"""Train the ConvLSTM classifier with optional distributed data parallelism."""

import json
import os
import random
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.firecubenet import FireCubeNet


class WildfireDataset(Dataset):
    def __init__(self, path, config):
        self.data = np.load(path)
        expected = config["data"]
        if str(self.data["format_version"]) != expected["format_version"]:
            raise ValueError("incompatible wildfire data format")
        expected_shape = (int(expected["sequence_days"]), int(expected["channels"]),
                          int(expected["patch_height"]), int(expected["patch_width"]))
        if self.data["inputs"].ndim != 5 or self.data["inputs"].shape[1:] != expected_shape:
            raise ValueError(f"inputs must have shape [B,{','.join(map(str, expected_shape))}]")
        count = len(self.data["inputs"])
        if self.data["labels"].shape != (count, 1):
            raise ValueError("labels must have shape [B,1]")
        if self.data["coords"].shape != (count, 2) or self.data["timestamps_unix_s"].shape != (count,):
            raise ValueError("coords/timestamps shape mismatch")
        if not np.isfinite(self.data["inputs"]).all() or not np.isfinite(self.data["labels"]).all():
            raise ValueError("inputs and labels must be finite")
        if not np.isin(self.data["labels"], (0, 1)).all():
            raise ValueError("labels must be binary")
        cover_sum = self.data["inputs"][:, :, 15:25].sum(axis=2)
        if not np.allclose(cover_sum, 1.0, atol=1e-5):
            raise ValueError("land-cover fractions must sum to one")

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

    def __getitem__(self, index):
        return (torch.from_numpy(self.data["inputs"][index]).float(),
                torch.from_numpy(self.data["labels"][index]).float())


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())
    seed = int(config["seed"])
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(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 = WildfireDataset(ROOT / config["data"]["root"] / "train.npz", config)
    sampler = DistributedSampler(dataset, shuffle=True, seed=seed) if distributed else None
    loader = DataLoader(dataset, batch_size=int(config["train"]["batch_size"]),
                        shuffle=sampler is None, sampler=sampler,
                        num_workers=int(config["train"]["num_workers"]))
    channel_mean = dataset.data["inputs"].mean(axis=(0, 1, 3, 4)).astype(np.float32)
    channel_std = dataset.data["inputs"].std(axis=(0, 1, 3, 4)).clip(1e-6).astype(np.float32)
    model = FireCubeNet(**config["model"]).to(device)
    wrapped = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None) if distributed else model
    optimizer = torch.optim.Adam(wrapped.parameters(), lr=float(config["train"]["learning_rate"]),
                                 weight_decay=float(config["train"]["weight_decay"]))
    criterion = torch.nn.BCEWithLogitsLoss()
    mean = torch.from_numpy(channel_mean).to(device).view(1, 1, -1, 1, 1)
    std = torch.from_numpy(channel_std).to(device).view(1, 1, -1, 1, 1)
    history = []
    for epoch in range(int(config["train"]["epochs"])):
        if sampler is not None:
            sampler.set_epoch(epoch)
        total, samples = 0.0, 0
        wrapped.train()
        for inputs, labels in loader:
            inputs, labels = inputs.to(device), labels.to(device)
            logits = wrapped((inputs - mean) / std)
            loss = criterion(logits, labels)
            optimizer.zero_grad(set_to_none=True)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(wrapped.parameters(), float(config["train"]["gradient_clip_norm"]))
            optimizer.step()
            total += float(loss.detach()) * len(inputs)
            samples += len(inputs)
        loss_sum = torch.tensor([total, samples], dtype=torch.float64, device=device)
        if distributed:
            torch.distributed.all_reduce(loss_sum)
        if rank == 0:
            history.append({"epoch": epoch + 1, "bce_with_logits": float(loss_sum[0] / loss_sum[1])})
    if rank == 0:
        checkpoint_path = ROOT / config["paths"]["checkpoint"]
        metrics_path = ROOT / config["paths"]["training_metrics"]
        checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
        metrics_path.parent.mkdir(parents=True, exist_ok=True)
        bare_model = wrapped.module if distributed else wrapped
        torch.save({
            "model_state_dict": bare_model.state_dict(),
            "optimizer_state_dict": optimizer.state_dict(),
            "model_config": config["model"], "epoch": int(config["train"]["epochs"]),
            "channel_mean": channel_mean, "channel_std": channel_std,
            "format_version": config["data"]["format_version"], "seed": seed,
        }, checkpoint_path)
        metrics_path.write_text(json.dumps({"history": history}, indent=2) + "\n")
        print(f"checkpoint={checkpoint_path.relative_to(ROOT)} final_loss={history[-1]['bce_with_logits']:.6f}")
    if distributed:
        torch.distributed.destroy_process_group()


if __name__ == "__main__":
    main()