// Source-row → EvidenceRecord transforms. Promoted from
// auditor/schemas/distillation/realdata.test.ts PROBES array. Each
// transform is pure: no I/O, no model calls, no clock reads (caller
// supplies recorded_at). Deterministic by construction so re-running
// the materializer on identical input produces byte-identical output.
//
// Adding a new source: append a TransformDef. Order in TRANSFORMS[]
// has no effect (each runs against its own source_file).

import type { EvidenceRecord, ModelRole } from "../../auditor/schemas/distillation/evidence_record";
import { EVIDENCE_SCHEMA_VERSION } from "../../auditor/schemas/distillation/evidence_record";
import { canonicalSha256 } from "../../auditor/schemas/distillation/types";

export interface TransformInput {
  row: any;
  line_offset: number;
  source_file_relpath: string;   // relative to repo root
  recorded_at: string;           // ISO 8601 — caller's "now"
  sig_hash: string;              // canonical sha256 of orderedKeys(row), pre-computed by caller
}

export interface TransformDef {
  source_file_relpath: string;   // relative to repo root, e.g. "data/_kb/distilled_facts.jsonl"
  transform: (input: TransformInput) => Partial<EvidenceRecord> | null;
}

function provenance(input: TransformInput): EvidenceRecord["provenance"] {
  return {
    source_file: input.source_file_relpath,
    line_offset: input.line_offset,
    sig_hash: input.sig_hash,
    recorded_at: input.recorded_at,
  };
}

const TIME_TO_MS = (iso: string): number => new Date(iso).getTime();

export const TRANSFORMS: TransformDef[] = [
  // ── Tier 1: validated 100% in Phase 1 ─────────────────────────────
  {
    source_file_relpath: "data/_kb/distilled_facts.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: String(row.run_id ?? `distilled_facts:${line_offset}`),
      task_id: String(row.source_label ?? `distilled_facts:${line_offset}`),
      timestamp: row.created_at,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_name: row.extractor,
      model_role: "extractor" as ModelRole,
      model_provider: "ollama",
      text: row.text,
    }),
  },
  {
    source_file_relpath: "data/_kb/distilled_procedures.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: String(row.run_id ?? `distilled_procedures:${line_offset}`),
      task_id: String(row.source_label ?? `distilled_procedures:${line_offset}`),
      timestamp: row.created_at,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_name: row.extractor,
      model_role: "extractor" as ModelRole,
      model_provider: "ollama",
      text: row.text,
    }),
  },
  {
    source_file_relpath: "data/_kb/distilled_config_hints.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: String(row.run_id ?? `distilled_config_hints:${line_offset}`),
      task_id: String(row.source_label ?? `distilled_config_hints:${line_offset}`),
      timestamp: row.created_at,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_name: row.extractor,
      model_role: "extractor" as ModelRole,
      model_provider: "ollama",
      text: row.text,
    }),
  },
  {
    source_file_relpath: "data/_kb/contract_analyses.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: `contract_analysis:${row.permit_id}:${TIME_TO_MS(row.ts)}`,
      task_id: `permit:${row.permit_id}`,
      timestamp: row.ts,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_role: "executor" as ModelRole,
      retrieved_context: {
        matrix_corpora: Object.keys(row.matrix_corpora ?? {}),
        matrix_hits: row.matrix_hits,
      },
      observer_notes: row.observer_notes ? [row.observer_notes].flat().filter(Boolean) : undefined,
      observer_verdict: row.observer_verdict,
      observer_confidence: row.observer_conf,
      success_markers: row.ok ? ["matrix_hits_above_threshold"] : undefined,
      failure_markers: !row.ok || row.observer_verdict === "reject" ? ["observer_rejected"] : undefined,
      cost_usd: typeof row.cost === "number" ? row.cost / 1_000_000 : undefined,
      latency_ms: row.duration_ms,
      text: row.analysis,
      metadata: typeof row.contractor === "string" && row.contractor.length > 0
        ? { contractor: row.contractor }
        : undefined,
    }),
  },
  {
    source_file_relpath: "data/_kb/mode_experiments.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: `mode_exec:${TIME_TO_MS(row.ts)}:${row.file_path ?? line_offset}`,
      task_id: row.task_class,
      timestamp: row.ts,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_name: row.model,
      model_role: "executor" as ModelRole,
      model_provider: row.model?.includes("/") ? "openrouter" : "ollama_cloud",
      retrieved_context: {
        matrix_corpora: row.sources?.matrix_corpus,
        matrix_chunks_kept: row.sources?.matrix_chunks_kept,
        matrix_chunks_dropped: row.sources?.matrix_chunks_dropped,
        pathway_fingerprints_seen: row.sources?.bug_fingerprints_count,
      },
      latency_ms: row.latency_ms,
      text: row.response,
      source_files: row.file_path ? [row.file_path] : undefined,
    }),
  },
  {
    source_file_relpath: "data/_kb/scrum_reviews.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: `scrum:${TIME_TO_MS(row.reviewed_at)}:${row.file}`,
      task_id: `scrum_review:${row.file}`,
      timestamp: row.reviewed_at,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_name: row.accepted_model,
      model_role: "executor" as ModelRole,
      source_files: [row.file],
      success_markers: row.accepted_on_attempt ? [`accepted_on_attempt_${row.accepted_on_attempt}`] : undefined,
      text: row.suggestions_preview,
    }),
  },
  {
    source_file_relpath: "data/_kb/observer_escalations.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: `obs_esc:${TIME_TO_MS(row.ts)}:${row.sig_hash}`,
      task_id: `observer_escalation:${row.cluster_endpoint ?? "?"}`,
      timestamp: row.ts,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_role: "reviewer" as ModelRole,
      prompt_tokens: row.prompt_tokens,
      completion_tokens: row.completion_tokens,
      text: row.analysis,
    }),
  },
  {
    source_file_relpath: "data/_kb/audit_facts.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: `audit_facts:${row.head_sha}:${line_offset}`,
      task_id: `pr:${row.pr_number}`,
      timestamp: row.extracted_at,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_name: row.extractor,
      model_role: "extractor" as ModelRole,
      text: JSON.stringify({
        facts: row.facts?.length ?? 0,
        entities: row.entities?.length ?? 0,
        relationships: row.relationships?.length ?? 0,
      }),
    }),
  },

  // ── Tier 2: untested streams that still belong in EvidenceRecord ──
  {
    // auto_apply.jsonl is metadata-only (no text payload). Keep the row
    // in evidence so success/failure markers contribute to scoring,
    // even though the text field is empty.
    source_file_relpath: "data/_kb/auto_apply.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => {
      // Deterministic fallback: use the source-file's recorded_at when
      // the row itself lacks a ts. Wall-clock (new Date()) leaked here
      // pre-2026-04-27 — broke bit-identical reproducibility on rows
      // that historically wrote without a ts field.
      const ts: string = row.ts ?? recorded_at;
      const action = String(row.action ?? "unknown");
      const success = action === "committed";
      const reverted = action.includes("reverted");
      return {
        run_id: `auto_apply:${TIME_TO_MS(ts)}:${row.file ?? line_offset}`,
        task_id: `auto_apply:${row.file ?? "?"}`,
        timestamp: ts,
        schema_version: EVIDENCE_SCHEMA_VERSION,
        provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
        model_role: "applier" as ModelRole,
        source_files: row.file ? [row.file] : undefined,
        success_markers: success ? ["committed"] : undefined,
        failure_markers: reverted ? [action] : undefined,
      };
    },
  },
  {
    source_file_relpath: "data/_kb/observer_reviews.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: `obs_rev:${TIME_TO_MS(row.ts ?? row.reviewed_at)}:${row.file ?? line_offset}`,
      task_id: row.file ? `observer_review:${row.file}` : `observer_review:${line_offset}`,
      timestamp: row.ts ?? row.reviewed_at,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_role: "reviewer" as ModelRole,
      observer_verdict: row.verdict,
      observer_confidence: row.confidence,
      observer_notes: row.notes ? [row.notes].flat().filter(Boolean) : undefined,
      text: row.notes ?? row.review ?? undefined,
    }),
  },
  {
    // 2026-04-26 correction: data/_kb/audits.jsonl is the auditor's
    // per-FINDING stream (recon misnamed it "PR verdicts"). Schema:
    //   {embedding, evidence, finding_id, phase, resolution, severity, topic, ts}
    // The actual per-PR verdicts live in data/_auditor/verdicts/*.json,
    // not in this JSONL. So we score by severity here: info/low →
    // accepted (audit found minor issue), medium → partially_accepted,
    // high/critical → rejected (real problem in the audited code).
    source_file_relpath: "data/_kb/audits.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => {
      const sev = String(row.severity ?? "unknown").toLowerCase();
      const minor = sev === "info" || sev === "low";
      const blocking = sev === "high" || sev === "critical";
      return {
        run_id: `audit_finding:${row.finding_id ?? line_offset}`,
        task_id: row.phase ? `phase:${row.phase}` : "audit_finding",
        timestamp: row.ts ?? new Date().toISOString(),
        schema_version: EVIDENCE_SCHEMA_VERSION,
        provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
        model_role: "reviewer" as ModelRole,
        success_markers: minor ? [`audit_severity_${sev}`] : undefined,
        failure_markers: blocking ? [`audit_severity_${sev}`] : (sev === "medium" ? ["audit_severity_medium"] : undefined),
        text: typeof row.evidence === "string" ? row.evidence : (row.resolution ?? ""),
      };
    },
  },
  {
    source_file_relpath: "data/_kb/outcomes.jsonl",
    transform: ({ row, line_offset, source_file_relpath, recorded_at, sig_hash }) => ({
      run_id: `outcome:${row.run_id ?? line_offset}`,
      task_id: row.sig_hash ? `outcome_sig:${row.sig_hash}` : `outcome:${line_offset}`,
      timestamp: row.created_at,
      schema_version: EVIDENCE_SCHEMA_VERSION,
      provenance: provenance({ row, line_offset, source_file_relpath, recorded_at, sig_hash }),
      model_role: "executor" as ModelRole,
      latency_ms: typeof row.elapsed_secs === "number" ? Math.round(row.elapsed_secs * 1000) : undefined,
      success_markers: typeof row.ok_events === "number" && typeof row.total_events === "number"
        ? (row.ok_events === row.total_events && row.total_events > 0 ? ["all_events_ok"] : undefined)
        : undefined,
      validation_results: typeof row.total_gap_signals === "number"
        ? { gap_signals: row.total_gap_signals, citation_count: row.total_citations }
        : undefined,
    }),
  },
];

export function transformByPath(source_file_relpath: string): TransformDef | undefined {
  return TRANSFORMS.find(t => t.source_file_relpath === source_file_relpath);
}

// Re-export for materializer convenience.
export { canonicalSha256 };