"""Summarize completed run artifacts without fitting or changing a policy."""

import collections
import csv
import json
from pathlib import Path

import numpy as np

root = Path(__file__).resolve().parents[1]
reports = root / "reports"
primary = {}
for split in ["development", "final", "ood"]:
    folder = reports / "runs" / f"{split}-primary"
    primary[split] = json.loads((folder / "summary.json").read_text())
manifest = json.loads((reports / "runs/final-primary/manifest.json").read_text())
metadata = manifest["model"]
hardware = json.loads((reports / "hardware.json").read_text())
lines = [
    "# Seal Cell: measured zero-shot evaluation",
    "",
    "This release implements the local simulator, bounded supervisor, three main policies, "
    "two control/ablation policies, evaluation CLI and Gradio dashboard. "
    "The results below are actual local CPU inference on synthetic episodes. "
    "No threshold, label, schema or policy was tuned on the final split.",
    "",
    f"Checkpoint: `{metadata['model_id']}@{metadata['revision']}`. Device/dtype: CPU / float32. "
    f"Exact input limit: {metadata['input_limit']} tokens including schema. "
    f"Measured model construction/weight load: {metadata['load_ms']:.0f} ms (Python imports and warm-up excluded).",
    "",
    f"Host: {hardware.get('cpu', hardware['machine'])}, "
    f"{hardware.get('logical_cpus', 'unknown')} logical CPUs, "
    f"{hardware.get('memory_bytes', 0) / 2**30:.0f} GiB RAM, "
    f"{hardware['torch_threads']} PyTorch CPU threads, Python {hardware['python']}. "
    f"Versions: {', '.join(f'{k} {v}' for k, v in metadata['versions'].items())}. "
    "Wall-clock measurements were taken on an interactive workstation, not an isolated benchmark host.",
    "",
    "## Held-out physical outcomes",
    "",
    "1,000 paired scenario seeds per policy; recipe C and a distinct inspection-text family. "
    "The 13 fault presets have fixed magnitudes with bounded observation/quality noise. "
    "This measures variability across seeds, not broad process robustness.",
    "",
    "| Policy | Automated batches | Recovery on recoverable cases (95% CI) | Bad / released pouches | Good / simulated hour | Review |",
    "|---|---:|---:|---:|---:|---:|",
]
for name, m in primary["final"]["policies"].items():
    ci = m["recovery_ci95"]
    lines.append(
        f"| {name} | {m['automation_rate']:.1%} | {m['recovery_rate_recoverable']:.1%} ({ci[0]:.1%}–{ci[1]:.1%}) | {m['bad_released']} / {m['released_denominator']} | {m['good_per_simulated_hour']:.1f} | {m['review_rate']:.1%} |"
    )
lines += [
    "",
    "Recovery means a completed batch with no bad pouch. Automation counts completed batches. "
    "Intervals use 1,000 episode-bootstrap replicates, never independent-pouch sampling. "
    "The hold policy has zero coverage and throughput; its defect rate is undefined, not a perfect score.",
    "",
    "## Proposals, gates and latency",
    "",
    "| Policy | Invalid proposal / gate rejection | Model errors | Scrap | Mean corrections | Warm decision p50 / p95 ms |",
    "|---|---:|---:|---:|---:|---:|",
]
for name, m in primary["final"]["policies"].items():
    latency = m["warm_latency_ms"]
    lines.append(
        f"| {name} | {m['gate_rejection_rate']:.1%} | {m['model_errors']} | {m['scrap']} | {m['mean_corrections']:.2f} | {latency['p50']:.2f} / {latency['p95']:.2f} |"
    )
lines += [
    "",
    "Every invalid top proposal ends in HOLD, so the invalid-proposal and gate-rejection rates coincide. "
    "Warm decision latency includes serialization, budget checks, inference and policy mapping; rules "
    "use the same timed decision boundary. Cold construction and warm-up are excluded. "
    "The simulator does not advance while inference is running.",
    "",
]
counts = collections.defaultdict(collections.Counter)
with (reports / "runs/final-primary/episodes.csv").open() as f:
    for row in csv.DictReader(f):
        if row["reason"]:
            counts[row["policy"]][row["reason"]] += 1
for name in ["hybrid", "direct"]:
    lines.append(
        f"**{name} terminal reasons:** "
        + "; ".join(f"{reason} ({n} episodes)" for reason, n in counts[name].most_common())
        + "."
    )
trimmed = collections.Counter()
inference = collections.defaultdict(list)
invalid_mass = []
with (reports / "runs/final-primary/decisions.jsonl").open() as f:
    for line in f:
        row = json.loads(line)
        record = row["proposal"]["model"]
        if record:
            trimmed[row["policy"]] += bool(record.get("removed_oldest_history"))
            if not record.get("error"):
                inference[row["policy"]].append(record["inference_ms"])
        if row["invalid_action_probability"] is not None:
            invalid_mass.append(row["invalid_action_probability"])
lines += [
    "",
    "Successful model calls, inference-only p50/p95: "
    + "; ".join(
        f"{name} {np.quantile(values, 0.5):.1f}/{np.quantile(values, 0.95):.1f} ms"
        for name, values in inference.items()
    )
    + ".",
    f"Calls with explicitly logged oldest-history removal: {dict(trimmed)}. "
    f"Mean probability mass assigned to invalid direct actions: {np.mean(invalid_mass):.1%}.",
    "",
    "## Fixture diagnosis and action agreement",
    "",
    "Five original three-head probes were rerun verbatim. The selected labels match the "
    "playground observations; full precision local scores, raw logits and timings are preserved. "
    "The original hosted scores were rounded and its checkpoint revision was not exposed.",
    "",
    "| Probe | Local issue | Local action | Reference-action agreement |",
    "|---|---|---|---|",
]
probes = [json.loads(line) for line in (reports / "local-probes.jsonl").read_text().splitlines()]
for r in probes:
    h = r["heads"]
    lines.append(
        f"| {r['fixture_id']} | {h['suspected_issue']['selected_label']} | {h['next_action']['selected_label']} | {r['action_correct']} |"
    )
fixtures = [
    json.loads(line) for line in (reports / "development-fixtures.jsonl").read_text().splitlines()
]
lines += [
    "",
    "The 24 frozen development fixtures are 12 matched raw/derived pairs, including missing/stale "
    "sensors, negation, resolved history, repeated ineffective correction and contradictory text. "
    "Expected diagnoses can be sets. These are development checks, not an estimate of held-out accuracy.",
    "",
]
for condition in ["raw", "derived"]:
    selected = [r for r in fixtures if r["fixture_id"].endswith(condition)]
    lines.append(
        f"- {condition}: {sum(r['diagnosis_correct'] is True for r in selected)}/{len(selected)} diagnosis agreements; {sum(bool(r['error']) for r in selected)} model errors."
    )
lines += [
    "",
    "The exported development error-versus-coverage curve is descriptive. The 0.5 issue/evidence "
    "thresholds were frozen prototype operating points. No temperature scaling was fitted and no "
    "score is advertised as calibrated confidence. Separate calibrated policy selection remains future work.",
    "",
    "## Development robustness and OOD",
    "",
    "| Development condition | Episodes / policy | Hybrid automation | Direct automation | Model errors (hybrid / direct) |",
    "|---|---:|---:|---:|---:|",
]
for condition in [
    "primary",
    "raw",
    "three-heads",
    "no-maintenance",
    "reverse-labels",
    "descriptions",
    "paraphrase",
]:
    result = json.loads((reports / f"runs/development-{condition}/summary.json").read_text())[
        "policies"
    ]
    h, d = result["hybrid"], result["direct"]
    lines.append(
        f"| {condition} | {h['episodes']} | {h['automation_rate']:.1%} | {d['automation_rate']:.1%} | {h['model_errors']} / {d['model_errors']} |"
    )
lines += [
    "",
    "The primary development run has 200 episodes; each robustness check has 26 matched development "
    "episodes. No condition was selected based on final results. Descriptions use the library-supported "
    "label-description schema; reversed order retains identical meanings. The paraphrase condition "
    "changes the introductory observation wording and is only a narrow robustness check.",
    "",
    "The separate OOD run uses 100 episodes (recipe B, reserved combined-fault and unseen-material scenarios):",
    "",
]
for name, m in primary["ood"]["policies"].items():
    lines.append(
        f"- {name}: {m['automation_rate']:.1%} automation, {m['bad_released']}/{m['released_denominator']} bad/released pouches."
    )
lines += [
    "",
    "## Interpretation and scope",
    "",
    "The competent rules controller is the stronger controller in this toy domain. "
    "These zero-shot GLiNER policies do not establish learned planning or an advantage over rules. "
    "Their zero defect release must be read together with automation and throughput. "
    "The proposed research target (90% recoverable recovery and 70% automation, with no worse "
    "defect release than rules) is not achieved by the model policies. The current balanced fault "
    "mix contains five unrecoverable presets out of thirteen, so even perfect recovery cannot "
    "reach 70% batch automation across this entire population; that research target needs a "
    "separately predefined operational scenario mix.",
    "",
    "Engineering validation covers deterministic replay, causal recovery, unrecoverable faults, "
    "observation isolation, units, stale/missing sensors, fresh sample qualification, all budgets, "
    "invalid model proposals, model failure and stale in-flight UI results. "
    "The real-model integration check verifies full distributions and overlength rejection. "
    "The dashboard was inspected in native Chrome; live Gradio endpoint smoke tests cover "
    "load/run/step/reset/pause, exported traces and two-session isolation.",
    "",
    "Known limits: fixed toy fault severities; perfect threshold-based inspection of noisy synthetic "
    "quality; narrow lexical baseline; no trained TF-IDF/tabular baseline, no fine-tuning, no fitted "
    "calibration; no hard cancellation of a stuck native inference call. "
    "All coefficients are synthetic, and confidence intervals do not imply industrial assurance.",
    "",
    "## Reproduce and inspect",
    "",
    "An earlier engineering run was stopped during an input-parity audit: the renderer omitted "
    "material thickness, recipe targets, approved alternate profiles and sample freshness context. "
    "That partial run is archived under `runs/superseded-input-v1/` and excluded here. "
    "The delivered v2 evaluation includes those fields; labels and thresholds were unchanged.",
    "",
    "- [README and uv commands](../README.md)",
    "- [Separate MPS acceleration study](mps.md)",
    "- [Final full report](runs/final-primary/report.md), [summary](runs/final-primary/summary.json), [manifest](runs/final-primary/manifest.json)",
    "- [Development report](runs/development-primary/report.md)",
    "- [OOD report](runs/ood-primary/report.md)",
    "- [Local probes](local-probes.jsonl), [development fixture outputs](development-fixtures.jsonl), [coverage curve](development-fixtures.jsonl.coverage.json)",
    "- Each run directory also contains complete `decisions.jsonl` and `episodes.csv`.",
    "",
]
(reports / "zero-shot.md").write_text("\n".join(lines))
print(reports / "zero-shot.md")