"""Maps taxonomy dimensions to current inference-pipeline coverage.

Answers: which of the 12 taxonomy dimensions the current MAEST, MuQ, PaSST,
taxonomy-adapter, lyrics, rhythm, and GT-review layers produce tags for, and
how completely. Static map because the answer depends on the pipeline
architecture, not the per-track data.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import Literal

CoverageStatus = Literal["covered", "partial", "none", "expert"]


@dataclass(frozen=True)
class DimensionCoverage:
    status: CoverageStatus
    sources: list[str]
    note: str


DIMENSION_COVERAGE: dict[int, DimensionCoverage] = {
    1: DimensionCoverage(
        "covered",
        [
            "maest/genre",
            "muq-probe/genre",
            "passt-probe/genre",
            "taxonomy-adapter/genre",
        ],
        "MAEST keeps a Discogs baseline while MuQ, PaSST, and the taxonomy adapter now provide the Turkish taxonomy-facing genre families used in the track explorer.",
    ),
    2: DimensionCoverage(
        "covered",
        ["muq-probe/genre", "passt-probe/genre", "taxonomy-adapter/genre"],
        "Expanded MuQ/PaSST probes and the taxonomy adapter provide the subtype-oriented Turkish genre terms surfaced for review.",
    ),
    3: DimensionCoverage(
        "partial",
        ["groundtruth/web-research"],
        "Yöre — populated via Tier 4 web research pipeline (Wikipedia TR action=query, Discogs v2 API + release credits, MusicBrainz, YouTube → DeepSeek extraction against taxonomy controlled vocab). Full catalog (68/68 records on disk; one is 005_benim_icin_uzulme which exists as GT-only after the YouTube DMCA gap). Yöre populated on 4/68 tracks (güneydoğu 075, roman 082, urban-ankara 087, iç-anadolu 069) — the rest correctly null because the title carries no deterministic yöre cue and sources don't explicitly assert one. Largest residual gap in the taxonomy; needs Aran/Murat expert pass for rural-folk tracks where the source web doesn't disambiguate. Ground truth lives in sync_pilot/data/groundtruth/median/<track_id>.json (visible per-track on the Tracks page).",
    ),
    4: DimensionCoverage(
        "covered",
        ["librosa/beat_track", "librosa/tempogram"],
        "Tier 3: librosa.beat.beat_track for BPM + tempogram-based aksak hint for meter. "
        "Catalog distribution: 55/67 tracks 4/4, 11/67 flagged 9/8 aksak candidate, 1/67 uncertain (silent / no onsets). "
        "Aksak classification is exploratory — librosa alone cannot reliably distinguish 9/8 from 7/8 or 5/8 from a single waveform, "
        "so the heuristic confidence ceiling for non-4/4 calls is capped at 0.49. Catalog confidence distribution: "
        "0/67 above 0.7, 5/67 in [0.5, 0.7], 62/67 below 0.5 (flagged for expert spot-check). The dashboard caption reads aksak "
        "meters as '9/8 aksak candidate' rather than as definitive calls. A future Tier 3.5 pass with madmom or a dedicated "
        "aksak model could refine.",
    ),
    5: DimensionCoverage(
        "expert",
        [],
        "Makam — intentionally expert-only per taxonomy. Only Aran or a session musician should populate.",
    ),
    6: DimensionCoverage(
        "covered",
        ["muq-probe/instrument", "passt-probe/instrument", "taxonomy-adapter/instrument"],
        "Instrumentation is now grounded primarily by promoted MuQ/PaSST probe heads plus the taxonomy adapter. GT review can override these into source-verified instrumentation.",
    ),
    7: DimensionCoverage(
        "covered",
        ["muq-probe/vocal", "taxonomy-adapter/vocal", "review-sidecar/vocal"],
        "Vocal configuration and technique candidates come from MuQ probes and the taxonomy adapter, with reviewer selections persisted in the GT-review sidecar.",
    ),
    8: DimensionCoverage(
        "covered",
        ["lyrics-llm"],
        "Themes extracted from Whisper lyrics via DeepSeek against the taxonomy's 22 controlled theme terms (gurbet, aşk-acısı, kabadayı, isyan, dini, etc.). Tier 2: 19 distinct themes fire across the 67-track catalog (top: aşk-acısı 37, pişmanlık 26, ayrılık 22, aşk 22, kader 21). 59/67 tracks carry at least one theme tag; 8 tracks have no themes (short/instrumental lyrics or empty after subtitle-strip).",
    ),
    9: DimensionCoverage(
        "partial",
        ["muq-probe/mood", "review-sidecar/mood"],
        "Mood candidates are currently MuQ-probe driven and can be accepted or corrected in GT review. Coverage is useful but still needs human review because mood vocabulary is subjective.",
    ),
    10: DimensionCoverage(
        "partial",
        ["groundtruth/web-research"],
        "Era — populated via Tier 4 web research. Full catalog: 54/68 tracks have era populated (14 high + 40 medium confidence), 14 uncertain (anonymous/multi-recording titles). Era is deterministically derived from year via a post-extract correction (year < 1970 → pre-1970; 1970–1979 → 1970-1980; …; ≥2020 → 2020-plus) so internal consistency is machine-guaranteed. Catalog era distribution: 1980-1990 leads (15), then 1990-2000 (8), 2000-2010 (7), 1970-1980 (6), 2010-2020 (5), 2020-plus (1).",
    ),
    11: DimensionCoverage(
        "covered",
        ["librosa/beat_track", "librosa/rms"],
        "Tier 3: librosa BPM + RMS energy. Tempo band per taxonomy controlled vocab "
        "(yavaş <80 BPM, orta 80–120 BPM, hızlı >120 BPM). Catalog distribution: orta 45/67, hızlı 21/67, yavaş 1/67. "
        "Mean BPM 112.9 ± 21.9; one outlier above 160 (161.5 BPM on 090_ilk_ogretmen), one BPM=0 sentinel on the "
        "silent/onset-free 088_kara_sevda track. Energy bands: high 44/67, medium 21/67, low 2/67. "
        "Unblocks the description LLM to ground 'mid-tempo'/'yavaş'/'hızlı' claims on a real signal instead of "
        "hallucinating them — Tier 3 sets up the v0.7 description rerun.",
    ),
    12: DimensionCoverage(
        "partial",
        ["groundtruth/web-research"],
        "Production arrangement/context — Tier 4 uses arrangement_aesthetic (orchestral-arabesk, electric-anadolu-rock, modern-pop-production, gazino-fantezi, bare-bones-halk) and recording_context (studio, live-recording, lo-fi-cassette, broadcast).",
    ),
}


STATUS_ICON: dict[CoverageStatus, str] = {
    "covered": "✅",
    "partial": "🟡",
    "none": "⛔",
    "expert": "🔒",
}


STATUS_LABEL: dict[CoverageStatus, str] = {
    "covered": "Covered",
    "partial": "Partial",
    "none": "No inference",
    "expert": "Expert-only",
}


def summary_counts() -> dict[str, int]:
    """Quick rollup — used by the page header."""
    counts: dict[str, int] = {"covered": 0, "partial": 0, "none": 0, "expert": 0}
    for c in DIMENSION_COVERAGE.values():
        counts[c.status] += 1
    return counts