#!/usr/bin/env python

from __future__ import annotations

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, "StyleGAN-Human")

TITLE = "StyleGAN-Human (Interpolation)"
DESCRIPTION = "https://github.com/stylegan-human/StyleGAN-Human"


def load_model(file_name: str, device: torch.device) -> nn.Module:
    path = hf_hub_download("public-data/StyleGAN-Human", f"models/{file_name}")
    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, force_fp32=True)
    return model


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = load_model("stylegan_human_v2_1024.pkl", device)


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


@torch.inference_mode()
def generate_interpolated_images(
    seed0: int, psi0: float, seed1: int, psi1: float, num_intermediate: int
) -> list[np.ndarray]:
    seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
    seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))

    z0 = generate_z(model.z_dim, seed0)
    z1 = generate_z(model.z_dim, seed1)
    z0 = z0.to(device)
    z1 = z1.to(device)
    vec = z1 - z0
    dvec = vec / (num_intermediate + 1)
    zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
    dpsi = (psi1 - psi0) / (num_intermediate + 1)
    psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]

    label = torch.zeros([1, model.c_dim], device=device)

    res = []
    for z, psi in zip(zs, psis):
        out = model(z, label, truncation_psi=psi, force_fp32=True)
        out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
        out = out[0].cpu().numpy()
        res.append(out)
    return res


demo = gr.Interface(
    fn=generate_interpolated_images,
    inputs=[
        gr.Slider(label="Seed 1", minimum=0, maximum=100000, step=1, value=0, randomize=True),
        gr.Slider(label="Truncation psi 1", minimum=0, maximum=2, step=0.05, value=0.7),
        gr.Slider(label="Seed 2", minimum=0, maximum=100000, step=1, value=1, randomize=True),
        gr.Slider(label="Truncation psi 2", minimum=0, maximum=2, step=0.05, value=0.7),
        gr.Slider(label="Number of Intermediate Frames", minimum=0, maximum=21, step=1, value=7),
    ],
    outputs=gr.Gallery(label="Output Images"),
    title=TITLE,
    description=DESCRIPTION,
)

if __name__ == "__main__":
    demo.queue(max_size=10).launch()