"""Checkpoint Management for Training"""

import json
import logging
import shutil
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional

import torch

logger = logging.getLogger(__name__)


@dataclass
class CheckpointMetadata:
    """Metadata for a checkpoint."""
    step: int
    epoch: int
    global_step: int
    metrics: Dict[str, float] = field(default_factory=dict)
    config: Dict[str, Any] = field(default_factory=dict)
    model_name: str = "zenith"
    timestamp: str = ""

    def to_dict(self) -> Dict[str, Any]:
        return {
            "step": self.step,
            "epoch": self.epoch,
            "global_step": self.global_step,
            "metrics": self.metrics,
            "config": self.config,
            "model_name": self.model_name,
            "timestamp": self.timestamp,
        }

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> "CheckpointMetadata":
        return cls(**data)


class CheckpointManager:
    """Manages saving and loading of checkpoints."""

    def __init__(
        self,
        checkpoint_dir: str,
        save_total_limit: int = 5,
        save_best_only: bool = False,
        metric_for_best: str = "eval_loss",
        greater_is_better: bool = False,
    ):
        self.checkpoint_dir = Path(checkpoint_dir)
        self.checkpoint_dir.mkdir(parents=True, exist_ok=True)
        self.save_total_limit = save_total_limit
        self.save_best_only = save_best_only
        self.metric_for_best = metric_for_best
        self.greater_is_better = greater_is_better

        self.best_metric = None
        self.checkpoints: List[Path] = []

        # Load existing checkpoints
        self._scan_checkpoints()

    def _scan_checkpoints(self):
        """Scan checkpoint directory for existing checkpoints."""
        for path in self.checkpoint_dir.glob("checkpoint-*"):
            if path.is_dir():
                self.checkpoints.append(path)
        self.checkpoints.sort(key=lambda p: int(p.name.split("-")[1]))

    def save_checkpoint(
        self,
        state: Dict[str, Any],
        name: str,
        metrics: Optional[Dict[str, float]] = None,
    ) -> Path:
        """Save checkpoint to disk."""
        checkpoint_path = self.checkpoint_dir / f"checkpoint-{name}"
        checkpoint_path.mkdir(exist_ok=True)

        # Save model state
        torch.save(state["model_state_dict"], checkpoint_path / "pytorch_model.bin")

        # Save optimizer and scheduler states
        if "optimizer_state_dict" in state:
            torch.save(state["optimizer_state_dict"], checkpoint_path / "optimizer.pt")
        if "scheduler_state_dict" in state and state["scheduler_state_dict"]:
            torch.save(state["scheduler_state_dict"], checkpoint_path / "scheduler.pt")
        if "scaler_state_dict" in state and state["scaler_state_dict"]:
            torch.save(state["scaler_state_dict"], checkpoint_path / "scaler.pt")

        # Save metadata
        metadata = CheckpointMetadata(
            step=state.get("step", 0),
            epoch=state.get("epoch", 0),
            global_step=state.get("global_step", 0),
            metrics=metrics or {},
            config=state.get("config", {}),
            timestamp=state.get("timestamp", ""),
        )
        with open(checkpoint_path / "metadata.json", "w") as f:
            json.dump(metadata.to_dict(), f, indent=2)

        logger.info(f"Checkpoint saved: {checkpoint_path}")

        # Update checkpoint list
        if checkpoint_path not in self.checkpoints:
            self.checkpoints.append(checkpoint_path)
            self.checkpoints.sort(key=lambda p: int(p.name.split("-")[1]))

        # Enforce limit
        if self.save_total_limit > 0 and len(self.checkpoints) > self.save_total_limit:
            self._remove_oldest_checkpoint()

        return checkpoint_path

    def load_checkpoint(
        self,
        checkpoint_path: Union[str, Path],
        model: torch.nn.Module,
        optimizer: Optional[torch.optim.Optimizer] = None,
        scheduler: Optional[Any] = None,
        scaler: Optional[torch.cuda.amp.GradScaler] = None,
    ) -> CheckpointMetadata:
        """Load checkpoint from disk."""
        checkpoint_path = Path(checkpoint_path)

        if not checkpoint_path.exists():
            raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")

        # Load model
        model_path = checkpoint_path / "pytorch_model.bin"
        if model_path.exists():
            state_dict = torch.load(model_path, map_location="cpu")
            model.load_state_dict(state_dict)
            logger.info(f"Loaded model from {model_path}")
        else:
            logger.warning(f"Model weights not found at {model_path}")

        # Load optimizer
        if optimizer is not None:
            opt_path = checkpoint_path / "optimizer.pt"
            if opt_path.exists():
                optimizer.load_state_dict(torch.load(opt_path, map_location="cpu"))
                logger.info(f"Loaded optimizer from {opt_path}")

        # Load scheduler
        if scheduler is not None:
            sched_path = checkpoint_path / "scheduler.pt"
            if sched_path.exists():
                scheduler.load_state_dict(torch.load(sched_path, map_location="cpu"))
                logger.info(f"Loaded scheduler from {sched_path}")

        # Load scaler
        if scaler is not None:
            scaler_path = checkpoint_path / "scaler.pt"
            if scaler_path.exists():
                scaler.load_state_dict(torch.load(scaler_path, map_location="cpu"))
                logger.info(f"Loaded scaler from {scaler_path}")

        # Load metadata
        meta_path = checkpoint_path / "metadata.json"
        if meta_path.exists():
            with open(meta_path, "r") as f:
                metadata = CheckpointMetadata.from_dict(json.load(f))
            logger.info(f"Loaded metadata: epoch={metadata.epoch}, step={metadata.step}")
        else:
            metadata = CheckpointMetadata(step=0, epoch=0, global_step=0)

        return metadata

    def get_latest_checkpoint(self) -> Optional[Path]:
        """Get the most recent checkpoint."""
        if self.checkpoints:
            return self.checkpoints[-1]
        return None

    def get_best_checkpoint(self) -> Optional[Path]:
        """Get the best checkpoint based on metric."""
        if not self.checkpoints:
            return None

        best_path = None
        best_value = None

        for path in self.checkpoints:
            meta_path = path / "metadata.json"
            if meta_path.exists():
                with open(meta_path, "r") as f:
                    meta = CheckpointMetadata.from_dict(json.load(f))

                if self.metric_for_best in meta.metrics:
                    value = meta.metrics[self.metric_for_best]
                    if best_value is None or (
                        self.greater_is_better and value > best_value
                    ) or (not self.greater_is_better and value < best_value):
                        best_value = value
                        best_path = path

        return best_path

    def _remove_oldest_checkpoint(self):
        """Remove the oldest checkpoint to maintain limit."""
        if len(self.checkpoints) > self.save_total_limit:
            oldest = self.checkpoints.pop(0)
            if oldest.exists():
                shutil.rmtree(oldest)
                logger.info(f"Removed old checkpoint: {oldest}")

    def cleanup(self, keep: Optional[List[Path]] = None):
        """Clean up checkpoints, optionally keeping specific ones."""
        if keep is None:
            keep = []

        for path in self.checkpoints:
            if path not in keep:
                if path.exists():
                    shutil.rmtree(path)
                    logger.info(f"Removed checkpoint: {path}")

        self._scan_checkpoints()


def save_checkpoint(
    model: torch.nn.Module,
    optimizer: torch.optim.Optimizer,
    scheduler: Optional[Any],
    scaler: Optional[torch.cuda.amp.GradScaler],
    checkpoint_dir: str,
    epoch: int,
    global_step: int,
    metrics: Optional[Dict[str, float]] = None,
    config: Optional[Dict[str, Any]] = None,
    save_optimizer: bool = True,
    save_scheduler: bool = True,
):
    """Convenience function to save a checkpoint."""
    manager = CheckpointManager(checkpoint_dir, save_total_limit=0)
    state = {
        "model_state_dict": model.state_dict(),
        "global_step": global_step,
        "epoch": epoch,
        "config": config or {},
        "timestamp": "",
    }

    if save_optimizer:
        state["optimizer_state_dict"] = optimizer.state_dict()
    if save_scheduler and scheduler is not None:
        state["scheduler_state_dict"] = scheduler.state_dict()

    manager.save_checkpoint(state, f"step-{global_step}", metrics)


def load_checkpoint(
    checkpoint_path: str,
    model: torch.nn.Module,
    optimizer: Optional[torch.optim.Optimizer] = None,
    scheduler: Optional[Any] = None,
    scaler: Optional[torch.cuda.amp.GradScaler] = None,
) -> int:
    """Convenience function to load a checkpoint."""
    manager = CheckpointManager(Path(checkpoint_path).parent)
    metadata = manager.load_checkpoint(checkpoint_path, model, optimizer, scheduler, scaler)
    return metadata.global_step