#!/usr/bin/env python3
"""8-variant analysis (POST-only EXCLUDED): focus on PRE vs POST+UP across tiers.

Variants:
  - Cluster A MQ-PRE, Cluster A MQ-POST+UP
  - Cluster B MQ-PRE, Cluster B MQ-POST+UP, Cluster B HQ-PRE, Cluster B HQ-POST+UP, Cluster B LQ-PRE, Cluster B LQ-POST+UP

Tables produced:
  Table 1  — 8-variant headline accuracy matrix (15 metrics + 4 aggregates)
  Table 2  — Tier ordering at PRE and at POST+UP, with HQ-vs-LQ spread
  Table 3  — Repair effect (POST+UP - PRE) by tier, with seed-variance floor
  Table 4  — Cross-cluster seed-variance noise floor (Cluster A MQ vs Cluster B MQ, PRE and POST+UP only)
  Table 5  — Paloma-11 BPB by variant + distributional split
"""
from __future__ import annotations
import json, glob, math, statistics

BASE = './eval_results'

VARIANTS = [
    # (lookup_label, display_name, cluster, tier, treatment)
    ('clusterA_mq_new',                          'A-MQ-PRE',   'A', 'mq', 'pre'),
    ('clusterA_mq_new_repaired_upsampled',       'A-MQ-UP',    'A', 'mq', 'ups'),
    ('clusterB_mq_new',                      'B-MQ-PRE',   'B', 'mq', 'pre'),
    ('clusterB_mq_new_repaired_upsampled',   'B-MQ-UP',    'B', 'mq', 'ups'),
    ('clusterB_hq_new',                      'B-HQ-PRE',   'B', 'hq', 'pre'),
    ('clusterB_hq_new_repaired_upsampled',   'B-HQ-UP',    'B', 'hq', 'ups'),
    ('clusterB_lq_new',                      'B-LQ-PRE',   'B', 'lq', 'pre'),
    ('clusterB_lq_new_repaired_upsampled',   'B-LQ-UP',    'B', 'lq', 'ups'),
]

METRIC_KEYS = {
    'hellaswag':'acc_norm,none','piqa':'acc_norm,none','winogrande':'acc,none',
    'commonsense_qa':'acc,none','social_iqa':'acc,none','openbookqa':'acc_norm,none',
    'sciq':'acc_norm,none','arc_easy':'acc_norm,none','arc_challenge':'acc_norm,none',
    'logiqa':'acc,none','pubmedqa':'acc,none','boolq':'acc,none','race':'acc,none',
    'squadv2':'best_f1,none','coqa':'f1,none','copa':'acc,none','cb':'acc,none',
    'rte':'acc,none','anli_r1':'acc,none','anli_r2':'acc,none','anli_r3':'acc,none',
    'truthfulqa_mc2':'acc,none','triviaqa':'exact_match,remove_whitespace',
    'nq_open':'exact_match,remove_whitespace','lambada_openai':'acc,none',
}
SCALE = {'squadv2':0.01}

HEADLINE = [
    ('hellaswag','HellaSwag','CS'),
    ('piqa','PIQA','CS'),
    ('winogrande','WinoGrande','CS'),
    ('commonsense_qa','CSQA','CS'),
    ('social_iqa','SIQA','CS'),
    ('arc_easy','ARC-E','KR'),
    ('arc_challenge','ARC-C','KR'),
    ('openbookqa','OpenBookQA','KR'),
    ('sciq','SciQ','KR'),
    ('mmlu_mean','MMLU-57','KR'),
    ('pubmedqa','PubMedQA','KR'),
    ('boolq','BoolQ','RC'),
    ('race','RACE','RC'),
    ('lambada_openai','LAMBADA','LM'),
    ('blimp_mean','BLiMP-67','L'),
]

def load(suite, label):
    f = glob.glob(f'{BASE}/{suite}/{label}_results/results_*.json')
    return json.load(open(f[0]))['results'] if f else {}

def task_value(results, task):
    if task == 'mmlu_mean':
        accs = [v.get('acc,none') for k,v in results.items() if k.startswith('mmlu_') and k != 'mmlu']
        accs = [a for a in accs if isinstance(a,(int,float))]
        return statistics.mean(accs) if accs else None
    if task == 'blimp_mean':
        accs = [v.get('acc,none') for k,v in results.items() if k.startswith('blimp_') and k != 'blimp']
        accs = [a for a in accs if isinstance(a,(int,float))]
        return statistics.mean(accs) if accs else None
    key = METRIC_KEYS.get(task)
    if key is None or task not in results: return None
    v = results[task].get(key)
    return v * SCALE.get(task,1.0) if isinstance(v,(int,float)) else None

# Load
data = {}
for lookup, *_ in VARIANTS:
    data[lookup] = {'prose': load('prose', lookup), 'paloma': load('paloma', lookup)}

def fineweb_agg(results):
    vs = [task_value(results,t) for t in ['commonsense_qa','hellaswag','openbookqa','piqa','social_iqa','winogrande']]
    vs = [v for v in vs if v is not None]
    arc_e = task_value(results,'arc_easy'); arc_c = task_value(results,'arc_challenge')
    if arc_e is not None and arc_c is not None: vs.append((arc_e+arc_c)/2)
    mmlu = task_value(results,'mmlu_mean')
    if mmlu is not None: vs.append(mmlu)
    return statistics.mean(vs) if vs else None

def dolma8_agg(results):
    vs = [task_value(results,t) for t in ['hellaswag','piqa','winogrande','openbookqa','arc_easy','arc_challenge','sciq','boolq']]
    vs = [v for v in vs if v is not None]
    return statistics.mean(vs) if vs else None

def knowledge_agg(results):
    vs = [task_value(results,t) for t in ['arc_challenge','openbookqa']]
    for t in ['mmlu_mean','triviaqa','nq_open']:
        v = task_value(results,t)
        if v is not None: vs.append(v)
    vs = [v for v in vs if v is not None]
    return statistics.mean(vs) if vs else None

# ============================================================================
# TABLE 1 — 8-variant accuracy matrix
# ============================================================================
print("="*150)
print(" Table 1 — Per-variant zero-shot accuracy (%) — POST-only EXCLUDED (PRE vs POST+UP only)")
print(" Qwen3-1.7B, 1 epoch (≈ 34.4 B tokens), BF16, all eval on Cluster A H100 + NeMo 26.04 + lm-eval 0.4.12")
print("="*150)
hdr = f"  {'metric':<14}"
for _, disp, *_ in VARIANTS:
    hdr += f"  {disp:>10}"
print(hdr)
print('-'*150)
for mid, disp, grp in HEADLINE:
    line = f"  {disp:<14}"
    for lookup, *_ in VARIANTS:
        v = task_value(data[lookup]['prose'], mid)
        line += f"  {v*100:>10.2f}" if v is not None else f"  {'--':>10}"
    print(line)
print('-'*150)
for agg_name, agg_fn in [('FineWeb-Agg-8', fineweb_agg), ('Dolma-Hdln-8', dolma8_agg), ('Knowledge-Agg', knowledge_agg)]:
    line = f"  {agg_name:<14}"
    for lookup, *_ in VARIANTS:
        v = agg_fn(data[lookup]['prose'])
        line += f"  {v*100:>10.2f}" if v is not None else f"  {'--':>10}"
    print(line)
# Paloma BPB
line = f"  {'Paloma-BPB ↓':<14}"
for lookup, *_ in VARIANTS:
    palo = data[lookup]['paloma']
    bpbs = [palo[t]['bits_per_byte,none'] for t in palo if 'bits_per_byte,none' in palo[t]]
    v = statistics.mean(bpbs)
    line += f"  {v:>10.4f}"
print(line)

# ============================================================================
# TABLE 2 — Tier ordering at PRE and at POST+UP
# ============================================================================
print()
print("="*150)
print(" Table 2 — Tier ordering at each treatment (Cluster B cluster; A-MQ is cross-cluster anchor)")
print(" Spread = HQ − LQ (positive = expected HQ-best ordering; negative = inverted)")
print("="*150)
hdr = f"  {'metric':<14}  {'treat':<8}  {'LQ':>7}  {'MQ':>7}  {'HQ':>7}  {'spread':>8}  {'order':<12}"
print(hdr)
print('-'*100)
for mid, disp, _ in HEADLINE:
    for trt, sfx in [('PRE','new'), ('POST+UP','new_repaired_upsampled')]:
        lq = task_value(data[f'clusterB_lq_{sfx}']['prose'], mid)
        mq = task_value(data[f'clusterB_mq_{sfx}']['prose'], mid)
        hq = task_value(data[f'clusterB_hq_{sfx}']['prose'], mid)
        if lq is None or mq is None or hq is None: continue
        spread = (hq - lq) * 100
        # Determine ordering
        triple = [('LQ',lq),('MQ',mq),('HQ',hq)]
        triple_sorted = sorted(triple, key=lambda r:-r[1])
        order = '>'.join(t[0] for t in triple_sorted)
        line = f"  {disp:<14}  {trt:<8}  {lq*100:>7.2f}  {mq*100:>7.2f}  {hq*100:>7.2f}  {spread:>+7.2f}  {order:<12}"
        print(line)
    print()

# ============================================================================
# TABLE 3 — Repair effect (POST+UP − PRE) by tier  +  seed-variance noise floor
# ============================================================================
print("="*150)
print(" Table 3 — Repair effect Δ (POST+UP − PRE) by tier  +  cross-cluster seed-variance floor")
print(" Floor is |Cluster B MQ - Cluster A MQ| paired delta at the same treatment.")
print(" S/N column = |best tier delta| / floor.")
print("="*150)
hdr = f"  {'metric':<14}  {'Δ LQ':>9}  {'Δ MQ-A':>9}  {'Δ MQ-B':>9}  {'Δ HQ':>9}    {'seed-floor':>11}  {'best-S/N':>10}"
print(hdr)
print('-'*120)
def delta(lab_pre, lab_ups, mid):
    pv = task_value(data[lab_pre]['prose'], mid)
    uv = task_value(data[lab_ups]['prose'], mid)
    return (uv-pv)*100 if (pv is not None and uv is not None) else None

floor_results = {}
for mid, disp, _ in HEADLINE:
    d_lq = delta('clusterB_lq_new','clusterB_lq_new_repaired_upsampled', mid)
    d_mq_a = delta('clusterA_mq_new','clusterA_mq_new_repaired_upsampled', mid)
    d_mq_b = delta('clusterB_mq_new','clusterB_mq_new_repaired_upsampled', mid)
    d_hq = delta('clusterB_hq_new','clusterB_hq_new_repaired_upsampled', mid)
    # Seed-variance floor = median of |Cluster A MQ vs Cluster B MQ| at PRE and POST+UP
    a_pre = task_value(data['clusterA_mq_new']['prose'], mid)
    b_pre = task_value(data['clusterB_mq_new']['prose'], mid)
    a_ups = task_value(data['clusterA_mq_new_repaired_upsampled']['prose'], mid)
    b_ups = task_value(data['clusterB_mq_new_repaired_upsampled']['prose'], mid)
    floor_vals = []
    if a_pre is not None and b_pre is not None:
        floor_vals.append(abs(b_pre - a_pre) * 100)
    if a_ups is not None and b_ups is not None:
        floor_vals.append(abs(b_ups - a_ups) * 100)
    floor = statistics.median(floor_vals) if floor_vals else 0.0
    floor_results[mid] = floor
    # Best tier signal
    deltas_pp = [d for d in [d_lq, d_mq_a, d_mq_b, d_hq] if d is not None]
    best = max(deltas_pp, key=abs) if deltas_pp else 0.0
    sn = abs(best)/floor if floor > 0.001 else float('inf')
    sn_str = f"{sn:>6.1f}×" if sn != float('inf') else "   ∞"
    def fmt(d):
        return f"{d:>+8.2f}pp" if d is not None else f"  {'--':>7}"
    print(f"  {disp:<14}  {fmt(d_lq):>9}  {fmt(d_mq_a):>9}  {fmt(d_mq_b):>9}  {fmt(d_hq):>9}    {floor:>9.3f}pp  {sn_str:>10}")

# Aggregate deltas
print('-'*120)
def agg_delta(agg_fn, pre, ups):
    p = agg_fn(data[pre]['prose']); u = agg_fn(data[ups]['prose'])
    return (u-p)*100 if (p is not None and u is not None) else None
for name, agg_fn in [('FineWeb-Agg-8', fineweb_agg), ('Dolma-Hdln-8', dolma8_agg), ('Knowledge-Agg', knowledge_agg)]:
    d_lq = agg_delta(agg_fn,'clusterB_lq_new','clusterB_lq_new_repaired_upsampled')
    d_mq_a = agg_delta(agg_fn,'clusterA_mq_new','clusterA_mq_new_repaired_upsampled')
    d_mq_b = agg_delta(agg_fn,'clusterB_mq_new','clusterB_mq_new_repaired_upsampled')
    d_hq = agg_delta(agg_fn,'clusterB_hq_new','clusterB_hq_new_repaired_upsampled')
    def fmt(d):
        return f"{d:>+8.2f}pp" if d is not None else f"  {'--':>7}"
    print(f"  {name:<14}  {fmt(d_lq):>9}  {fmt(d_mq_a):>9}  {fmt(d_mq_b):>9}  {fmt(d_hq):>9}")

# Paloma BPB Δ
def palo_mean_delta(pre, ups):
    p = statistics.mean([data[pre]['paloma'][c]['bits_per_byte,none'] for c in data[pre]['paloma']])
    u = statistics.mean([data[ups]['paloma'][c]['bits_per_byte,none'] for c in data[ups]['paloma']])
    return u - p
d_lq = palo_mean_delta('clusterB_lq_new','clusterB_lq_new_repaired_upsampled')
d_mq_a = palo_mean_delta('clusterA_mq_new','clusterA_mq_new_repaired_upsampled')
d_mq_b = palo_mean_delta('clusterB_mq_new','clusterB_mq_new_repaired_upsampled')
d_hq = palo_mean_delta('clusterB_hq_new','clusterB_hq_new_repaired_upsampled')
print(f"  {'Paloma-BPB Δ ↓':<14}  {d_lq:>+8.4f}  {d_mq_a:>+8.4f}  {d_mq_b:>+8.4f}  {d_hq:>+8.4f}     (lower = improvement)")

# ============================================================================
# TABLE 4 — Cross-cluster seed-variance noise floor (PRE and POST+UP only)
# ============================================================================
print()
print("="*150)
print(" Table 4 — Seed-variance noise floor (Cluster A MQ vs Cluster B MQ, same treatment)")
print(" Excludes POST-only (Cluster B MQ-POST was anomalous: BoolQ +7.6pp, PubMedQA +15.2pp)")
print("="*150)
print(f"  {'metric':<14}  {'|Δ| PRE':>10}  {'|Δ| POST+UP':>14}  {'median':>9}")
print('-'*60)
floors = []
for mid, disp, _ in HEADLINE:
    mp = task_value(data['clusterA_mq_new']['prose'], mid)
    hp = task_value(data['clusterB_mq_new']['prose'], mid)
    mu = task_value(data['clusterA_mq_new_repaired_upsampled']['prose'], mid)
    hu = task_value(data['clusterB_mq_new_repaired_upsampled']['prose'], mid)
    vs = []
    if mp is not None and hp is not None: vs.append(abs(hp-mp)*100)
    if mu is not None and hu is not None: vs.append(abs(hu-mu)*100)
    med = statistics.median(vs) if vs else 0
    print(f"  {disp:<14}  {vs[0]:>10.3f}pp  {vs[1]:>13.3f}pp  {med:>8.3f}pp")
    floors.extend(vs)
print('-'*60)
floors.sort()
print(f"\n  Overall noise floor (15 metrics × 2 treatments = 30 deltas):")
print(f"    median = {statistics.median(floors):.3f} pp")
print(f"    p75    = {floors[int(len(floors)*0.75)]:.3f} pp")
print(f"    p95    = {floors[int(len(floors)*0.95)]:.3f} pp")
print(f"    max    = {floors[-1]:.3f} pp")

# Paloma seed floor
palo_floors = []
for trt_label, mlab, hlab in [('PRE','clusterA_mq_new','clusterB_mq_new'),('POST+UP','clusterA_mq_new_repaired_upsampled','clusterB_mq_new_repaired_upsampled')]:
    for c in data[mlab]['paloma']:
        m = data[mlab]['paloma'][c]['bits_per_byte,none']
        h = data[hlab]['paloma'][c]['bits_per_byte,none']
        palo_floors.append(abs(h-m))
palo_floors.sort()
print(f"\n  Paloma seed floor (22 deltas):  median = {statistics.median(palo_floors):.5f}  max = {palo_floors[-1]:.5f}")

# ============================================================================
# TABLE 5 — Paloma per-corpus 8-variant matrix + distributional split
# ============================================================================
print()
print("="*150)
print(" Table 5 — Paloma-11 bits-per-byte per corpus (lower=better)")
print("="*150)
corpora = sorted(data['clusterA_mq_new']['paloma'].keys())
hdr = f"  {'corpus':<32}"
for _, disp, *_ in VARIANTS: hdr += f"  {disp:>10}"
print(hdr)
print('-'*150)
for c in corpora:
    name = data['clusterA_mq_new']['paloma'][c].get('alias', c)
    line = f"  {name:<32}"
    for lookup, *_ in VARIANTS:
        v = data[lookup]['paloma'][c]['bits_per_byte,none']
        line += f"  {v:>10.4f}"
    print(line)
print('-'*150)
line = f"  {'Paloma-BPB-11 mean ↓':<32}"
for lookup, *_ in VARIANTS:
    v = statistics.mean([data[lookup]['paloma'][c]['bits_per_byte,none'] for c in corpora])
    line += f"  {v:>10.4f}"
print(line)

# Per-corpus repair delta by tier (POST+UP - PRE)
print()
print(" Repair Δ on Paloma BPB (POST+UP − PRE), by tier")
print(" Negative = repair improves on that corpus; positive = repair regresses")
print(" Distributional-specialization signature: web-y corpora improve, literary corpora regress")
print('-'*100)
print(f"  {'corpus':<32}  {'Δ LQ':>9}  {'Δ MQ-A':>9}  {'Δ MQ-B':>9}  {'Δ HQ':>9}")
for c in corpora:
    name = data['clusterA_mq_new']['paloma'][c].get('alias', c)
    def palo_d(pre, ups):
        return data[ups]['paloma'][c]['bits_per_byte,none'] - data[pre]['paloma'][c]['bits_per_byte,none']
    d_lq = palo_d('clusterB_lq_new','clusterB_lq_new_repaired_upsampled')
    d_mq_a = palo_d('clusterA_mq_new','clusterA_mq_new_repaired_upsampled')
    d_mq_b = palo_d('clusterB_mq_new','clusterB_mq_new_repaired_upsampled')
    d_hq = palo_d('clusterB_hq_new','clusterB_hq_new_repaired_upsampled')
    print(f"  {name:<32}  {d_lq:>+9.4f}  {d_mq_a:>+9.4f}  {d_mq_b:>+9.4f}  {d_hq:>+9.4f}")

print()
print("="*150)
print("END")
print("="*150)