"""Data/model loading helpers for the GRPO training notebook."""

from __future__ import annotations

import json
import logging
from pathlib import Path
from typing import Any

logger = logging.getLogger(__name__)


def filter_questions_by_difficulty(
    questions: list[dict[str, Any]], allowed: list[str] | None
) -> list[dict[str, Any]]:
    """Filter question records by case-insensitive difficulty labels."""

    if not allowed:
        return questions

    allowed_set = {level.lower() for level in allowed}
    return [
        question
        for question in questions
        if str(question.get("difficulty", "")).lower() in allowed_set
    ]


def _drop_harness_broken(
    questions: list[dict[str, Any]], questions_path: str, db_dir: str
) -> list[dict[str, Any]]:
    """Drop questions whose gold answer is harness-broken (see validator)."""
    # The installed-package form (sql_env.scripts) is tried first — it is the
    # only one that resolves when analyst-buddy is pip-installed. The bare forms
    # cover pytest (pythonpath=".") and direct script invocation.
    try:
        from sql_env.scripts.validate_questions import broken_question_texts
    except ImportError:  # pragma: no cover - fallback for non-installed contexts
        try:
            from scripts.validate_questions import broken_question_texts
        except ImportError:
            from validate_questions import broken_question_texts  # type: ignore

    broken = broken_question_texts(questions_path, db_dir)
    if not broken:
        return questions
    kept = [q for q in questions if q.get("question_text") not in broken]
    logger.warning(
        "Excluded %d harness-broken question(s) from %s before training "
        "(run scripts/validate_questions.py for details).",
        len(questions) - len(kept),
        questions_path,
    )
    return kept


def load_question_prompts(
    questions_path: str,
    allowed: list[str] | None = None,
    db_dir: str | None = None,
) -> list[dict[str, str]]:
    """Load question text prompts from JSON and apply difficulty filtering.

    When ``db_dir`` is provided, harness-broken questions (broken/empty gold
    answers) are dropped before prompts are built, so they never reach the
    gradient. Leave ``db_dir`` as None to skip the (slightly slower) gold check.
    """

    path = Path(questions_path)
    if not path.exists():
        raise FileNotFoundError(f"Questions file not found: {questions_path}")

    try:
        payload = json.loads(path.read_text(encoding="utf-8"))
    except json.JSONDecodeError as exc:
        raise ValueError(f"Invalid JSON in questions file: {questions_path}") from exc

    if not isinstance(payload, list) or not payload:
        raise ValueError(f"Questions file is empty or invalid: {questions_path}")

    filtered = filter_questions_by_difficulty(payload, allowed)
    if not filtered:
        raise ValueError(
            f"No questions match difficulty_filter={allowed} in {questions_path}"
        )

    if db_dir is not None:
        filtered = _drop_harness_broken(filtered, questions_path, db_dir)
        if not filtered:
            raise ValueError(
                f"All questions in {questions_path} are harness-broken "
                f"for db_dir={db_dir}"
            )

    prompts = [
        {"prompt": str(item["question_text"])}
        for item in filtered
        if item.get("question_text")
    ]
    if not prompts:
        raise ValueError(f"No usable question_text values found in {questions_path}")

    return prompts


def validate_no_data_leak(
    train_path: str,
    eval_path: str,
) -> None:
    """Assert zero question overlap between train and eval sets.

    Raises
    ------
    ValueError
        If any question text appears in both files.
    """
    train = json.loads(Path(train_path).read_text(encoding="utf-8"))
    eval_ = json.loads(Path(eval_path).read_text(encoding="utf-8"))

    train_qs = {q["question_text"] for q in train if "question_text" in q}
    eval_qs = {q["question_text"] for q in eval_ if "question_text" in q}
    overlap = train_qs & eval_qs

    if overlap:
        examples = list(overlap)[:3]
        raise ValueError(
            f"Data leak: {len(overlap)} questions appear in both train and eval. "
            f"Examples: {examples}"
        )


def load_model_and_tokenizer(
    model_name: str,
    *,
    revision: str | None = None,
    torch_dtype: Any | None = None,
) -> tuple[Any, Any]:
    """Load HuggingFace tokenizer and model with fail-fast errors.

    ``transformers`` is imported lazily here (not at module top) so the data /
    guardrail helpers in this module stay importable without the heavy training
    extras installed.

    Pass ``revision`` (a Hub commit SHA or tag) to pin the exact weights for
    reproducibility — without it, ``model_name`` resolves to whatever is HEAD on
    the Hub at download time, so two runs weeks apart can load different weights.
    ``torch_dtype`` (e.g. "bfloat16") is forwarded when set.
    """

    from transformers import AutoModelForCausalLM, AutoTokenizer

    tokenizer_kwargs: dict[str, Any] = {}
    model_kwargs: dict[str, Any] = {}
    if revision is not None:
        tokenizer_kwargs["revision"] = revision
        model_kwargs["revision"] = revision
    if torch_dtype is not None:
        # dtype applies to the model only — the tokenizer has no dtype, and passing
        # it there is at best ignored, at worst an error on some tokenizers.
        model_kwargs["torch_dtype"] = torch_dtype

    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
        model = AutoModelForCausalLM.from_pretrained(model_name, **model_kwargs)
    except Exception as exc:  # pragma: no cover - covered by monkeypatched tests
        raise RuntimeError(f"Cannot load model '{model_name}': {exc}") from exc
    return model, tokenizer