"""Train the ConvLSTM radar encoder-forecaster with full-sequence BPTT."""

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.convlstm import ConvLSTM


class RadarDataset(Dataset):
    def __init__(self, path, config):
        self.data = np.load(path)
        data = config["data"]
        if str(self.data["format_version"]) != data["format_version"]:
            raise ValueError("incompatible radar data format")
        expected_input = (int(data["input_frames"]), int(data["channels"]), int(data["height"]), int(data["width"]))
        expected_target = (int(data["output_frames"]), int(data["channels"]), int(data["height"]), int(data["width"]))
        if self.data["inputs"].shape[1:] != expected_input or self.data["targets"].shape[1:] != expected_target:
            raise ValueError("radar tensors do not preserve the paper dimensions")

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

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


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


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)
    dataset = RadarDataset(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 = ConvLSTM(config["model"]).to(device)
    if distributed:
        model = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None)
    optimizer = torch.optim.RMSprop(model.parameters(), lr=float(config["train"]["learning_rate"]),
                                    alpha=float(config["train"]["rmsprop_alpha"]),
                                    weight_decay=float(config["train"]["weight_decay"]))
    history = []
    for epoch in range(int(config["train"]["epochs"])):
        model.train()
        total, steps = 0.0, 0
        for inputs, targets in loader:
            _, logits = model(inputs.to(device))
            patched_target = torch.nn.functional.pixel_unshuffle(targets.to(device).flatten(0, 1),
                                                                  int(config["model"]["patch_size"])).unflatten(0, targets.shape[:2])
            loss = torch.nn.functional.binary_cross_entropy_with_logits(logits, patched_target)
            optimizer.zero_grad(set_to_none=True)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), float(config["train"]["gradient_clip_norm"]))
            optimizer.step()
            total += float(loss.detach())
            steps += 1
        metrics = {"epoch": epoch + 1, "binary_cross_entropy": total / max(steps, 1)}
        history.append(metrics)
        if rank == 0:
            print(f"epoch={epoch + 1} binary_cross_entropy={metrics['binary_cross_entropy']:.6f}")
    if rank == 0:
        checkpoint, metrics_path = ROOT / config["paths"]["checkpoint"], 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"],
                    "format_version": config["data"]["format_version"]}, checkpoint)
        metrics_path.write_text(json.dumps({"history": history}, indent=2) + "\n")
    if distributed:
        torch.distributed.destroy_process_group()


if __name__ == "__main__":
    main()