import assert from "node:assert/strict";
import test from "node:test";

import { applyProbeOutcome } from "../src/lib/rectification-agentic/core/apply-probe-outcome.ts";
import {
  answersFromEvidence,
  applyAnswerToState,
  buildInferenceState,
} from "../src/lib/rectification-agentic/core/build-state.ts";
import { applyChoiceWithoutEvidence } from "../src/lib/rectification-agentic/v9/inference-adapter.ts";
import { HOLDOUT_MESSAGE_PREFIX } from "../src/lib/rectification-agentic/v9/choice-card.ts";
import { existsSync, readFileSync } from "node:fs";
import { fileURLToPath } from "node:url";
import { clusterEquivalentCandidates } from "../src/lib/rectification-agentic/core/cluster-candidates.ts";
import { evaluateConvergence } from "../src/lib/rectification-agentic/core/convergence-evaluator.ts";
import { isDuplicateProbe } from "../src/lib/rectification-agentic/core/duplicate-probes.ts";
import { entropyFromScores } from "../src/lib/rectification-agentic/core/entropy.ts";
import { selectHighestGainProbe } from "../src/lib/rectification-agentic/core/select-probe.ts";
import { holdoutEventIds, splitHoldoutEvents } from "../src/lib/rectification-agentic/core/split-holdout.ts";
import type { ConflictProbe, InferenceCandidate, ProbeAnswer } from "../src/lib/rectification-agentic/core/types.ts";

function probe(input: {
  id: string;
  domain?: string;
  year?: number;
  gain: number;
  yesSupports: readonly string[];
  yesConflicts: readonly string[];
  split?: string;
}): ConflictProbe {
  return {
    id: input.id,
    semantic_key: `${input.domain ?? "career"}.${input.year ?? 2019}`,
    candidate_split_hash: input.split ?? `${input.yesSupports.join(",")}|${input.yesConflicts.join(",")}`,
    domain: input.domain ?? "career",
    year: input.year ?? 2019,
    question: "是否发生",
    candidate_ids: [...input.yesSupports, ...input.yesConflicts],
    expected_outcomes: [
      { answer_class: "yes", supports: input.yesSupports, conflicts: input.yesConflicts },
      { answer_class: "no", supports: input.yesConflicts, conflicts: input.yesSupports },
      { answer_class: "unsure", supports: [], conflicts: [] },
    ],
    information_gain: input.gain,
    source: "dasha_boundary",
  };
}

function candidates(scores: Readonly<Record<string, number>>): InferenceCandidate[] {
  return Object.entries(scores).map(([id, score], index) => ({
    id,
    time: id,
    cluster_range: [id, id] as const,
    prior_score: 10,
    posterior_score: score,
    probability: score,
    status: "active" as const,
    rank: index + 1,
    strong_conflict_count: 0,
  }));
}

test("an informative answer lowers entropy and cannot revive an eliminated candidate", () => {
  const conflict = probe({
    id: "p1",
    gain: 0.3,
    yesSupports: ["05:00"],
    yesConflicts: ["05:10"],
  });
  const before = { "04:50": 10, "05:00": 10, "05:10": 10 };
  const first = applyProbeOutcome(before, conflict, "yes");
  assert.equal(first.kind, "informative");
  assert.ok(entropyFromScores(first.scores) < entropyFromScores(before));
  assert.ok(first.eliminated_ids.includes("05:10"));
  const next = buildInferenceState({
    range_start: "04:50",
    range_end: "05:10",
    candidates: [
      { id: "04:50", time: "04:50", relative_support: 10 },
      { id: "05:00", time: "05:00", relative_support: 10 },
      { id: "05:10", time: "05:10", relative_support: 10 },
    ],
    events: [
      { id: "e1", domain: "education", year: 2016, precision: "month" },
      { id: "e2", domain: "career", year: 2019, precision: "year" },
      { id: "e3", domain: "relationship", year: 2021, precision: "year" },
      { id: "e4", domain: "family", year: 2023, precision: "year" },
    ],
    probes: [conflict],
    answered_probes: [{
      probe_id: "p1",
      semantic_key: conflict.semantic_key,
      candidate_split_hash: conflict.candidate_split_hash,
      answer_class: "no",
      classified_from: "choice",
    }],
  });
  assert.equal(next.candidates.find((item) => item.id === "05:00")?.status, "eliminated");
  const revived = applyAnswerToState(next, "p1", "yes");
  assert.equal(revived.candidates.find((item) => item.id === "05:00")?.status, "eliminated");
});

test("an unsure answer is low-information and the next probe cannot reuse the same split", () => {
  const first = probe({
    id: "p-split",
    domain: "relationship",
    year: 2019,
    gain: 0.4,
    yesSupports: ["05:00"],
    yesConflicts: ["05:10"],
    split: "05:00|05:10",
  });
  const second = probe({
    id: "p-repeat",
    domain: "relationship",
    year: 2019,
    gain: 0.5,
    yesSupports: ["05:00"],
    yesConflicts: ["05:10"],
    split: "05:00|05:10",
  });
  const third = probe({
    id: "p-other",
    domain: "career",
    year: 2022,
    gain: 0.2,
    yesSupports: ["04:50"],
    yesConflicts: ["05:10"],
    split: "04:50|05:10",
  });
  const applied = applyProbeOutcome({ "05:00": 10, "05:10": 10 }, first, "unsure");
  assert.equal(applied.kind, "low_information");
  assert.deepEqual(applied.scores, { "05:00": 10, "05:10": 10 });
  const asked: ProbeAnswer[] = [{
    probe_id: first.id,
    semantic_key: first.semantic_key,
    candidate_split_hash: first.candidate_split_hash,
    answer_class: "unsure",
    classified_from: "choice",
  }];
  assert.equal(isDuplicateProbe(second, asked), true);
  assert.equal(selectHighestGainProbe([first, second, third], asked)?.id, "p-other");
});

test("max rounds is not success and equivalent minutes return a range", () => {
  const clustered = clusterEquivalentCandidates([
    { id: "a", time: "04:58", score: 12 },
    { id: "b", time: "05:00", score: 12 },
    { id: "c", time: "05:01", score: 12 },
  ]);
  assert.equal(clustered.length, 1);
  assert.equal(clustered[0]?.range_start, "04:58");
  assert.equal(clustered[0]?.range_end, "05:01");
  const state = buildInferenceState({
    range_start: "04:58",
    range_end: "05:04",
    candidates: [
      { id: "a", time: "04:58", relative_support: 12 },
      { id: "b", time: "05:00", relative_support: 12 },
      { id: "c", time: "05:01", relative_support: 12 },
    ],
    events: [
      { id: "e1", domain: "education", year: 2016, precision: "month" },
      { id: "e2", domain: "career", year: 2019, precision: "year" },
      { id: "e3", domain: "relationship", year: 2021, precision: "year" },
      { id: "e4", domain: "family", year: 2023, precision: "year" },
    ],
    probes: [],
  });
  assert.equal(state.result_status, "credible_range");
  assert.deepEqual(state.credible_range, ["04:58", "05:01"]);
  const exhausted = evaluateConvergence({
    candidates: candidates({ a: 0.45, b: 0.35, c: 0.2 }).map((item, index) => ({
      ...item,
      probability: [0.45, 0.35, 0.2][index] ?? 0,
    })),
    events: splitHoldoutEvents([
      { id: "e1", domain: "education", year: 2016, precision: "month" },
      { id: "e2", domain: "career", year: 2019, precision: "year" },
      { id: "e3", domain: "relationship", year: 2021, precision: "year" },
      { id: "e4", domain: "family", year: 2023, precision: "year" },
      { id: "e5", domain: "health", year: 2018, precision: "year" },
    ]),
    probes: [probe({ id: "open", gain: 0.3, yesSupports: ["a"], yesConflicts: ["b"] })],
    answered_probes: [],
    rounds: [
      {
        round: 1,
        phase: "discrimination",
        probe_id: "r1",
        scores_before: {},
        scores_after: {},
        entropy_before: 1,
        entropy_after: 0.9,
        eliminated_ids: [],
        winner_id: "a",
        kind: "informative",
      },
      {
        round: 2,
        phase: "discrimination",
        probe_id: "r2",
        scores_before: {},
        scores_after: {},
        entropy_before: 0.9,
        entropy_after: 0.8,
        eliminated_ids: [],
        winner_id: "a",
        kind: "informative",
      },
    ],
    credible_range: null,
    max_rounds: 2,
  });
  assert.equal(exhausted.result_status, "max_rounds_reached");
  assert.equal(exhausted.converged, false);
});

test("holdout events stay out of training and a winner must stay stable for two rounds", () => {
  const events = splitHoldoutEvents([
    { id: "edu", domain: "education", year: 2016, precision: "month" },
    { id: "job", domain: "career", year: 2019, precision: "year" },
    { id: "love", domain: "relationship", year: 2021, precision: "year" },
    { id: "home", domain: "relocation", year: 2023, precision: "day" },
    { id: "health", domain: "health", year: 2018, precision: "year" },
  ]);
  assert.equal(holdoutEventIds(events).size, 1);
  assert.equal(events.filter((item) => item.usage === "training").length, 4);
  const oneRound = evaluateConvergence({
    candidates: [
      { ...candidates({ "05:00": 12 })[0]!, probability: 0.8, posterior_score: 12 },
      { ...candidates({ "05:10": 4 })[0]!, id: "05:10", time: "05:10", probability: 0.2, posterior_score: 4 },
    ],
    events,
    probes: [],
    answered_probes: [],
    rounds: [{
      round: 1,
      phase: "discrimination",
      probe_id: "p",
      scores_before: {},
      scores_after: {},
      entropy_before: 1,
      entropy_after: 0.4,
      eliminated_ids: [],
      winner_id: "05:00",
      kind: "informative",
    }],
    credible_range: null,
  });
  assert.equal(oneRound.converged, false);
  const twoRounds = evaluateConvergence({
    candidates: [
      {
        id: "05:00",
        time: "05:00",
        cluster_range: ["05:00", "05:00"],
        prior_score: 8,
        posterior_score: 14,
        probability: 0.82,
        status: "active",
        rank: 1,
        strong_conflict_count: 0,
      },
      {
        id: "05:10",
        time: "05:10",
        cluster_range: ["05:10", "05:10"],
        prior_score: 8,
        posterior_score: 4,
        probability: 0.18,
        status: "active",
        rank: 2,
        strong_conflict_count: 0,
      },
    ],
    events,
    probes: [],
    answered_probes: [],
    rounds: [
      {
        round: 1,
        phase: "discrimination",
        probe_id: "p1",
        scores_before: {},
        scores_after: {},
        entropy_before: 1,
        entropy_after: 0.5,
        eliminated_ids: [],
        winner_id: "05:00",
        kind: "informative",
      },
      {
        round: 2,
        phase: "discrimination",
        probe_id: "p2",
        scores_before: {},
        scores_after: {},
        entropy_before: 0.5,
        entropy_after: 0.3,
        eliminated_ids: [],
        winner_id: "05:00",
        kind: "informative",
      },
    ],
    credible_range: null,
  });
  assert.equal(twoRounds.converged, true);
  assert.equal(twoRounds.result_status, "converged");
  const matching = answersFromEvidence(
    [probe({ id: "p-job", domain: "career", year: 2019, gain: 0.2, yesSupports: ["05:00"], yesConflicts: ["05:10"] })],
    [{ id: "job", domain: "career", year: 2019, precision: "year" }],
  );
  assert.equal(matching[0]?.classified_from, "evidence");
});

test("C without new evidence updates the posterior immediately and D only marks the split asked", () => {
  const conflict = probe({
    id: "p-cd",
    domain: "career",
    year: 2019,
    gain: 0.4,
    yesSupports: ["05:00"],
    yesConflicts: ["05:10"],
    split: "05:00|05:10",
  });
  const state = buildInferenceState({
    range_start: "04:50",
    range_end: "05:10",
    candidates: [
      { id: "05:00", time: "05:00", relative_support: 10 },
      { id: "05:10", time: "05:10", relative_support: 10 },
    ],
    events: [
      { id: "e1", domain: "education", year: 2016, precision: "month" },
      { id: "e2", domain: "career", year: 2018, precision: "year" },
      { id: "e3", domain: "relationship", year: 2021, precision: "year" },
      { id: "e4", domain: "family", year: 2023, precision: "year" },
    ],
    probes: [conflict],
  });
  const denied = applyChoiceWithoutEvidence(state, {
    choiceKey: "C",
    status: "declined",
    schema: { choice: { prompt: "2019 年前后有没有入职或职责加重？" }, semantic_key: conflict.semantic_key },
  });
  assert.equal(denied.applied, true);
  assert.equal(denied.answerClass, "no");
  assert.equal(denied.state.candidates.find((item) => item.id === "05:00")?.status, "eliminated");
  assert.ok(denied.state.entropy < state.entropy);
  assert.equal(denied.state.answered_probes.some((item) => item.semantic_key === conflict.semantic_key), true);

  const unsure = applyChoiceWithoutEvidence(state, {
    choiceKey: "D",
    status: "skipped",
    userMessage: "D. 不记得 / 不确定",
    schema: { choice: { prompt: "2019 年前后有没有入职或职责加重？" }, semantic_key: conflict.semantic_key },
  });
  assert.equal(unsure.applied, true);
  assert.equal(unsure.answerClass, "unsure");
  assert.deepEqual(
    unsure.state.candidates.map((item) => item.posterior_score),
    state.candidates.map((item) => item.posterior_score),
  );
  assert.equal(selectHighestGainProbe([conflict, probe({
    id: "p-other",
    domain: "relationship",
    year: 2021,
    gain: 0.2,
    yesSupports: ["05:00"],
    yesConflicts: ["05:10"],
    split: "05:00|2021",
  })], unsure.state.answered_probes)?.id, "p-other");
});

test("holdout and collection declines do not write a probe answer", () => {
  const conflict = probe({
    id: "p-holdout",
    gain: 0.3,
    yesSupports: ["05:00"],
    yesConflicts: ["05:10"],
  });
  const state = buildInferenceState({
    range_start: "04:50",
    range_end: "05:10",
    candidates: [
      { id: "05:00", time: "05:00", relative_support: 10 },
      { id: "05:10", time: "05:10", relative_support: 10 },
    ],
    events: [
      { id: "e1", domain: "education", year: 2016, precision: "month" },
      { id: "e2", domain: "career", year: 2019, precision: "year" },
      { id: "e3", domain: "relationship", year: 2021, precision: "year" },
      { id: "e4", domain: "family", year: 2023, precision: "year" },
    ],
    probes: [conflict],
  });
  const holdout = applyChoiceWithoutEvidence(state, {
    choiceKey: "C",
    userMessage: `${HOLDOUT_MESSAGE_PREFIX}：C. 没有明显发生`,
    schema: { choice: { prompt: "盘外核对" }, scoring: false },
    questionId: "relatives:family_event:holdout",
  });
  assert.equal(holdout.reason, "holdout");
  assert.equal(holdout.state.answered_probes.length, state.answered_probes.length);

  const collection = applyChoiceWithoutEvidence(state, {
    status: "declined",
    schema: { required: ["year"] },
  });
  assert.equal(collection.reason, "no_choice");
});

test("choice answers without new evidence patch inference_state in place instead of the candidate cache", () => {
  const migration = readFileSync(
    fileURLToPath(new URL("../supabase/migrations/20260823020000_rectification_inference_choice_write.sql", import.meta.url)),
    "utf8",
  );
  assert.match(migration, /create or replace function public\.patch_agentic_rectification_inference_state\(/);
  assert.match(migration, /jsonb_set\(v_result\.decision_receipt, '\{inference_state\}', p_inference_state, true\)/);
  assert.doesNotMatch(migration, /persist_agentic_rectification_candidate_v2/);
  assert.equal(
    existsSync(new URL("../db/migrations/20260823020000_rectification_inference_choice_write.sql", import.meta.url)),
    false,
    "business migration must not be copied into frontend/db/migrations (BUG-127/BUG-144)",
  );
});