"""Which lc0 self-play runs to draw from, and in what proportion.

Tiers follow generation quality: the networks producing the games get weaker as
you go down, so the lower tiers contribute noisier, more exploratory positions.
Keeping a minority of them is deliberate — a corpus drawn only from the strongest
run is narrow in exactly the way this corpus is meant not to be.

Availability was surveyed against storage.lczero.org on 2026-08-25 and differs
from the run list usually quoted:

  * test70, test77, test76, test50, test20 and test10 are not hosted at all.
  * test78 and test90 exist but every tar is a 10 KB placeholder.
  * test80's retained window now starts 2024-04-01; earlier tars were pruned.
  * test80 stopped producing on 2025-09-24. test91 is the live run.
  * test71 is the Chess960 run — measured 30-36% Chess960 against at most 1.2%
    elsewhere — so it is excluded rather than tiered. test71_5 measured 0%.

Named substitutes for the missing runs stay in era and strength order:
test79 and test75 stand in for T70/T78 in the mid tier, test71_5 for T50.
"""

from __future__ import annotations

from dataclasses import dataclass, field


@dataclass(frozen=True)
class Run:
    name: str
    # Restrict to a slice of the run's date-sorted tars, as "late test60" and
    # "early test60" belong to different tiers.
    first: float = 0.0
    last: float = 1.0
    weight: float = 1.0


@dataclass(frozen=True)
class Tier:
    name: str
    share: float
    runs: tuple[Run, ...]
    note: str = ""


TIERS: tuple[Tier, ...] = (
    Tier(
        "strong",
        0.60,
        (Run("test80", weight=2.0), Run("test91", weight=1.0)),
        "Mature run1 plus the live run2. Core modern play.",
    ),
    Tier(
        "mid",
        0.22,
        (Run("test79"), Run("test75"), Run("test60", first=0.60)),
        "Late-T60 through T79: transitional styles, sound but more varied.",
    ),
    Tier(
        "low",
        0.13,
        (Run("test40"), Run("test60", last=0.35), Run("test71_5", weight=0.5)),
        "2019-2020 nets. Messier tactics and unusual structures.",
    ),
    Tier(
        "early",
        0.05,
        (Run("test30"), Run("run3", weight=0.5)),
        "2018-2019 semi-initial play. Kept small; maximum noise.",
    ),
)

EXCLUDED = {
    "test71": "Chess960 run; 30-36% of positions are Chess960",
    "test78": "all tars are 10 KB placeholders",
    "test90": "all tars are 10 KB placeholders",
}


def quotas(total: int) -> dict[str, int]:
    """Positions per tier, with the rounding residue given to the largest."""

    assigned = {tier.name: int(total * tier.share) for tier in TIERS}
    largest = max(TIERS, key=lambda t: t.share).name
    assigned[largest] += total - sum(assigned.values())
    return assigned


def run_quotas(tier: Tier, tier_total: int) -> dict[str, int]:
    """Positions per (run, slice) inside one tier, split by run weight."""

    total_weight = sum(r.weight for r in tier.runs)
    out: dict[str, int] = {}
    for run in tier.runs:
        key = f"{run.name}:{run.first:.2f}-{run.last:.2f}"
        out[key] = int(tier_total * run.weight / total_weight)
    residue = tier_total - sum(out.values())
    if out:
        out[next(iter(out))] += residue
    return out


assert abs(sum(t.share for t in TIERS) - 1.0) < 1e-9