from __future__ import annotations

from typing import Any

DEFAULT_MET = 5.0

MET_BY_KEYWORD: list[tuple[str, float]] = [
    ("триатлон", 10.0),
    ("марафон", 9.8),
    ("бег", 9.8),
    ("running", 9.8),
    ("run", 9.0),
    ("плаван", 8.0),
    ("swim", 8.0),
    ("велосипед", 7.5),
    ("cycling", 7.5),
    ("вел", 7.5),
    ("hiit", 8.0),
    ("кроссфит", 8.0),
    ("силов", 6.0),
    ("strength", 6.0),
    ("зал", 5.5),
    ("gym", 5.5),
    ("йога", 3.0),
    ("yoga", 3.0),
    ("ходьб", 3.5),
    ("walk", 3.5),
    ("прогул", 3.5),
]


def infer_met(workout: dict[str, Any]) -> float | None:
    explicit = workout.get("met")
    if explicit is not None:
        return float(explicit)

    activity_type = str(workout.get("activity_type") or "").lower()
    title = str(workout.get("title") or "").lower()
    notes = str(workout.get("notes") or "").lower()
    haystack = f"{activity_type} {title} {notes}"

    for keyword, met in MET_BY_KEYWORD:
        if keyword in haystack:
            return met
    return None


def estimate_workout_active_kcal(workout: dict[str, Any], *, weight_kg: float) -> float:
    active = workout.get("active_calories")
    if active is not None:
        return round(float(active), 1)

    duration = workout.get("duration_min")
    if not duration:
        return 0.0

    met = infer_met(workout)
    if met is None:
        return 0.0

    hours = float(duration) / 60.0
    return round(met * weight_kg * hours, 1)


def workouts_kcal_total(workouts: list[dict[str, Any]], *, weight_kg: float) -> float:
    if not workouts:
        return 0.0
    return round(sum(estimate_workout_active_kcal(w, weight_kg=weight_kg) for w in workouts), 1)