image_mode][0]
                elif entangle == 'camera':
                    img = G.synthesis(ws=w.unsqueeze(0), c=c[0:1], noise_mode='const')[image_mode][0]
                    # img = G.synthesis(ws=ws[yi, xi], c=c[0:1], noise_mode='const')[image_mode][0]
                elif entangle == 'both':
                    w_c = G.mapping(z=zs[0:1], c=c[0:1], truncation_psi=psi, truncation_cutoff=truncation_cutoff)
                    img = G.synthesis(ws=w_c, c=c[0:1], noise_mode='const')[image_mode][0]

                if image_mode == 'image_depth':
                    img = -img
                    img = (img - img.min()) / (img.max() - img.min()) * 2 - 1

                imgs.append(img)

                if gen_shapes:
                    # generate shapes
                    print('Generating shape for frame %d / %d ...' % (frame_idx, num_keyframes * w_frames))

                    samples, voxel_origin, voxel_size = create_samples(N=voxel_resolution, voxel_origin=[0, 0, 0], cube_length=G.rendering_kwargs['box_warp'])
                    samples = samples.to(device)
                    sigmas = torch.zeros((samples.shape[0], samples.shape[1], 1), device=device)
                    transformed_ray_directions_expanded = torch.zeros((samples.shape[0], max_batch, 3), device=device)
                    transformed_ray_directions_expanded[..., -1] = -1

                    head = 0
                    with tqdm(total = samples.shape[1]) as pbar:
                        with torch.no_grad():
                            while head < samples.shape[1]:
                                torch.manual_seed(0)
                                sigma = G.sample_mixed(samples[:, head:head+max_batch], transformed_ray_directions_expanded[:, :samples.shape[1]-head], w.unsqueeze(0), truncation_psi=psi, noise_mode='const')['sigma']
                                sigmas[:, head:head+max_batch] = sigma
                                head += max_batch
                                pbar.update(max_batch)

                    sigmas = sigmas.reshape((voxel_resolution, voxel_resolution, voxel_resolution)).cpu().numpy()
                    sigmas = np.flip(sigmas, 0)

                    pad = int(30 * voxel_resolution / 256)
                    pad_top = int(38 * voxel_resolution / 256)
                    sigmas[:pad] = 0
                    sigmas[-pad:] = 0
                    sigmas[:, :pad] = 0
                    sigmas[:, -pad_top:] = 0
                    sigmas[:, :, :pad] = 0
                    sigmas[:, :, -pad:] = 0

                    output_ply = False
                    if output_ply:
                        try:
                            from shape_utils import convert_sdf_samples_to_ply
                            convert_sdf_samples_to_ply(np.transpose(sigmas, (2, 1, 0)), [0, 0, 0], 1, os.path.join(outdir, f'{frame_idx:04d}_shape.ply'), level=10)
                        except:
                            pass
                    else: # output mrc
                        with mrcfile.new_mmap(outdir + f'{frame_idx:04d}_shape.mrc', overwrite=True, shape=sigmas.shape, mrc_mode=2) as mrc:
                            mrc.data[:] = sigmas

        video_out.append_data(layout_grid(torch.stack(imgs), grid_w=grid_w, grid_h=grid_h))
    video_out.close()
    all_poses = np.stack(all_poses)

    if gen_shapes:
        print(all_poses.shape)
        with open(mp4.replace('.mp4', '_trajectory.npy'), 'wb') as f:
            np.save(f, all_poses)

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

def parse_range(s: Union[str, List[int]]) -> List[int]:
    '''Parse a comma separated list of numbers or ranges and return a list of ints.

    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
    '''
    if isinstance(s, list): return s
    ranges = []
    range_re = re.compile(r'^(\d+)-(\d+)$')
    for p in s.split(','):
        if m := range_re.match(p):
            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
        else:
            ranges.append(int(p))
    return ranges

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

def parse_tuple(s: Union[str, Tuple[int,int]]) -> Tuple[int, int]:
    '''Parse a 'M,N' or 'MxN' integer tuple.

    Example:
        '4x2' returns (4,2)
        '0,1' returns (0,1)
    '''
    if isinstance(s, tuple): return s
    if m := re.match(r'^(\d+)[x,](\d+)$', s):
        return (int(m.group(1)), int(m.group(2)))
    raise ValueError(f'cannot parse tuple {s}')

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

@click.command()
@click.option('--network', help='Network path',multiple=True, required=True)
@click.option('--w_pth', help='latent path')
@click.option('--generator_type', help='Generator type', type=click.Choice(['ffhq', 'cat']), required=False, metavar='STR', default='ffhq', show_default=True)
@click.option('--model_is_state_dict', type=bool, default=False)
@click.option('--seeds', type=parse_range, help='List of random seeds', required=True)
@click.option('--shuffle-seed', type=int, help='Random seed to use for shuffling seed order', default=None)
@click.option('--grid', type=parse_tuple, help='Grid width/height, e.g. \'4x3\' (default: 1x1)', default=(1,1))
@click.option('--num-keyframes', type=int, help='Number of seeds to interpolate through.  If not specified, determine based on the length of the seeds array given by --seeds.', default=None)
@click.option('--w-frames', type=int, help='Number of frames to interpolate between latents', default=120)
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
@click.option('--trunc-cutoff', 'truncation_cutoff', type=int, help='Truncation cutoff', default=14, show_default=True)
@click.option('--outdir', help='Output directory', type=str, default='../test_runs/manip_3D_recon/4_manip_result', metavar='DIR')
@click.option('--image_mode', help='Image mode', type=click.Choice(['image', 'image_depth', 'image_raw']), required=False, metavar='STR', default='image', show_default=True)
@click.option('--sample_mult', 'sampling_multiplier', type=float, help='Multiplier for depth sampling in volume rendering', default=2, show_default=True)
@click.option('--nrr', type=int, help='Neural rendering resolution override', default=None, show_default=True)
@click.option('--shapes', type=bool, help='Gen shapes for shape interpolation', default=False, show_default=True)

def generate_images(
    network: List[str],
    w_pth: str,
    seeds: List[int],
    shuffle_seed: Optional[int],
    truncation_psi: float,
    truncation_cutoff: int,
    grid: Tuple[int,int],
    num_keyframes: Optional[int],
    w_frames: int,
    outdir: str,
    generator_type: str,
    image_mode: str,
    sampling_multiplier: float,
    nrr: Optional[int],
    shapes: bool,
    model_is_state_dict: bool,
):

    if not os.path.exists(outdir):
        os.makedirs(outdir, exist_ok=True)

    device = torch.device('cuda')

    if generator_type == 'ffhq':
        network_pkl_tmp = 'pretrained/ffhqrebalanced512-128.pkl'
    elif generator_type == 'cat':
        network_pkl_tmp = 'pretrained/afhqcats512-128.pkl'
    else:
        NotImplementedError()

    G_list = []
    outputs = []
    for network_path in network:
        print('Loading networks from "%s"...' % network_path)
        dir_label = network_path.split('/')[-2] + '___' + network_path.split('/')[-1]
        output = os.path.join(outdir, dir_label)
        outputs.append(output)
        if model_is_state_dict:
            with dnnlib.util.open_url(network_pkl_tmp) as f:
                G = legacy.load_network_pkl(f)['G_ema'].to(device)  # type: ignore
            ckpt = torch.load(network_path)
            G.load_state_dict(ckpt, strict=False)
        else:
            with dnnlib.util.open_url(network_path) as f:
                G = legacy.load_network_pkl(f)['G_ema'].to(device)  # type: ignore

        G.rendering_kwargs['depth_resolution'] = int(G.rendering_kwargs['depth_resolution'] * sampling_multiplier)
        G.rendering_kwargs['depth_resolution_importance'] = int(G.rendering_kwargs['depth_resolution_importance'] * sampling_multiplier)

        if generator_type == 'cat':
            G.rendering_kwargs['avg_camera_pivot'] = [0, 0, -0.06]
        elif generator_type == 'ffhq':
            G.rendering_kwargs['avg_camera_pivot'] = [0, 0, 0.2]

        if nrr is not None: G.neural_rendering_resolution = nrr
        G_list.append(G)


    if truncation_cutoff == 0:
        truncation_psi = 1.0 # truncation cutoff of 0 means no truncation anyways
    if truncation_psi == 1.0:
        truncation_cutoff = 14 # no truncation so doesn't matter where we cutoff

    grid_w, grid_h = grid
    seeds = seeds[:grid_w * grid_h]

    seed_idx = ''

    for i, seed in enumerate(seeds):
        if i < len(seeds) - 1:
            seed_idx += f'{seed}_'
        else:
            seed_idx += f'{seed}'


    for G, output in zip(G_list, outputs):
        if w_pth is not None:
            grid = (1, 1)
            w_given =  torch.load(w_pth).cuda()
            w_given_id = os.path.split(w_pth)[-1].split('.')[-2]
            output = output + f'__{w_given_id}.mp4'
            gen_interp_video(G=G, w_given=w_given, mp4=output, bitrate='10M', grid_dims=grid, num_keyframes=num_keyframes,
                             w_frames=w_frames,
                             seeds=seeds, shuffle_seed=shuffle_seed, psi=truncation_psi,
                             truncation_cutoff=truncation_cutoff, generator_type=generator_type, image_mode=image_mode,
                             gen_shapes=shapes)

        else:
            output = output + f'__{seed_idx}.mp4'
            gen_interp_video(G=G, w_given=None, mp4=output, bitrate='10M', grid_dims=grid, num_keyframes=num_keyframes,
                             w_frames=w_frames,
                             seeds=seeds, shuffle_seed=shuffle_seed, psi=truncation_psi,
                             truncation_cutoff=truncation_cutoff, generator_type=generator_type, image_mode=image_mode,
                             gen_shapes=shapes)


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

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

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