"""SWE-bench Lite task adapter for CodeEnv."""

from __future__ import annotations

import os
from functools import lru_cache
from pathlib import Path
from typing import Any, Dict, List, Tuple


DATASET_ROOT = Path(__file__).parent
DEFAULT_TASKS_ROOT = DATASET_ROOT / "swebench_lite_tasks"
DIFFICULTIES = ("easy", "medium", "hard")


def _difficulty_bounds(total: int) -> Dict[str, Tuple[int, int]]:
    one_third = max(total // 3, 1)
    two_third = max((2 * total) // 3, one_third + 1)
    return {
        "easy": (0, one_third),
        "medium": (one_third, two_third),
        "hard": (two_third, total),
    }


@lru_cache(maxsize=1)
def _load_swebench_lite_rows() -> List[Dict[str, Any]]:
    from datasets import load_dataset

    ds = load_dataset("princeton-nlp/SWE-bench_Lite", split="test")
    return [dict(row) for row in ds]


def _candidate_dirs(tasks_root: Path, instance_id: str, row_idx: int) -> List[Path]:
    return [
        tasks_root / instance_id,
        tasks_root / f"instance_{row_idx}",
        tasks_root / str(row_idx),
    ]


def get_swebench_task(difficulty: str) -> Dict[str, Any]:
    """
    Resolve one SWE-bench Lite task into CodeEnv-compatible task dict.

    Expected local layout:
      dataset/swebench_lite_tasks/<instance_id>/buggy.py
      dataset/swebench_lite_tasks/<instance_id>/test.py
    
    First tries to load from local files, then falls back to HuggingFace dataset.
    """
    diff = (difficulty or "").strip().lower()
    if diff not in DIFFICULTIES:
        raise ValueError(f"Invalid difficulty '{difficulty}'. Must be one of {DIFFICULTIES}.")

    tasks_root = Path(os.getenv("SWEBENCH_TASKS_ROOT", str(DEFAULT_TASKS_ROOT)))
    
    # First, try to load from local materialized tasks
    if tasks_root.exists():
        # Find all instance directories
        instance_dirs = []
        for item in tasks_root.iterdir():
            if item.is_dir() and (item / "buggy.py").exists() and (item / "test.py").exists():
                # Check if this directory matches the difficulty
                if diff in item.name.lower():
                    instance_dirs.append(item)
        
        if instance_dirs:
            # Sort for deterministic selection
            instance_dirs.sort(key=lambda x: x.name)
            
            # Select based on SWEBENCH_INDEX
            preferred_offset = int(os.getenv("SWEBENCH_INDEX", "0"))
            selected_dir = instance_dirs[preferred_offset % len(instance_dirs)]
            
            buggy_file = selected_dir / "buggy.py"
            test_file = selected_dir / "test.py"
            metadata_file = selected_dir / "metadata.json"
            
            code = buggy_file.read_text(encoding="utf-8")
            
            # Load metadata if available
            metadata = {"source": "swebench_lite", "difficulty": diff}
            if metadata_file.exists():
                import json
                metadata = json.loads(metadata_file.read_text(encoding="utf-8"))
            
            return {
                "code": code,
                "tests": str(test_file),
                "metadata": metadata,
                "problem_dir": str(selected_dir),
                "problem_id": selected_dir.name,
            }
    
    # Fallback: try to load from HuggingFace dataset
    try:
        rows = _load_swebench_lite_rows()
        if not rows:
            raise RuntimeError("SWE-bench Lite split is empty.")

        bounds = _difficulty_bounds(len(rows))
        start, end = bounds[diff]
        candidates = rows[start:end] if end > start else rows

        preferred_offset = int(os.getenv("SWEBENCH_INDEX", "0"))

        # Deterministic scan order with optional offset.
        ordered = candidates[preferred_offset:] + candidates[:preferred_offset]
        for row in ordered:
            row_idx = int(row.get("__index_level_0__", 0))
            instance_id = str(row.get("instance_id", f"row_{row_idx}"))
            for folder in _candidate_dirs(tasks_root, instance_id, row_idx):
                buggy_file = folder / "buggy.py"
                test_file = folder / "test.py"
                if buggy_file.exists() and test_file.exists():
                    code = buggy_file.read_text(encoding="utf-8")
                    metadata = {
                        "source": "swebench_lite",
                        "instance_id": instance_id,
                        "repo": row.get("repo"),
                        "base_commit": row.get("base_commit"),
                        "problem_statement": row.get("problem_statement"),
                        "difficulty": diff,
                    }
                    return {
                        "code": code,
                        "tests": str(test_file),
                        "metadata": metadata,
                        "problem_dir": str(folder),
                        "problem_id": instance_id,
                    }
    except Exception as e:
        # If HuggingFace fails, raise the original error about missing local files
        pass

    raise FileNotFoundError(
        "No materialized SWE-bench task workspace found. "
        f"Expected buggy.py/test.py under '{tasks_root}'."
    )