"""Профили занятий и настроений: соответствие трека контексту (context_fit)."""

from __future__ import annotations

from importlib import resources
from pathlib import Path

import numpy as np
import yaml
from numpy.typing import NDArray
from pydantic import BaseModel, model_validator

FEATURES = ("bpm", "energy", "danceability", "valence", "acousticness")


class Rule(BaseModel):
    feature: str
    min: float | None = None
    max: float | None = None
    sigma: float
    half_time: bool = False

    @model_validator(mode="after")
    def check(self) -> Rule:
        if self.feature not in FEATURES:
            raise ValueError(f"unknown feature {self.feature!r}; known: {', '.join(FEATURES)}")
        if self.min is None and self.max is None:
            raise ValueError("rule needs min or max")
        if self.sigma <= 0:
            raise ValueError("sigma must be positive")
        return self


class Context(BaseModel):
    icon: str
    rules: list[Rule]


class Contexts(BaseModel):
    activities: dict[str, Context]
    moods: dict[str, Context]


def load_contexts(path: str = "") -> Contexts:
    """contexts.yaml из пути или встроенный; ошибка в файле — ошибка запуска (Pydantic)."""
    raw = (
        Path(path).read_text("utf-8")
        if path
        else resources.files(__package__).joinpath("contexts.yaml").read_text("utf-8")
    )
    return Contexts.model_validate(yaml.safe_load(raw))


def _bell(x: NDArray[np.float64], rule: Rule) -> NDArray[np.float64]:
    lo = -np.inf if rule.min is None else rule.min
    hi = np.inf if rule.max is None else rule.max
    dist = np.where(x < lo, lo - x, np.where(x > hi, x - hi, 0.0))
    return np.asarray(np.exp(-0.5 * (dist / rule.sigma) ** 2))


def fit(features: dict[str, NDArray[np.float64]], rules: list[Rule]) -> NDArray[np.float64]:
    """Произведение «колоколов» по правилам; NaN (признака нет) — нейтрально (1).
    half_time: для темпа засчитывается и половинный (бег под 85 BPM — тот же шаг, что 170)."""
    n = len(next(iter(features.values())))
    out = np.ones(n)
    for rule in rules:
        x = features[rule.feature]
        known = ~np.isnan(x)
        b = _bell(np.nan_to_num(x), rule)
        if rule.half_time:
            b = np.maximum(b, _bell(np.nan_to_num(x) * 2, rule))
        out *= np.where(known, b, 1.0)
    return out