import assert from "node:assert/strict";
import { after, before, test } from "node:test";
import { readFile } from "node:fs/promises";
import { createServer } from "vite";

let server;
let fetchM48TRiskQualityResult;

const resultId = `m48q-native-risk-quality-lab-${"a".repeat(64)}`;

before(async () => {
  server = await createServer({
    appType: "custom",
    logLevel: "silent",
    server: { middlewareMode: true },
  });
  ({ fetchM48TRiskQualityResult } = await server.ssrLoadModule(
    "/src/core/laboratory/m48tRiskQuality.ts",
  ));
});

after(async () => server?.close());

function response(value) {
  return { ok: true, status: 200, json: async () => value };
}

function payload() {
  return {
    schema_version: "missioncore.m48q-native-risk-quality-view/v1",
    variant: "native-risk-review",
    result_id: resultId,
    created_at_utc: "2026-08-26T10:00:00Z",
    status: "complete-review-ready-quality-not-adjudicated",
    access: "read-only",
    ground_truth: false,
    source: {
      source_id: "RAVNOVES00",
      frame_count: 4489,
      raster_width: 800,
      raster_height: 600,
      geometric_resampling: false,
      rectification: false,
      warp: false,
    },
    configuration: {
      candidate: {
        provider_id: "triton-rf-detr-large-coco-native-kb4-risk-fp16-shadow/v0",
        model_id: "rf_detr_large_native_kb4:1",
        preprocess_id: "raw-kb4-uint8-fused-mask-rgb-pad8-imagenet-trt/v0",
        minimum_score: 0.25,
      },
    },
    method: {
      schema_version: "missioncore.laboratory-method/v1",
      completeness: "complete",
      execution_class: "hybrid",
      pipeline_id: "m48q-native-raw-fisheye-risk-case-review/v1",
      components: [{
        kind: "model",
        name: "RF-DETR-L native KB4 TensorRT",
        version: "rf_detr_large_native_kb4:1",
        role: "risk proposals",
        identity_sha256: "b".repeat(64),
      }],
    },
    execution: {
      effective_world_state_fps: 11.84338,
      world_state_completion_p95_ms: 47.940779,
      detector_total_p95_ms: 21.19895,
      gpu_utilization_p95_percent: 53,
      gpu_memory_used_maximum_mib: 9718,
      additional_inference_passes: 0,
    },
    metrics: {
      selection: {
        case_count: 24,
        minimum_sequence_separation: 12,
        selected_bucket_coverage: { person: 22, animal: 3 },
        selected_class_counts: { person: 44, dog: 3 },
      },
      runtime: {
        delivery_ratio: 1,
        integrated_runtime_gate_passed: true,
        operating_target_gate_passed: true,
      },
      native_tensor_parity: {
        risk_detection_precision: 0.9887,
        risk_detection_recall: 0.9831,
        matched_mean_iou: 0.9882,
      },
    },
    acceptance: {
      review_ready: true,
      integrated_runtime_gate_passed: true,
      independent_quality_evaluated: false,
      semantic_candidate_accepted: false,
    },
    review: {
      source_raster: { width: 800, height: 600 },
      overlay: { client_rendered: true, toggleable: true },
      cases: Array.from({ length: 24 }, (_, index) => {
        const caseId = String(index * 20 + 12).padStart(6, "0");
        return {
          case_id: caseId,
          sequence: Number(caseId),
          frame_id: `frame-${caseId}`,
          evidence_time_ns: 35_000_000_000 + index * 1_000_000,
          image_url: `/review/${caseId}.jpg`,
          media_type: "image/jpeg",
          width: 800,
          height: 600,
          byte_length: 1000 + index,
          sha256: String(index.toString(16)).padStart(64, "0"),
          geometric_resampling: false,
          selection_buckets: [index % 2 ? "person" : "animal"],
          comparison: {
            native_detection_count: 1,
            legacy_704_detection_count: 1,
            matched_detection_count_iou_at_least_0_5: 1,
          },
          proposals: [{
            proposal_id: `proposal-${index}-0`,
            class_name: index % 2 ? "person" : "dog",
            risk_family: index % 2 ? "person" : "animal",
            score: 0.75,
            box_xyxy: [10, 20, 100, 200],
          }],
        };
      }),
    },
    limitations: ["No independent route truth."],
  };
}

test("M4.8Q parser accepts only the native 800x600 no-resampling review contract", async () => {
  const result = await fetchM48TRiskQualityResult(resultId, {
    fetcher: async () => response(payload()),
  });

  assert.equal(result.variant, "native-risk-review");
  assert.equal(result.review.cases.length, 24);
  assert.equal(result.review.cases[0].width, 800);
  assert.equal(result.review.cases[0].proposals[0].className, "dog");
  assert.equal(result.execution.additionalInferencePasses, 0);
  assert.equal(result.acceptance.independentQualityEvaluated, false);
});

test("M4.8Q parser fails closed if raw-fisheye geometry is resampled", async () => {
  const invalid = payload();
  invalid.source.geometric_resampling = true;

  await assert.rejects(
    fetchM48TRiskQualityResult(resultId, {
      fetcher: async () => response(invalid),
    }),
    /source resampling/,
  );
});

test("M4.8Q reuses the canonical image scene and viewer overlay toggle", async () => {
  const source = await readFile(
    new URL("../src/workspaces/laboratory/M48TRiskQualityVisual.tsx", import.meta.url),
    "utf8",
  );

  assert.match(source, /<LaboratoryEvidenceViewer/);
  assert.match(source, /<RecordedEvidenceImageScene/);
  assert.match(source, /name=\{boxesVisible \? "eye-off" : "eye"\}/);
  assert.match(source, /raw KB4 800×600 · resampling NO/);
  assert.doesNotMatch(source, /<canvas/);
});