from __future__ import annotations

import json
import random
from pathlib import Path

import numpy as np
import torch
import trackio
from model import TuringSurrogate, parameter_count
from physics import gray_scott_step, initial_state
from PIL import Image
from safetensors.torch import save_file
from torch.nn import functional as F
from torch.utils.data import DataLoader, TensorDataset

PROJECT_DIR = Path(__file__).resolve().parent
DATA_DIR = PROJECT_DIR / "data"
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "turing-neural-field"


def seed_everything(seed: int) -> None:
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


def load_data() -> dict[str, torch.Tensor]:
    arrays = np.load(DATA_DIR / "gray_scott_trajectories.npz")
    return {
        key: torch.from_numpy(arrays[key])
        for key in ["states", "next_states", "feeds", "kills", "trajectory_ids"]
    }


@torch.inference_mode()
def one_step_metrics(
    model: TuringSurrogate,
    state: torch.Tensor,
    target: torch.Tensor,
    feed: torch.Tensor,
    kill: torch.Tensor,
) -> dict:
    model.eval()
    predictions = []
    for start in range(0, len(state), 64):
        predictions.append(
            model(
                state[start : start + 64],
                feed[start : start + 64],
                kill[start : start + 64],
            )
        )
    prediction = torch.cat(predictions)
    model_mse = F.mse_loss(prediction, target).item()
    persistence_mse = F.mse_loss(state, target).item()
    return {
        "model_mse": model_mse,
        "persistence_mse": persistence_mse,
        "improvement_percent": 100 * (persistence_mse - model_mse) / persistence_mse,
    }


@torch.inference_mode()
def rollout(
    model: TuringSurrogate,
    steps: int = 120,
) -> tuple[dict, list[torch.Tensor], list[torch.Tensor]]:
    feed = torch.tensor([0.042])
    kill = torch.tensor([0.061])
    physics_state = initial_state(1, 32, seed=4096)
    neural_state = physics_state.clone()
    physics_frames = [physics_state.clone()]
    neural_frames = [neural_state.clone()]
    model.eval()
    for step in range(1, steps + 1):
        physics_state = gray_scott_step(physics_state, feed, kill)
        neural_state = model(neural_state, feed, kill)
        if step % 5 == 0:
            physics_frames.append(physics_state.clone())
            neural_frames.append(neural_state.clone())
    field_mse = F.mse_loss(neural_state, physics_state).item()
    physics_v = physics_state[:, 1]
    neural_v = neural_state[:, 1]
    centered_physics = physics_v - physics_v.mean()
    centered_neural = neural_v - neural_v.mean()
    correlation = float(
        (centered_physics * centered_neural).sum()
        / (
            torch.sqrt((centered_physics.square()).sum())
            * torch.sqrt((centered_neural.square()).sum())
            + 1e-9
        )
    )
    metrics = {
        "steps": steps,
        "final_field_mse": field_mse,
        "v_field_correlation": correlation,
        "physics_v_mean": float(physics_v.mean()),
        "neural_v_mean": float(neural_v.mean()),
        "physics_v_spatial_std": float(physics_v.std()),
        "neural_v_spatial_std": float(neural_v.std()),
    }
    return metrics, physics_frames, neural_frames


def field_image(state: torch.Tensor) -> np.ndarray:
    v = state[0, 1].cpu().numpy()
    normalized = np.clip(v / max(0.05, float(v.max())), 0, 1)
    red = np.clip(normalized * 70, 0, 255)
    green = np.clip(normalized * 210, 0, 255)
    blue = np.clip(30 + normalized * 225, 0, 255)
    return np.stack([red, green, blue], axis=2).astype(np.uint8)


def save_comparison_gif(
    physics_frames: list[torch.Tensor],
    neural_frames: list[torch.Tensor],
    path: Path,
) -> None:
    images = []
    for physics_state, neural_state in zip(
        physics_frames,
        neural_frames,
        strict=True,
    ):
        physics_image = field_image(physics_state)
        neural_image = field_image(neural_state)
        separator = np.full((32, 2, 3), 245, dtype=np.uint8)
        combined = np.concatenate([physics_image, separator, neural_image], axis=1)
        images.append(
            Image.fromarray(combined).resize((1056, 512), Image.Resampling.NEAREST)
        )
    images[0].save(
        path,
        save_all=True,
        append_images=images[1:],
        duration=120,
        loop=0,
    )
    images[-1].save(path.with_name("final_comparison.png"))


def main() -> None:
    seed_everything(2040)
    data = load_data()
    train_mask = data["trajectory_ids"] < 28
    validation_mask = (data["trajectory_ids"] >= 28) & (data["trajectory_ids"] < 32)
    test_mask = data["trajectory_ids"] >= 32
    train_dataset = TensorDataset(
        data["states"][train_mask],
        data["next_states"][train_mask],
        data["feeds"][train_mask],
        data["kills"][train_mask],
    )
    loader = DataLoader(
        train_dataset,
        batch_size=32,
        shuffle=True,
        generator=torch.Generator().manual_seed(2040),
    )
    model = TuringSurrogate()
    optimizer = torch.optim.AdamW(model.parameters(), lr=0.0015, weight_decay=0.001)
    epochs = 55
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
    best_validation_mse = float("inf")
    best_epoch = 0
    best_state = None
    trackio.init(
        project="turing-neural-field",
        name="gray-scott-surrogate-v1",
        config={
            "parameters": parameter_count(model),
            "train_trajectories": 28,
            "validation_trajectories": 4,
            "test_trajectories": 4,
            "epochs": epochs,
        },
    )
    for epoch in range(1, epochs + 1):
        model.train()
        losses = []
        for state, target, feed, kill in loader:
            prediction = model(state, feed, kill)
            loss = F.mse_loss(prediction, target) * 10_000
            optimizer.zero_grad(set_to_none=True)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step()
            losses.append(loss.item())
        scheduler.step()
        validation = one_step_metrics(
            model,
            data["states"][validation_mask],
            data["next_states"][validation_mask],
            data["feeds"][validation_mask],
            data["kills"][validation_mask],
        )
        if validation["model_mse"] < best_validation_mse:
            best_validation_mse = validation["model_mse"]
            best_epoch = epoch
            best_state = {
                key: value.detach().cpu().clone()
                for key, value in model.state_dict().items()
            }
        trackio.log(
            {
                "epoch": epoch,
                "train_scaled_mse": float(np.mean(losses)),
                "validation_one_step_mse": validation["model_mse"],
                "learning_rate": scheduler.get_last_lr()[0],
            }
        )
    assert best_state is not None
    model.load_state_dict(best_state)
    test = one_step_metrics(
        model,
        data["states"][test_mask],
        data["next_states"][test_mask],
        data["feeds"][test_mask],
        data["kills"][test_mask],
    )
    rollout_metrics, physics_frames, neural_frames = rollout(model)
    results = {
        "model": "Turing Neural Field",
        "parameters": parameter_count(model),
        "best_epoch": best_epoch,
        "test_states": int(test_mask.sum()),
        "one_step_test": test,
        "held_out_rollout": rollout_metrics,
        "gif_layout": "left physics simulator, right neural surrogate",
    }
    trackio.log(
        {
            "test_one_step_mse": test["model_mse"],
            "rollout_final_field_mse": rollout_metrics["final_field_mse"],
            "rollout_v_correlation": rollout_metrics["v_field_correlation"],
        }
    )
    trackio.finish()
    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
    save_file(model.state_dict(), ARTIFACT_DIR / "model.safetensors")
    save_comparison_gif(
        physics_frames,
        neural_frames,
        ARTIFACT_DIR / "physics_vs_neural.gif",
    )
    (ARTIFACT_DIR / "evaluation.json").write_text(
        json.dumps(results, indent=2),
        encoding="utf-8",
    )
    print(json.dumps(results, indent=2))


if __name__ == "__main__":
    main()