"""Ground truth comparison using GAIA validation dataset.

Author: @mangubee

Since the GAIA API only returns summary stats (X/Y correct) without per-question
correctness, we load the public validation dataset to compare our answers locally.
This enables per-question debugging and error analysis.
"""

import os
import logging
from typing import Dict, Optional

logger = logging.getLogger(__name__)

# ============================================================================
# CONFIG
# ============================================================================
CACHE_DIR = os.path.expanduser("~/.cache/gaia_dataset")
# ============================================================================


class GAIAGroundTruth:
    """Load GAIA validation dataset and provide ground truth answers."""

    def __init__(self):
        """Initialize ground truth loader."""
        self.ground_truth: Dict[str, str] = {}  # task_id -> final_answer
        self.metadata: Dict[str, dict] = {}  # task_id -> full item data
        self._loaded = False

    def load_validation_set(self) -> bool:
        """Load GAIA validation dataset from HuggingFace.

        Returns:
            bool: True if loaded successfully, False otherwise
        """
        if self._loaded:
            return True

        try:
            from datasets import load_dataset

            logger.info("Loading GAIA validation dataset...")

            # Load validation set (public answers)
            # Using 2023_all which includes all levels
            dataset = load_dataset(
                "gaia-benchmark/GAIA",
                "2023_all",
                split="validation",
                cache_dir=CACHE_DIR
            )

            # Build task_id -> final_answer mapping and metadata
            for item in dataset:
                task_id = item.get("task_id")
                final_answer = item.get("Final answer")

                if task_id and final_answer:
                    self.ground_truth[task_id] = str(final_answer).strip()
                    # Store full item for metadata access
                    self.metadata[task_id] = dict(item)

            self._loaded = True
            logger.info(f"Loaded {len(self.ground_truth)} ground truth answers")
            return True

        except Exception as e:
            logger.error(f"Failed to load GAIA dataset: {e}")
            return False

    def get_answer(self, task_id: str) -> Optional[str]:
        """Get ground truth answer for a task_id.

        Args:
            task_id: Question task ID

        Returns:
            Ground truth answer or None if not found
        """
        if not self._loaded:
            self.load_validation_set()

        return self.ground_truth.get(task_id)

    def compare_answer(self, task_id: str, submitted_answer: str) -> Optional[bool]:
        """Compare submitted answer against ground truth.

        Args:
            task_id: Question task ID
            submitted_answer: Answer submitted by agent

        Returns:
            True if correct, False if incorrect, None if no ground truth available
        """
        ground_truth = self.get_answer(task_id)

        if ground_truth is None:
            return None

        # Normalize both answers for comparison
        submitted = str(submitted_answer).strip().lower()
        expected = str(ground_truth).strip().lower()

        # Exact match comparison
        return submitted == expected


# Singleton instance
_ground_truth_instance = None


def get_ground_truth() -> GAIAGroundTruth:
    """Get or create singleton ground truth instance.

    Returns:
        GAIAGroundTruth instance
    """
    global _ground_truth_instance

    if _ground_truth_instance is None:
        _ground_truth_instance = GAIAGroundTruth()

    return _ground_truth_instance