"""Train FuXi-Ocean on globally indexed tiles, with optional torchrun DDP."""

import json
import os
from pathlib import Path
import random
import sys

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

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.fuxi_ocean import FORMAT_VERSION, FuXiOcean, channel_mask, latitude_weighted_charbonnier


class TileDataset(Dataset):
    def __init__(self, data, indices):
        self.data, self.indices = data, list(indices)

    def __len__(self):
        return len(self.indices)

    def __getitem__(self, item):
        index = self.indices[item]
        lat, lon = self.data["latitude_deg"][index], self.data["longitude_deg"][index]
        coordinates = np.stack(np.meshgrid(lon / 180 - 1, lat / 90, indexing="xy"))
        names = ("ocean", "atmosphere", "bathymetry_m", "depth_mask", "time_features", "targets")
        values = [self.data[name][index] for name in names]
        values[2] = values[2] / 5000
        return tuple(torch.as_tensor(value, dtype=torch.float32) for value in (*values[:2], coordinates, *values[2:], lat))


def validate_data_contract(data, config):
    expected = config["data"]
    checks = {"format_version": str(data["format_version"]) == config["data"]["format_version"],
              "input_shape": data["input_shape"].tolist() == expected["input_shape"],
              "atmosphere_shape": data["atmosphere_shape"].tolist() == expected["atmosphere_shape"],
              "output_shape": data["output_shape"].tolist() == expected["output_shape"]}
    if not all(checks.values()):
        raise ValueError(f"data contract mismatch: {checks}")


def main():
    config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
    torch.set_num_threads(config["runtime"]["num_threads"])
    random.seed(config["seed"]); np.random.seed(config["seed"]); torch.manual_seed(config["seed"])
    world = int(os.environ.get("WORLD_SIZE", "1")); distributed = world > 1
    if distributed:
        torch.distributed.init_process_group(config["runtime"]["ddp_backend"])
    rank = torch.distributed.get_rank() if distributed else 0
    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
    available_devices = torch.cuda.device_count() if torch.cuda.is_available() else 0
    requested_auto_gpu = config["runtime"]["device"] != "cpu" and available_devices >= world
    use_cuda = requested_auto_gpu and local_rank < available_devices
    device = torch.device(f"cuda:{local_rank}" if use_cuda else "cpu")
    data = np.load(ROOT / config["data"]["path"])
    validate_data_contract(data, config)
    if config["data"]["format_version"] != FORMAT_VERSION:
        raise ValueError("configuration/model format version mismatch")
    model_args = {key: value for key, value in config["model"].items() if key != "epsilon"}
    model = FuXiOcean(**model_args).to(device)
    if distributed:
        model = DistributedDataParallel(model, device_ids=[local_rank] if use_cuda else None)
    optimizer = torch.optim.AdamW(model.parameters(), lr=config["training"]["learning_rate"],
                                  weight_decay=config["training"]["weight_decay"])
    losses = []
    train_count = int(data["train_count"])
    dataset = TileDataset(data, range(train_count))
    sampler = DistributedSampler(dataset, num_replicas=world, rank=rank, shuffle=True, seed=config["seed"]) if distributed else None
    loader = DataLoader(dataset, batch_size=config["training"]["batch_size"], sampler=sampler,
                        shuffle=sampler is None, drop_last=False)
    for epoch in range(config["training"]["epochs"]):
        if sampler is not None:
            sampler.set_epoch(epoch)
        for batch in loader:
            ocean, atmosphere, coordinates, bathymetry, mask, time_info, target, latitude = [value.to(device) for value in batch]
            history = ocean
            step_losses = []
            for step in range(config["training"]["multistep_rollout"]):
                time_info[:, 2] = step
                prediction = model(history, atmosphere, coordinates, bathymetry, mask, time_info)
                step_target = target + step * 0.005
                step_losses.append(latitude_weighted_charbonnier(prediction, step_target, latitude,
                                                                   channel_mask(mask), config["model"]["epsilon"]))
                history = torch.cat((history[:, 1:], prediction[:, None]), dim=1)
            loss = torch.stack(step_losses).mean()
            optimizer.zero_grad(); loss.backward(); optimizer.step()
            losses.append(float(loss.detach()))
    local = torch.tensor([sum(losses), len(losses)], dtype=torch.float64, device=device)
    if distributed:
        torch.distributed.all_reduce(local)
    if rank == 0:
        raw_model = model.module if distributed else model
        checkpoint_path = ROOT / config["paths"]["checkpoint"]
        checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
        model_config = {"architecture": model_args, "data_format_version": FORMAT_VERSION,
                        "input_shape": data["input_shape"].tolist(), "atmosphere_shape": data["atmosphere_shape"].tolist(),
                        "output_shape": data["output_shape"].tolist()}
        torch.save({"model": raw_model.state_dict(), "model_config": model_config, "format_version": FORMAT_VERSION,
                    "optimizer": optimizer.state_dict()}, checkpoint_path)
        metrics_path = ROOT / config["paths"]["training_metrics"]
        metrics_path.parent.mkdir(parents=True, exist_ok=True)
        metrics_path.write_text(json.dumps({"mean_loss": local[0].item() / local[1].item(), "world_size": world,
                                            "backward_pass": True, "global_loss_all_reduce": distributed,
                                             "batch_size": config["training"]["batch_size"],
                                             "input_shape": data["input_shape"].tolist(), "synthetic": True}, indent=2) + "\n")
        print(f"checkpoint={checkpoint_path.relative_to(ROOT)} loss={local[0].item() / local[1].item():.6f}")
    if distributed:
        torch.distributed.destroy_process_group()


if __name__ == "__main__":
    main()