"""Batch operations for import/export with progress tracking and error recovery.

Provides utilities for large-scale synchronization between PugBrain and
external memory systems with features like:
- Progress tracking and callbacks
- Error recovery (resume from checkpoint)
- Rate limiting
- Cancellation support
"""

from __future__ import annotations

import asyncio
import json
import logging
from dataclasses import dataclass, field
from datetime import datetime
from enum import StrEnum
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable

from neural_memory.integration.models import ExportResult, ImportResult
from neural_memory.utils.timeutils import utcnow

if TYPE_CHECKING:
    from neural_memory.integration.adapter import SourceAdapter
    from neural_memory.integration.sync_engine import SyncEngine

logger = logging.getLogger(__name__)

# Default rate limits
_DEFAULT_REQUESTS_PER_SECOND = 10
_DEFAULT_BATCH_SIZE = 50

# Callback types
ProgressCallback = Callable[[int, int, str], None]  # (current, total, current_id)
StatusCallback = Callable[[str, dict[str, Any]], None]  # (status, metadata)


class BatchOperationStatus(StrEnum):
    """Status of a batch operation."""

    PENDING = "pending"
    RUNNING = "running"
    PAUSED = "paused"
    COMPLETED = "completed"
    FAILED = "failed"
    CANCELLED = "cancelled"


@dataclass
class BatchCheckpoint:
    """Checkpoint for resumable batch operations.

    Attributes:
        operation_id: Unique identifier for this operation
        operation_type: "import" or "export"
        source_system: Source/target system name
        collection: Collection name
        started_at: When the operation started
        last_record_id: ID of the last processed record
        processed_count: Number of records processed so far
        failed_count: Number of failed records
        status: Current operation status
        metadata: Additional checkpoint data
    """

    operation_id: str
    operation_type: str
    source_system: str
    collection: str
    started_at: datetime
    last_record_id: str | None = None
    processed_count: int = 0
    failed_count: int = 0
    status: BatchOperationStatus = BatchOperationStatus.PENDING
    metadata: dict[str, Any] = field(default_factory=dict)

    def to_dict(self) -> dict[str, Any]:
        """Serialize checkpoint to dictionary."""
        return {
            "operation_id": self.operation_id,
            "operation_type": self.operation_type,
            "source_system": self.source_system,
            "collection": self.collection,
            "started_at": self.started_at.isoformat(),
            "last_record_id": self.last_record_id,
            "processed_count": self.processed_count,
            "failed_count": self.failed_count,
            "status": self.status.value,
            "metadata": self.metadata,
        }

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> BatchCheckpoint:
        """Deserialize checkpoint from dictionary."""
        return cls(
            operation_id=data["operation_id"],
            operation_type=data["operation_type"],
            source_system=data["source_system"],
            collection=data["collection"],
            started_at=datetime.fromisoformat(data["started_at"]),
            last_record_id=data.get("last_record_id"),
            processed_count=data.get("processed_count", 0),
            failed_count=data.get("failed_count", 0),
            status=BatchOperationStatus(data.get("status", "pending")),
            metadata=data.get("metadata", {}),
        )


@dataclass
class BatchConfig:
    """Configuration for batch operations.

    Attributes:
        batch_size: Number of records per batch
        requests_per_second: Rate limit for API calls
        max_retries: Maximum retry attempts per record
        retry_delay_seconds: Delay between retries
        checkpoint_interval: Save checkpoint every N records
        checkpoint_path: Path to save checkpoint files
    """

    batch_size: int = _DEFAULT_BATCH_SIZE
    requests_per_second: float = _DEFAULT_REQUESTS_PER_SECOND
    max_retries: int = 3
    retry_delay_seconds: float = 1.0
    checkpoint_interval: int = 100
    checkpoint_path: Path | None = None


class BatchOperationManager:
    """Manages batch import/export operations with progress tracking and recovery.

    Usage:
        manager = BatchOperationManager(sync_engine, config)

        # Import with progress tracking
        result = await manager.import_with_progress(
            adapter=mem0_adapter,
            collection="user123",
            on_progress=lambda c, t, id: print(f"{c}/{t}: {id}"),
        )

        # Export with checkpointing
        checkpoint = await manager.export_with_checkpoint(
            adapter=mem0_adapter,
            collection="user123",
            checkpoint_path=Path("export_checkpoint.json"),
        )
    """

    def __init__(
        self,
        sync_engine: SyncEngine,
        config: BatchConfig | None = None,
    ) -> None:
        self._engine = sync_engine
        self._config = config or BatchConfig()
        self._cancelled = False
        self._paused = False

    def cancel(self) -> None:
        """Cancel the current operation."""
        self._cancelled = True

    def pause(self) -> None:
        """Pause the current operation."""
        self._paused = True

    def resume(self) -> None:
        """Resume a paused operation."""
        self._paused = False

    async def import_with_progress(
        self,
        adapter: SourceAdapter,
        collection: str | None = None,
        limit: int | None = None,
        on_progress: ProgressCallback | None = None,
        on_status: StatusCallback | None = None,
    ) -> ImportResult:
        """Run import with progress tracking and rate limiting.

        Args:
            adapter: Source adapter to import from
            collection: Optional collection filter
            limit: Optional maximum records to import
            on_progress: Optional progress callback
            on_status: Optional status callback

        Returns:
            ImportResult with import statistics
        """
        self._cancelled = False
        self._paused = False

        operation_id = f"import_{adapter.system_name}_{utcnow().strftime('%Y%m%d_%H%M%S')}"

        if on_status:
            on_status("started", {"operation_id": operation_id})

        # Rate limiter
        rate_limiter = _RateLimiter(self._config.requests_per_second)

        # Wrap progress callback with rate limiting
        tracked_progress: list[int] = [0]

        async def tracked_callback(current: int, total: int, record_id: str) -> None:
            tracked_progress[0] = current

            # Check for cancellation
            if self._cancelled:
                raise asyncio.CancelledError("Operation cancelled by user")

            # Check for pause
            while self._paused:
                await asyncio.sleep(0.5)
                if self._cancelled:
                    raise asyncio.CancelledError("Operation cancelled by user")

            # Apply rate limiting
            await rate_limiter.acquire()

            if on_progress:
                on_progress(current, total, record_id)

        try:
            result = await self._engine.sync(
                adapter=adapter,
                collection=collection,
                limit=limit,
                progress_callback=tracked_callback if on_progress or self._config.requests_per_second > 0 else None,
            )
            result = result[0]  # sync returns (ImportResult, SyncState)

            if on_status:
                on_status("completed", {
                    "operation_id": operation_id,
                    "records_imported": result.records_imported,
                    "duration": result.duration_seconds,
                })

            return result

        except asyncio.CancelledError:
            if on_status:
                on_status("cancelled", {
                    "operation_id": operation_id,
                    "processed": tracked_progress[0],
                })
            raise
        except Exception as e:
            if on_status:
                on_status("failed", {
                    "operation_id": operation_id,
                    "error": str(e),
                    "processed": tracked_progress[0],
                })
            raise

    async def export_with_checkpoint(
        self,
        adapter: SourceAdapter,
        collection: str | None = None,
        limit: int | None = None,
        checkpoint_path: Path | None = None,
        resume_from: Path | None = None,
        on_progress: ProgressCallback | None = None,
        on_status: StatusCallback | None = None,
    ) -> tuple[ExportResult, BatchCheckpoint | None]:
        """Run export with checkpointing for resumable operations.

        Args:
            adapter: Target adapter to export to
            collection: Optional collection in target
            limit: Optional maximum records to export
            checkpoint_path: Path to save checkpoint (for resume)
            resume_from: Path to existing checkpoint to resume from
            on_progress: Optional progress callback
            on_status: Optional status callback

        Returns:
            Tuple of (ExportResult, final checkpoint or None)
        """
        self._cancelled = False
        self._paused = False

        # Load existing checkpoint if resuming
        checkpoint: BatchCheckpoint | None = None
        if resume_from and resume_from.exists():
            try:
                data = json.loads(resume_from.read_text())
                checkpoint = BatchCheckpoint.from_dict(data)
                logger.info(
                    "Resuming export from checkpoint: %s records already processed",
                    checkpoint.processed_count,
                )
            except Exception:
                logger.warning("Failed to load checkpoint, starting fresh", exc_info=True)

        if checkpoint is None:
            checkpoint = BatchCheckpoint(
                operation_id=f"export_{adapter.system_name}_{utcnow().strftime('%Y%m%d_%H%M%S')}",
                operation_type="export",
                source_system=adapter.system_name,
                collection=collection or "default",
                started_at=utcnow(),
                status=BatchOperationStatus.RUNNING,
            )

        if on_status:
            on_status("started", {"operation_id": checkpoint.operation_id})

        rate_limiter = _RateLimiter(self._config.requests_per_second)
        checkpoint_counter = 0

        def save_checkpoint() -> None:
            """Save checkpoint to file."""
            if checkpoint_path and checkpoint:
                checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
                checkpoint_path.write_text(json.dumps(checkpoint.to_dict(), indent=2))

        tracked_progress: list[int] = [checkpoint.processed_count]

        def tracked_callback(current: int, total: int, record_id: str) -> None:
            nonlocal checkpoint_counter

            tracked_progress[0] = current

            if checkpoint:
                checkpoint.last_record_id = record_id
                checkpoint.processed_count = current

                # Save checkpoint periodically
                checkpoint_counter += 1
                if checkpoint_counter >= self._config.checkpoint_interval:
                    save_checkpoint()
                    checkpoint_counter = 0

            if on_progress:
                on_progress(current, total, record_id)

        try:
            # Run export
            result = await self._engine.export(
                adapter=adapter,
                collection=collection,
                limit=limit,
                progress_callback=tracked_callback,
            )

            if checkpoint:
                checkpoint.status = BatchOperationStatus.COMPLETED
                save_checkpoint()

            if on_status:
                on_status("completed", {
                    "operation_id": checkpoint.operation_id if checkpoint else None,
                    "records_exported": result.records_exported,
                    "duration": result.duration_seconds,
                })

            return result, checkpoint

        except asyncio.CancelledError:
            if checkpoint:
                checkpoint.status = BatchOperationStatus.CANCELLED
                save_checkpoint()
            if on_status:
                on_status("cancelled", {
                    "operation_id": checkpoint.operation_id if checkpoint else None,
                    "processed": tracked_progress[0],
                })
            raise
        except Exception as e:
            if checkpoint:
                checkpoint.status = BatchOperationStatus.FAILED
                checkpoint.metadata["error"] = str(e)
                save_checkpoint()
            if on_status:
                on_status("failed", {
                    "operation_id": checkpoint.operation_id if checkpoint else None,
                    "error": str(e),
                    "processed": tracked_progress[0],
                })
            raise

    @staticmethod
    def load_checkpoint(path: Path) -> BatchCheckpoint | None:
        """Load a checkpoint from file.

        Args:
            path: Path to checkpoint file

        Returns:
            BatchCheckpoint or None if file doesn't exist or is invalid
        """
        if not path.exists():
            return None
        try:
            data = json.loads(path.read_text())
            return BatchCheckpoint.from_dict(data)
        except Exception:
            logger.warning("Failed to load checkpoint from %s", path, exc_info=True)
            return None


class _RateLimiter:
    """Simple token bucket rate limiter."""

    def __init__(self, requests_per_second: float) -> None:
        self._min_interval = 1.0 / requests_per_second if requests_per_second > 0 else 0
        self._last_time: float = 0

    async def acquire(self) -> None:
        """Wait until a request can be made within rate limits."""
        if self._min_interval == 0:
            return

        now = asyncio.get_running_loop().time()
        elapsed = now - self._last_time
        wait_time = self._min_interval - elapsed

        if wait_time > 0:
            await asyncio.sleep(wait_time)

        self._last_time = asyncio.get_running_loop().time()
