#!/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_utils
import torch.nn as nn
from huggingface_hub import hf_hub_download

sys.path.insert(0, 'StyleGAN-Human')

TITLE = 'StyleGAN-Human'
DESCRIPTION = '''This is an unofficial demo for https://github.com/stylegan-human/StyleGAN-Human.
Expected execution time on Hugging Face Spaces: 0.8s
Related App: [StyleGAN-Human (Interpolation)](https://huggingface.co/spaces/hysts/StyleGAN-Human-Interpolation)
'''
ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.stylegan-human" alt="visitor badge"/></center>'

TOKEN = "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"


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')
    return parser.parse_args()


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(seed: int, truncation_psi: float, model: nn.Module,
                   device: torch.device) -> np.ndarray:
    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)

    out = model(z, label, truncation_psi=truncation_psi, force_fp32=True)
    out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
    return out[0].cpu().numpy()


def load_model(path:str, file_name: str, device: torch.device) -> nn.Module:
    path = hf_hub_download(f'{path}',
                           f'{file_name}',
                           use_auth_token=TOKEN)
    with open(path, 'rb') as f:
        model = torch.load(f)
    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, force_fp32=True)
    return model


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

    model = load_model('feng2022/Time-TravelRephotography_e4e_ffhq_encode','e4e_ffhq_encode.pt', device)

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

    gr.Interface(
        func,
        [
            gr.inputs.Number(default=0, label='Seed'),
            gr.inputs.Slider(
                0, 2, step=0.05, default=0.7, label='Truncation psi'),
        ],
        gr.outputs.Image(type='numpy', label='Output'),
        title=TITLE,
        description=DESCRIPTION,
        article=ARTICLE,
        theme=args.theme,
        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()