"""
ArtiGen Sampling — Flow Matching ODE Solver.
Rectified flow allows efficient 1-4 step generation via Euler solver.
"""
import torch
import torch.nn.functional as F
import numpy as np

try:
    from artigen.model import ArtiGen
except ImportError:
    from model import ArtiGen


def euler_solver(model, z_noise, text_embed, num_steps=4, device='cpu', cfg_scale=1.0):
    model.eval()
    z = z_noise.clone()
    dt = -1.0 / num_steps
    with torch.no_grad():
        for i in range(num_steps):
            t = torch.ones(z.shape[0], device=device) * (1.0 + i * dt)
            if cfg_scale > 1.0:
                v_cond, _ = model(z, t, text_embed, return_asdl=False)
                v_uncond, _ = model(z, t, torch.zeros_like(text_embed), return_asdl=False)
                v = v_uncond + cfg_scale * (v_cond - v_uncond)
            else:
                v, _ = model(z, t, text_embed, return_asdl=False)
            z = z + dt * v
    return z


def sample(model, text_embed, latent_shape=(4, 32, 32), num_steps=4, device='cpu', cfg_scale=1.5):
    C, H, W = latent_shape
    B = text_embed.shape[0]
    z_noise = torch.randn(B, C, H, W, device=device)
    z0 = euler_solver(model, z_noise, text_embed, num_steps=num_steps, device=device, cfg_scale=cfg_scale)
    return z0


def decode_with_vae(vae, z, output_type='pil'):
    img = torch.randn(z.shape[0], 3, 256, 256)
    return img