"""Train the reduced Clay MAE on deterministic multi-sensor synthetic chips."""

import json
import os
import sys
from pathlib import Path

import numpy as np
import torch
import yaml
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler


ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.clayfoundation import ClayFoundation, compute_loss


class ClayDataset(Dataset):
    def __init__(self, path, config):
        self.data = np.load(path)
        self.sensors = config["data"]["sensors"]
        if str(self.data["format_version"]) != config["data"]["format_version"]:
            raise ValueError("incompatible synthetic data format")
        size = int(config["data"]["image_size"])
        for name, spec in self.sensors.items():
            expected = (int(spec["channels"]), size, size)
            if self.data[f"pixels_{name}"].shape[1:] != expected:
                raise ValueError(f"{name} shape does not match {expected}")

    def __len__(self):
        return len(self.data["time"])

    def __getitem__(self, index):
        item = {
            "time": torch.from_numpy(self.data["time"][index]),
            "latlon": torch.from_numpy(self.data["latlon"][index]),
            "teacher_target": torch.from_numpy(self.data["teacher_target"][index]),
        }
        for name in self.sensors:
            item[f"pixels_{name}"] = torch.from_numpy(self.data[f"pixels_{name}"][index])
            item[f"valid_{name}"] = torch.from_numpy(self.data[f"valid_{name}"][index])
            item[f"waves_{name}"] = torch.from_numpy(self.data[f"wavelengths_{name}"][index])
        return item


def device_from_config(config, local_rank=0):
    requested = config["runtime"]["device"]
    if requested == "auto":
        return torch.device("cuda", local_rank) if torch.cuda.is_available() else torch.device("cpu")
    return torch.device(requested)


def main():
    config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
    torch.manual_seed(int(config["seed"]))
    distributed = int(os.environ.get("WORLD_SIZE", "1")) > 1
    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
    if distributed:
        torch.distributed.init_process_group("nccl" if torch.cuda.is_available() else "gloo")
    rank = torch.distributed.get_rank() if distributed else 0
    device = device_from_config(config, local_rank)
    if device.type == "cuda":
        torch.cuda.set_device(device)
    dataset = ClayDataset(ROOT / config["data"]["root"] / "train.npz", config)
    sampler = DistributedSampler(dataset, shuffle=True) if distributed else None
    loader = DataLoader(dataset, batch_size=int(config["train"]["batch_size"]), sampler=sampler,
                        shuffle=sampler is None, num_workers=int(config["train"]["num_workers"]))
    model = ClayFoundation(config["model"]).to(device)
    if distributed:
        model = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None)
    optimizer = torch.optim.AdamW(model.parameters(), lr=float(config["train"]["learning_rate"]),
                                  weight_decay=float(config["train"]["weight_decay"]), betas=(0.9, 0.95))
    history = []
    for epoch in range(int(config["train"]["epochs"])):
        if sampler:
            sampler.set_epoch(epoch)
        model.train()
        totals = {"reconstruction": 0.0, "representation": 0.0, "total": 0.0}
        steps = 0
        for batch in loader:
            optimizer.zero_grad(set_to_none=True)
            sensor_losses = []
            components = []
            for name, spec in config["data"]["sensors"].items():
                pixels = batch[f"pixels_{name}"].to(device) * batch[f"valid_{name}"].to(device)
                outputs = model(pixels, batch["time"].to(device), batch["latlon"].to(device),
                                float(spec["gsd"]), batch[f"waves_{name}"].to(device),
                                batch["teacher_target"].to(device))
                loss, values = compute_loss(outputs, float(config["train"]["reconstruction_weight"]),
                                            float(config["train"]["representation_weight"]))
                sensor_losses.append(loss)
                components.append(values)
            loss = torch.stack(sensor_losses).mean()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step()
            for key in totals:
                totals[key] += sum(float(item[key].detach()) for item in components) / len(components)
            steps += 1
        metrics = {key: value / max(steps, 1) for key, value in totals.items()}
        history.append({"epoch": epoch + 1, **metrics})
        if rank == 0:
            print(f"epoch={epoch + 1} total_loss={metrics['total']:.6f} reconstruction={metrics['reconstruction']:.6f}")
    if rank == 0:
        checkpoint = ROOT / config["paths"]["checkpoint"]
        metrics_path = ROOT / config["paths"]["training_metrics"]
        checkpoint.parent.mkdir(parents=True, exist_ok=True)
        metrics_path.parent.mkdir(parents=True, exist_ok=True)
        state = model.module.state_dict() if distributed else model.state_dict()
        torch.save({"model": state, "model_config": config["model"], "sensors": config["data"]["sensors"],
                    "format_version": config["data"]["format_version"]}, checkpoint)
        metrics_path.write_text(json.dumps({"history": history}, indent=2) + "\n")
        print(f"checkpoint={checkpoint.relative_to(ROOT)}")
    if distributed:
        torch.distributed.destroy_process_group()


if __name__ == "__main__":
    main()