#!/usr/bin/env python

from __future__ import annotations

import functools
import os

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

from model import Model

TITLE = 'MobileStyleGAN'
DESCRIPTION = 'This is an unofficial demo for https://github.com/bes-dev/MobileStyleGAN.pytorch.'
SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/MobileStyleGAN/resolve/main/samples'
ARTICLE = f'''## Generated images
### FFHQ
- size: 1024x1024
- seed: 0-99
- truncation: 1.0
![FFHQ]({SAMPLE_IMAGE_DIR}/ffhq.jpg)
'''

HF_TOKEN = os.getenv('HF_TOKEN')


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, generator: str,
                   model: nn.Module, device: torch.device) -> np.ndarray:
    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))

    z = generate_z(model.mapping_net.style_dim, seed, device)

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


def load_model(device: torch.device) -> nn.Module:
    path = hf_hub_download('hysts/MobileStyleGAN',
                           'models/mobilestylegan_ffhq_v2.pth',
                           use_auth_token=HF_TOKEN)
    ckpt = torch.load(path)
    model = Model()
    model.load_state_dict(ckpt['state_dict'], strict=False)
    model.eval()
    model.to(device)
    with torch.inference_mode():
        z = torch.zeros((1, model.mapping_net.style_dim)).to(device)
        model(z)
    return model


device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = load_model(device)

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

gr.Interface(
    fn=func,
    inputs=[
        gr.Slider(label='Seed',
                  minimum=0,
                  maximum=100000,
                  step=1,
                  value=0,
                  randomize=True),
        gr.Slider(label='Truncation psi',
                  minimum=0,
                  maximum=2,
                  step=0.05,
                  value=1.0),
        gr.Radio(label='Generator',
                 choices=['student', 'teacher'],
                 type='value',
                 value='student'),
    ],
    outputs=gr.Image(label='Output', type='numpy'),
    title=TITLE,
    description=DESCRIPTION,
    article=ARTICLE,
).queue().launch(show_api=False)