"""Shared utilities used across tool modules."""

from __future__ import annotations

import asyncio
import logging
import re
from datetime import date, datetime
from difflib import SequenceMatcher

logger = logging.getLogger(__name__)

_PUNCT_RE = re.compile(r"[^\w\s]")


def schedule_embedding(note_id: int, user_id: int, title: str, body: str) -> None:
    """Fire-and-forget: update the embedding for a note after it's created/modified."""
    from fabledassistant.services.embeddings import upsert_note_embedding

    text = f"{title}\n{body}".strip() if body else (title or "")
    if text:
        asyncio.create_task(upsert_note_embedding(note_id, user_id, text))


def _normalize(s: str) -> str:
    """Lowercase and collapse non-alphanumerics to single spaces."""
    return re.sub(r"[^a-z0-9]+", " ", s.lower()).strip()


def score_project_match(query: str, project) -> float:
    """Score how well `query` matches `project`. Range [0.0, 1.0].

    Tiered: exact title → 1.0, substring either-way → 0.85, query found in
    description/summary → 0.70, otherwise SequenceMatcher ratio. Substring
    tiers exist because LLM-generated colloquial queries (e.g. "famous
    supply project" for "Famous-Supply Work topics") would otherwise score
    too low under pure SequenceMatcher and be treated as no match.
    """
    q = _normalize(query)
    if not q:
        return 0.0
    title = _normalize(project.title)
    description = _normalize(project.description or "")
    summary = _normalize(project.auto_summary or "")
    combined = f"{title} {description} {summary}".strip()

    if q == title:
        return 1.0
    if q in title or title in q:
        return 0.85
    if q in combined:
        return 0.70
    return SequenceMatcher(None, q, combined).ratio()


async def resolve_project(user_id: int, project_name: str):
    """Exact-then-fuzzy project lookup. Returns the Project or None.

    Resolution order:
    1. Exact title match (case-insensitive via DB)
    2. project_name is a substring of an existing title
    3. Existing title is a substring of project_name
    4. SequenceMatcher ratio >= 0.55
    """
    from fabledassistant.services.projects import get_project_by_title, list_projects

    proj = await get_project_by_title(user_id, project_name)
    if proj is not None:
        return proj
    needle = project_name.lower().strip()
    all_p = await list_projects(user_id)
    for p in all_p:
        haystack = p.title.lower().strip()
        if needle in haystack or haystack in needle:
            return p
    best, best_r = None, 0.0
    for p in all_p:
        r = SequenceMatcher(None, needle, p.title.lower().strip()).ratio()
        if r >= 0.55 and r > best_r:
            best, best_r = p, r
    return best


def parse_due_date(value: str | None) -> date | None:
    """Parse a due date string, returning None on failure."""
    if not value:
        return None
    try:
        return datetime.strptime(value, "%Y-%m-%d").date()
    except (ValueError, TypeError):
        logger.warning("Invalid due_date format: %s", value)
        return None


def fuzzy_title_match(title: str, candidates, threshold: float = 0.82):
    """Return (best_match, ratio) if any candidate's title is similar enough.

    Uses SequenceMatcher ratio. Threshold 0.82 catches near-duplicates like
    "Game Premise" / "Game Premise Notes" while leaving clearly different
    titles alone. Returns (None, 0.0) when no candidate meets the threshold.
    """
    needle = title.lower().strip()
    best, best_r = None, 0.0
    for c in candidates:
        r = SequenceMatcher(None, needle, c.title.lower().strip()).ratio()
        if r >= threshold and r > best_r:
            best, best_r = c, r
    return best, best_r


async def check_duplicate(
    user_id: int,
    title: str,
    body: str,
    is_task: bool,
    confirmed: bool,
) -> dict | None:
    """Check for exact, fuzzy, and semantic duplicates. Returns error dict or None."""
    from fabledassistant.services.notes import list_notes

    item_label = "task" if is_task else "note"

    existing, _ = await list_notes(user_id=user_id, q=title, is_task=is_task, limit=1)
    exact = next((n for n in existing if n.title.lower() == title.lower()), None)
    if exact is not None:
        return {
            "success": False,
            "error": f"A {item_label} titled '{title}' already exists (id: {exact.id}). Use update_note to modify it instead of creating a duplicate.",
        }

    clean_q = _PUNCT_RE.sub(" ", title).strip()
    candidates, _ = await list_notes(user_id=user_id, q=clean_q, is_task=is_task, limit=20)
    near, ratio = fuzzy_title_match(title, candidates)
    if near is not None:
        return {
            "success": False,
            "requires_confirmation": True,
            "similar_note": {"id": near.id, "title": near.title},
            "error": f"A {item_label} with a very similar title '{near.title}' already exists (similarity: {ratio:.0%}). Ask the user to confirm before creating a separate entry.",
        }

    if not confirmed and len(body.strip()) >= 80:
        from fabledassistant.services.embeddings import semantic_search_notes as _ssn
        sem_query = f"{title}\n{body}".strip()
        sem_hits = await _ssn(user_id, sem_query, limit=3, threshold=0.90, is_task=is_task)
        if sem_hits:
            best_score, best_note = sem_hits[0]
            return {
                "success": False,
                "requires_confirmation": True,
                "similar_note": {"id": best_note.id, "title": best_note.title},
                "error": f"A {item_label} with very similar content exists: '{best_note.title}' (semantic similarity: {best_score:.0%}). Ask the user to confirm before creating a separate entry.",
            }

    return None