"""Deterministic reference training with transactional, checksummed checkpoints."""
from dataclasses import asdict
from hashlib import sha256
from pathlib import Path
import json
import math
import os
import random
import time
import uuid
import numpy as np
import torch
from safetensors.torch import save_file
from .model import ModelConfig, NexoraLM


def seed_all(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)


def save_checkpoint(root, model, optimizer, step, generator, metadata):
    root = Path(root)
    root.mkdir(parents=True, exist_ok=True)
    name = f"step-{step:06d}-{uuid.uuid4().hex[:8]}.pt"
    target = root / name
    temp = root / (name + ".tmp")
    state = {"model": model.state_dict(), "optimizer": optimizer.state_dict(), "step": step,
             "config": asdict(model.config), "torch_rng": torch.get_rng_state(),
             "cuda_rng": torch.cuda.get_rng_state_all() if torch.cuda.is_available() else [],
             "batch_rng": generator.get_state(), "python_rng": random.getstate(),
             "numpy_rng": (np.random.get_state()[0], np.random.get_state()[1].tolist(), *np.random.get_state()[2:]),
             "metadata": metadata}
    torch.save(state, temp)
    os.replace(temp, target)
    receipt = {"file": name, "sha256": sha256(target.read_bytes()).hexdigest(), "step": step}
    receipt_tmp = root / "latest.json.tmp"
    receipt_tmp.write_text(json.dumps(receipt), encoding="utf-8")
    os.replace(receipt_tmp, root / "latest.json")
    return receipt


def load_checkpoint(root, model, optimizer, generator):
    root = Path(root)
    receipt = json.loads((root / "latest.json").read_text(encoding="utf-8"))
    path = (root / receipt["file"]).resolve()
    if path.parent != root.resolve() or sha256(path.read_bytes()).hexdigest() != receipt["sha256"]:
        raise ValueError("Checkpoint path or checksum mismatch")
    state = torch.load(path, map_location="cpu", weights_only=True)
    if state["config"] != asdict(model.config):
        raise ValueError("Checkpoint architecture mismatch")
    model.load_state_dict(state["model"])
    optimizer.load_state_dict(state["optimizer"])
    torch.set_rng_state(state["torch_rng"])
    if state["cuda_rng"] and torch.cuda.is_available():
        torch.cuda.set_rng_state_all(state["cuda_rng"])
    generator.set_state(state["batch_rng"])
    random.setstate(state["python_rng"])
    n = state["numpy_rng"]
    np.random.set_state((n[0], np.asarray(n[1], dtype=np.uint32), *n[2:]))
    return state


def batch(tokens, size, length, generator, device):
    if len(tokens) <= length:
        raise ValueError("Shard too short for sequence length")
    starts = torch.randint(len(tokens) - length, (size,), generator=generator)
    x = torch.stack([tokens[i:i + length] for i in starts]).to(device)
    y = torch.stack([tokens[i + 1:i + length + 1] for i in starts]).to(device)
    return x, y


def train(config_path, data_dir, output, *, resume=False, stop_after=None):
    config = json.loads(Path(config_path).read_text(encoding="utf-8"))
    c, t = ModelConfig(**config["model"]), config["training"]
    if t["steps"] < 1 or t["batch_size"] < 1 or not 0 < t["sequence_length"] <= c.max_context:
        raise ValueError("Invalid training configuration")
    torch.set_num_threads(t.get("threads", 4))
    seed_all(t["seed"])
    device = t.get("device", "auto")
    device = ("cuda" if torch.cuda.is_available() else "cpu") if device == "auto" else device
    model = NexoraLM(c).to(device)
    optimizer = torch.optim.AdamW(model.parameters(), lr=t["learning_rate"], weight_decay=0.1)
    generator = torch.Generator().manual_seed(t["seed"] + 1)
    out, data = Path(output), Path(data_dir)
    out.mkdir(parents=True, exist_ok=True)
    manifest_path = data / "manifest.json"
    manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
    if manifest["tokenizer"] != "utf8-byte-v1" or c.vocab_size != 259:
        raise ValueError("Tokenizer/model incompatibility")
    for split in ("train", "validation"):
        info = manifest["shards"][split]
        if sha256((data / info["file"]).read_bytes()).hexdigest() != info["sha256"]:
            raise ValueError("Dataset checksum mismatch")
    train_ids = torch.from_numpy(np.load(data / "train.npy", allow_pickle=False).astype(np.int64))
    val_ids = torch.from_numpy(np.load(data / "validation.npy", allow_pickle=False).astype(np.int64))
    metadata = {"data_manifest_sha256": sha256(manifest_path.read_bytes()).hexdigest(),
                "training": t, "experiment_id": uuid.uuid4().hex}
    start = 0
    if resume:
        state = load_checkpoint(out / "checkpoints", model, optimizer, generator)
        if state["metadata"]["data_manifest_sha256"] != metadata["data_manifest_sha256"] or state["metadata"]["training"] != t:
            raise ValueError("Resume requires identical data and training configuration")
        metadata, start = state["metadata"], state["step"]
    metrics = []
    start_time = time.perf_counter()
    if device.startswith("cuda"):
        torch.cuda.reset_peak_memory_stats()
    final_step = min(t["steps"], stop_after) if stop_after is not None else t["steps"]
    if final_step < start:
        raise ValueError("Stop step precedes checkpoint")
    for step in range(start, final_step):
        model.train()
        # Schedule depends on planned total steps, so interrupted runs resume identically.
        lr = t["learning_rate"] * (0.1 + 0.9 * (1 + math.cos(math.pi * step / t["steps"])) / 2)
        for group in optimizer.param_groups:
            group["lr"] = lr
        x, y = batch(train_ids, t["batch_size"], t["sequence_length"], generator, device)
        optimizer.zero_grad(set_to_none=True)
        _, loss = model(x, y)
        if not torch.isfinite(loss):
            raise FloatingPointError("Non-finite training loss")
        loss.backward()
        grad = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0, error_if_nonfinite=True)
        optimizer.step()
        if step == start or (step + 1) % t["eval_every"] == 0 or step + 1 == final_step:
            model.eval()
            vg = torch.Generator().manual_seed(917)
            with torch.no_grad():
                vx, vy = batch(val_ids, t["batch_size"], t["sequence_length"], vg, device)
                _, vl = model(vx, vy)
            row = {"step": step + 1, "train_loss": loss.item(), "validation_loss": vl.item(), "grad_norm": float(grad), "lr": lr}
            metrics.append(row)
            print(json.dumps(row), flush=True)
        if (step + 1) % t["checkpoint_every"] == 0 or step + 1 == final_step:
            save_checkpoint(out / "checkpoints", model, optimizer, step + 1, generator, metadata)
    if device.startswith("cuda"):
        torch.cuda.synchronize()
    elapsed = time.perf_counter() - start_time
    save_file({k: v.detach().cpu().contiguous() for k, v in model.state_dict().items()}, str(out / "model.safetensors"))
    (out / "config.json").write_text(json.dumps(asdict(c), indent=2), encoding="utf-8")
    report = {"status": "VALIDATED_SMALL_TRAINING_ONLY", "parameters": model.parameter_count(), "device": device,
              "elapsed_seconds": elapsed, "tokens_per_second_including_eval_and_checkpoints": (final_step-start)*t["batch_size"]*t["sequence_length"]/max(elapsed, 1e-9),
              "peak_vram_bytes": torch.cuda.max_memory_allocated() if device.startswith("cuda") else None,
              "steps_completed": final_step, "metadata": metadata, "metrics": metrics,
              "limitations": "Tiny educational corpus; no general assistant, reasoning or coding capability claim"}
    (out / ("resume-report.json" if resume else "training-report.json")).write_text(json.dumps(report, indent=2), encoding="utf-8")
    return report