#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import os
import pickle
import sys

import gradio as gr
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

sys.path.insert(0, 'stylegan3')

ORIGINAL_REPO_URL = 'https://github.com/NVlabs/stylegan3'
TITLE = 'NVlabs/stylegan3'
DESCRIPTION = f'This is a demo for {ORIGINAL_REPO_URL}.'
SAMPLE_IMAGE_DIR = 'https://huggingface.co./spaces/hysts/StyleGAN3/resolve/main/samples'
ARTICLE = f'''## Generated images
- truncation: 0.7
### AFHQv2
- size: 512x512
- seed: 0-99
![AFHQv2 samples]({SAMPLE_IMAGE_DIR}/afhqv2.jpg)
### FFHQ
- size: 1024x1024
- seed: 0-99
![FFHQ samples]({SAMPLE_IMAGE_DIR}/ffhq.jpg)
### FFHQ-U
- size: 1024x1024
- seed: 0-99
![FFHQ-U samples]({SAMPLE_IMAGE_DIR}/ffhq-u.jpg)
### MetFaces
- size: 1024x1024
- seed: 0-99
![MetFaces samples]({SAMPLE_IMAGE_DIR}/metfaces.jpg)
### MetFaces-U
- size: 1024x1024
- seed: 0-99
![MetFaces-U samples]({SAMPLE_IMAGE_DIR}/metfaces-u.jpg)
'''

TOKEN = os.environ['TOKEN']


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--live', action='store_true')
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    parser.add_argument('--allow-flagging', type=str, default='never')
    parser.add_argument('--allow-screenshot', action='store_true')
    return parser.parse_args()


def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray:
    mat = np.eye(3)
    sin = np.sin(angle / 360 * np.pi * 2)
    cos = np.cos(angle / 360 * np.pi * 2)
    mat[0][0] = cos
    mat[0][1] = sin
    mat[0][2] = translate[0]
    mat[1][0] = -sin
    mat[1][1] = cos
    mat[1][2] = translate[1]
    return mat


def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor:
    return torch.from_numpy(np.random.RandomState(seed).randn(
        1, z_dim)).to(device).float()


@torch.inference_mode()
def generate_image(model_name: str, seed: int, truncation_psi: float,
                   tx: float, ty: float, angle: float,
                   model_dict: dict[str, nn.Module],
                   device: torch.device) -> np.ndarray:
    model = model_dict[model_name]
    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))

    z = generate_z(model.z_dim, seed, device)
    label = torch.zeros([1, model.c_dim], device=device)

    mat = make_transform((tx, ty), angle)
    mat = np.linalg.inv(mat)
    model.synthesis.input.transform.copy_(torch.from_numpy(mat))

    out = model(z, label, truncation_psi=truncation_psi)
    out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
    return out[0].cpu().numpy()


def load_model(file_name: str, device: torch.device) -> nn.Module:
    path = hf_hub_download('hysts/StyleGAN3',
                           f'models/{file_name}',
                           use_auth_token=TOKEN)
    with open(path, 'rb') as f:
        model = pickle.load(f)['G_ema']
    model.eval()
    model.to(device)
    with torch.inference_mode():
        z = torch.zeros((1, model.z_dim)).to(device)
        label = torch.zeros([1, model.c_dim], device=device)
        model(z, label)
    return model


def main():
    args = parse_args()
    device = torch.device(args.device)

    model_names = {
        'AFHQv2-512-R': 'stylegan3-r-afhqv2-512x512.pkl',
        'FFHQ-1024-R': 'stylegan3-r-ffhq-1024x1024.pkl',
        'FFHQ-U-256-R': 'stylegan3-r-ffhqu-256x256.pkl',
        'FFHQ-U-1024-R': 'stylegan3-r-ffhqu-1024x1024.pkl',
        'MetFaces-1024-R': 'stylegan3-r-metfaces-1024x1024.pkl',
        'MetFaces-U-1024-R': 'stylegan3-r-metfacesu-1024x1024.pkl',
        'AFHQv2-512-T': 'stylegan3-t-afhqv2-512x512.pkl',
        'FFHQ-1024-T': 'stylegan3-t-ffhq-1024x1024.pkl',
        'FFHQ-U-256-T': 'stylegan3-t-ffhqu-256x256.pkl',
        'FFHQ-U-1024-T': 'stylegan3-t-ffhqu-1024x1024.pkl',
        'MetFaces-1024-T': 'stylegan3-t-metfaces-1024x1024.pkl',
        'MetFaces-U-1024-T': 'stylegan3-t-metfacesu-1024x1024.pkl',
    }

    model_dict = {
        name: load_model(file_name, device)
        for name, file_name in model_names.items()
    }

    func = functools.partial(generate_image,
                             model_dict=model_dict,
                             device=device)
    func = functools.update_wrapper(func, generate_image)

    gr.Interface(
        func,
        [
            gr.inputs.Radio(list(model_names.keys()),
                            type='value',
                            default='FFHQ-1024-R',
                            label='Model'),
            gr.inputs.Number(default=0, label='Seed'),
            gr.inputs.Slider(
                0, 2, step=0.05, default=0.7, label='Truncation psi'),
            gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate X'),
            gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate Y'),
            gr.inputs.Slider(-180, 180, step=5, default=0, label='Angle'),
        ],
        gr.outputs.Image(type='numpy', label='Output'),
        title=TITLE,
        description=DESCRIPTION,
        article=ARTICLE,
        theme=args.theme,
        allow_screenshot=args.allow_screenshot,
        allow_flagging=args.allow_flagging,
        live=args.live,
    ).launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == '__main__':
    main()