"""Tests du solveur (suggestions, jamais d'affectation automatique)."""

import datetime as dt

from app.solver.engine import solve
from app.solver.models import SolverInput, SolverShift, SolverVolunteer
from app.validation.models import AvailabilityWindow, ShiftInfo


def _dt(day: int, hour: int, minute: int = 0) -> dt.datetime:
    return dt.datetime(2026, 7, day, hour, minute)


def _shift(
    sid: str = "s1",
    *,
    position_id: str = "p1",
    start: dt.datetime | None = None,
    end: dt.datetime | None = None,
    min_staff=1,
    ideal_staff=1,
    already_assigned=0,
) -> SolverShift:
    return SolverShift(
        id=sid,
        position_id=position_id,
        starts_at=start or _dt(10, 14),
        ends_at=end or _dt(10, 18),
        min_staff=min_staff,
        ideal_staff=ideal_staff,
        already_assigned=already_assigned,
    )


def _volunteer(
    vid: str,
    *,
    availability=(),
    wished_position_ids=frozenset(),
    existing_shifts=(),
    existing_minutes=0,
) -> SolverVolunteer:
    return SolverVolunteer(
        id=vid,
        availability=tuple(availability),
        wished_position_ids=frozenset(wished_position_ids),
        existing_shifts=tuple(existing_shifts),
        existing_minutes=existing_minutes,
    )


def _full_day(day: int) -> AvailabilityWindow:
    return AvailabilityWindow(dt.date(2026, 7, day), dt.time(6, 0), dt.time(4, 0))


def _by_shift(solution, shift_id: str) -> tuple[str, ...]:
    for s in solution.suggestions:
        if s.shift_id == shift_id:
            return s.volunteer_ids
    return ()


def test_no_variables_returns_empty_solution():
    data = SolverInput(shifts=(), volunteers=())
    assert solve(data).suggestions == ()


def test_fully_staffed_shift_gets_no_suggestion():
    shift = _shift(ideal_staff=1, already_assigned=1)
    volunteer = _volunteer("v1", availability=[_full_day(10)])
    solution = solve(SolverInput(shifts=(shift,), volunteers=(volunteer,)))
    assert solution.suggestions == ()


def test_picks_the_only_free_volunteer_over_a_busy_one():
    shift = _shift()
    busy = _volunteer(
        "busy",
        availability=[_full_day(10)],
        existing_shifts=[ShiftInfo(id="e", starts_at=_dt(10, 15), ends_at=_dt(10, 19))],
    )
    free = _volunteer("free", availability=[_full_day(10)])
    solution = solve(SolverInput(shifts=(shift,), volunteers=(busy, free)))
    assert _by_shift(solution, "s1") == ("free",)


def test_does_not_exceed_ideal_staff_cap():
    shift = _shift(ideal_staff=2, already_assigned=1)  # il ne manque qu'une place
    a = _volunteer("a", availability=[_full_day(10)])
    b = _volunteer("b", availability=[_full_day(10)])
    solution = solve(SolverInput(shifts=(shift,), volunteers=(a, b)))
    assert len(_by_shift(solution, "s1")) == 1


def test_prefers_volunteer_with_matching_position_wish():
    shift = _shift(position_id="regie")
    plain = _volunteer("plain", availability=[_full_day(10)])
    wisher = _volunteer(
        "wisher", availability=[_full_day(10)], wished_position_ids={"regie"}
    )
    solution = solve(SolverInput(shifts=(shift,), volunteers=(plain, wisher)))
    assert _by_shift(solution, "s1") == ("wisher",)


def test_prefers_fully_available_volunteer_over_partially_available():
    shift = _shift(start=_dt(10, 14), end=_dt(10, 18))
    full = _volunteer("full", availability=[_full_day(10)])
    partial = _volunteer(
        "partial",
        availability=[AvailabilityWindow(dt.date(2026, 7, 10), dt.time(14, 0), dt.time(16, 0))],
    )
    solution = solve(SolverInput(shifts=(shift,), volunteers=(full, partial)))
    assert _by_shift(solution, "s1") == ("full",)


def test_ranks_both_candidates_when_two_slots_are_open():
    shift = _shift(ideal_staff=2)
    full = _volunteer("full", availability=[_full_day(10)])
    partial = _volunteer(
        "partial",
        availability=[AvailabilityWindow(dt.date(2026, 7, 10), dt.time(14, 0), dt.time(16, 0))],
    )
    solution = solve(SolverInput(shifts=(shift,), volunteers=(full, partial)))
    assert _by_shift(solution, "s1") == ("full", "partial")


def test_never_double_books_a_volunteer_across_two_new_suggestions():
    # Deux créneaux qui se chevauchent, un seul bénévole disponible pour les
    # deux : le solveur ne le propose qu'une fois.
    shift_a = _shift("a", start=_dt(10, 14), end=_dt(10, 18))
    shift_b = _shift("b", start=_dt(10, 16), end=_dt(10, 20))
    only = _volunteer("only", availability=[_full_day(10)])
    solution = solve(SolverInput(shifts=(shift_a, shift_b), volunteers=(only,)))
    picked_a = _by_shift(solution, "a")
    picked_b = _by_shift(solution, "b")
    assert len(picked_a) + len(picked_b) == 1


def test_avoids_volunteer_who_would_exceed_eight_hours_that_day():
    shift = _shift(start=_dt(10, 14), end=_dt(10, 18))  # 4 h
    tired = _volunteer(
        "tired",
        availability=[_full_day(10)],
        existing_shifts=[ShiftInfo(id="e", starts_at=_dt(10, 6), ends_at=_dt(10, 12))],  # 6 h déjà
    )
    fresh = _volunteer("fresh", availability=[_full_day(10)])
    solution = solve(SolverInput(shifts=(shift,), volunteers=(tired, fresh)))
    assert _by_shift(solution, "s1") == ("fresh",)