"""Multi-path inference: parallel hypothesis generation and scoring."""

from __future__ import annotations

from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Any, Callable

from fusionagi.schemas.atomic import AtomicSemanticUnit
from fusionagi.reasoning.tot import ThoughtNode
from fusionagi._logger import logger


def _score_coherence(node: ThoughtNode, _units: list[AtomicSemanticUnit]) -> float:
    return node.score * (0.9 + 0.1 * min(1, len(node.trace) / 5))


def _score_consistency(node: ThoughtNode, units: list[AtomicSemanticUnit]) -> float:
    if not units:
        return 0.5
    unit_content = " ".join(u.content.lower() for u in units)
    thought_words = set(node.thought.lower().split())
    unit_words = set(unit_content.split())
    overlap = len(thought_words & unit_words) / max(len(thought_words), 1)
    return min(1.0, overlap * 2)


def generate_and_score_parallel(
    hypotheses: list[str],
    units: list[AtomicSemanticUnit],
    score_fn: Callable[[ThoughtNode, list[AtomicSemanticUnit]], float] | None = None,
) -> list[tuple[ThoughtNode, float]]:
    """Score multiple hypotheses in parallel."""
    score_fn = score_fn or (lambda n, u: _score_coherence(n, u) * 0.5 + _score_consistency(n, u) * 0.5)

    def score_one(h: str, i: int) -> tuple[ThoughtNode, float]:
        node = ThoughtNode(thought=h, trace=[h], unit_refs=[u.unit_id for u in units[:10]])
        s = score_fn(node, units)
        node.score = s
        return node, s

    results: list[tuple[ThoughtNode, float]] = []
    with ThreadPoolExecutor(max_workers=min(len(hypotheses), 8)) as ex:
        futures = {ex.submit(score_one, h, i): i for i, h in enumerate(hypotheses)}
        for future in as_completed(futures):
            try:
                node, score = future.result()
                results.append((node, score))
            except Exception as e:
                logger.warning("Multi-path score failed", extra={"error": str(e)})
    return results