"""Per-recipe scoring with the 5 weighted signals from the spec."""
from __future__ import annotations

from datetime import date
from decimal import Decimal
from typing import Dict, Iterable, List, Optional
from uuid import UUID

from app.services.planner.config import PlannerConfig
from app.services.planner.types import RecipeCost, ScoredRecipe


def time_bonus(total_minutes: int, config: PlannerConfig) -> float:
    if total_minutes <= config.time_ideal_minutes:
        return 1.0
    if total_minutes >= config.time_full_minutes:
        return 0.0
    span = config.time_full_minutes - config.time_ideal_minutes
    over = total_minutes - config.time_ideal_minutes
    return max(0.0, min(1.0, 1.0 - over / span))


def recency_bonus(
    last_cooked: Optional[date],
    today: date,
    config: PlannerConfig,
) -> float:
    if last_cooked is None:
        return 1.0
    weeks_ago = (today - last_cooked).days / 7
    if weeks_ago >= config.recency_full_weeks:
        return 1.0
    # Below recency_weeks the recipe wouldn't be in the feasible set, so we treat
    # exactly recency_weeks as score 0 and recency_full_weeks as score 1.
    if weeks_ago <= config.recency_weeks:
        return 0.0
    span = config.recency_full_weeks - config.recency_weeks
    over = weeks_ago - config.recency_weeks
    return max(0.0, min(1.0, over / span))


def normalize_savings(values: List[Decimal]) -> List[float]:
    if not values:
        return []
    floats = [float(v) for v in values]
    lo, hi = min(floats), max(floats)
    if hi == lo:
        return [0.0] * len(floats)
    return [(v - lo) / (hi - lo) for v in floats]


def score_recipes(
    *,
    recipes: Iterable[dict],
    recipe_costs: Dict[UUID, RecipeCost],
    last_cooked_at: Dict[UUID, date],
    config: PlannerConfig,
    today: date,
) -> List[ScoredRecipe]:
    """Returns recipes scored DESC. Caller passes only the feasible set."""
    materialized = list(recipes)
    if not materialized:
        return []

    savings = [recipe_costs[r["id"]].total_savings for r in materialized]
    norm_savings = normalize_savings(savings)

    out: List[ScoredRecipe] = []
    for r, ns in zip(materialized, norm_savings):
        rid = r["id"]
        if isinstance(rid, str):
            rid = UUID(rid)
        cost = recipe_costs[rid]
        total_min = int(r.get("prep_time_minutes") or 0) + int(r.get("cook_time_minutes") or 0)
        tb = time_bonus(total_min, config)
        rb = recency_bonus(last_cooked_at.get(rid), today, config)

        components = {
            "savings_normalized": ns,
            "sale_coverage_pct": cost.sale_coverage_pct,
            "pantry_hit_pct": cost.pantry_hit_pct,
            "time_bonus": tb,
            "recency_bonus": rb,
            "savings_dollars": float(cost.total_savings),
        }

        score = (
            config.w_savings * ns
            + config.w_coverage * cost.sale_coverage_pct
            + config.w_pantry * cost.pantry_hit_pct
            + config.w_time * tb
            + config.w_recency * rb
        )

        cuisine_tags = r.get("cuisine_tags") or []
        primary_cuisine = cuisine_tags[0] if cuisine_tags else None

        out.append(
            ScoredRecipe(
                recipe_id=rid,
                score=score,
                components=components,
                cost=cost,
                protein=r.get("protein_type"),
                cuisine=primary_cuisine,
            )
        )

    out.sort(key=lambda s: s.score, reverse=True)
    return out