"""CLI: build annotations, batch eval, audit, serve web UI."""
  
  from __future__ import annotations
  
  import argparse
  import json

  import logging

  import shutil

  from pathlib import Path

  from typing import Any, Dict

  from config.loader import get_app_config
  
  from .datasets import audits_dir, query_builds_dir, resolve_dataset

  from .framework import SearchEvaluationFramework

  from .logging_setup import setup_eval_logging

  from .utils import ensure_dir, utc_now_iso, utc_timestamp
  from .web_app import create_web_app
  

  _cli_log = logging.getLogger("search_eval.cli")
  

  def _reset_build_artifacts(dataset_id: str) -> None:

      artifact_root = get_app_config().search_evaluation.artifact_root
      removed = []

      dataset_query_builds = query_builds_dir(artifact_root, dataset_id)
      dataset_audits = audits_dir(artifact_root, dataset_id)
      if dataset_query_builds.exists():
          shutil.rmtree(dataset_query_builds)
          removed.append(str(dataset_query_builds))
      if dataset_audits.exists():
          shutil.rmtree(dataset_audits)
          removed.append(str(dataset_audits))

      if removed:

          _cli_log.info("[build] reset dataset artifacts for %s: %s", dataset_id, ", ".join(removed))

      else:

          _cli_log.info("[build] no previous dataset artifacts to reset under %s for dataset=%s", artifact_root, dataset_id)

  def add_judge_llm_args(p: argparse.ArgumentParser) -> None:
      p.add_argument(
          "--judge-model",
          default=None,
          metavar="MODEL",

          help="Judge LLM model (default: config.yaml search_evaluation.judge_model).",

      )
      p.add_argument(
          "--enable-thinking",
          action=argparse.BooleanOptionalAction,
          default=None,

          help="enable_thinking for DashScope (default: search_evaluation.judge_enable_thinking).",

      )
      p.add_argument(
          "--dashscope-batch",
          action=argparse.BooleanOptionalAction,
          default=None,

          help="DashScope Batch File API vs sync chat (default: search_evaluation.judge_dashscope_batch).",

      )
  
  

  def add_intent_llm_args(p: argparse.ArgumentParser) -> None:
      p.add_argument(
          "--intent-model",
          default=None,
          metavar="MODEL",

          help="Query-intent LLM model before relevance judging (default: search_evaluation.intent_model).",

      )
      p.add_argument(
          "--intent-enable-thinking",
          action=argparse.BooleanOptionalAction,
          default=None,

          help="enable_thinking for intent model (default: search_evaluation.intent_enable_thinking).",

      )
  
  

  def framework_kwargs_from_args(args: argparse.Namespace) -> Dict[str, Any]:
      kw: Dict[str, Any] = {}
      if args.judge_model is not None:
          kw["judge_model"] = args.judge_model
      if args.enable_thinking is not None:
          kw["enable_thinking"] = args.enable_thinking
      if args.dashscope_batch is not None:
          kw["use_dashscope_batch"] = args.dashscope_batch

      if getattr(args, "intent_model", None) is not None:
          kw["intent_model"] = args.intent_model
      if getattr(args, "intent_enable_thinking", None) is not None:
          kw["intent_enable_thinking"] = args.intent_enable_thinking

      return kw
  
  

  def _apply_search_evaluation_cli_defaults(args: argparse.Namespace) -> None:
      """Fill None CLI defaults from ``config.yaml`` ``search_evaluation`` (via ``get_app_config()``)."""

      se = get_app_config().search_evaluation

      if getattr(args, "dataset_id", None) in (None, "") and getattr(args, "queries_file", None) in (None, ""):
          args.dataset_id = se.default_dataset_id

      if getattr(args, "tenant_id", None) in (None, ""):
          args.tenant_id = se.default_tenant_id
      if getattr(args, "queries_file", None) in (None, ""):
          args.queries_file = str(se.queries_file)
      if getattr(args, "language", None) in (None, ""):
          args.language = se.default_language
  
      if args.command == "serve":
          if getattr(args, "host", None) in (None, ""):
              args.host = se.web_host
          if getattr(args, "port", None) is None:
              args.port = se.web_port
  
      if args.command == "batch":
          if getattr(args, "top_k", None) is None:
              args.top_k = se.batch_top_k
  
      if args.command == "audit":
          if getattr(args, "top_k", None) is None:
              args.top_k = se.audit_top_k
          if getattr(args, "limit_suspicious", None) is None:
              args.limit_suspicious = se.audit_limit_suspicious
  
      if args.command == "build":
          if getattr(args, "search_depth", None) is None:
              args.search_depth = se.build_search_depth
          if getattr(args, "rerank_depth", None) is None:
              args.rerank_depth = se.build_rerank_depth
          if getattr(args, "annotate_search_top_k", None) is None:
              args.annotate_search_top_k = se.annotate_search_top_k
          if getattr(args, "annotate_rerank_top_k", None) is None:
              args.annotate_rerank_top_k = se.annotate_rerank_top_k
          if getattr(args, "search_recall_top_k", None) is None:
              args.search_recall_top_k = se.search_recall_top_k
          if getattr(args, "rerank_high_threshold", None) is None:
              args.rerank_high_threshold = se.rerank_high_threshold
          if getattr(args, "rerank_high_skip_count", None) is None:
              args.rerank_high_skip_count = se.rerank_high_skip_count
          if getattr(args, "rebuild_llm_batch_size", None) is None:
              args.rebuild_llm_batch_size = se.rebuild_llm_batch_size
          if getattr(args, "rebuild_min_batches", None) is None:
              args.rebuild_min_batches = se.rebuild_min_llm_batches
          if getattr(args, "rebuild_max_batches", None) is None:
              args.rebuild_max_batches = se.rebuild_max_llm_batches
          if getattr(args, "rebuild_irrelevant_stop_ratio", None) is None:
              args.rebuild_irrelevant_stop_ratio = se.rebuild_irrelevant_stop_ratio
          if getattr(args, "rebuild_irrel_low_combined_stop_ratio", None) is None:
              args.rebuild_irrel_low_combined_stop_ratio = se.rebuild_irrel_low_combined_stop_ratio
          if getattr(args, "rebuild_irrelevant_stop_streak", None) is None:
              args.rebuild_irrelevant_stop_streak = se.rebuild_irrelevant_stop_streak
  
  

  def _resolve_dataset_from_args(args: argparse.Namespace, *, require_enabled: bool = False):
      queries_file = getattr(args, "queries_file", None)
      query_path = Path(str(queries_file)).resolve() if queries_file not in (None, "") else None
      dataset = resolve_dataset(
          dataset_id=getattr(args, "dataset_id", None),
          query_file=query_path,
          tenant_id=getattr(args, "tenant_id", None),
          language=getattr(args, "language", None),
          require_enabled=require_enabled,
      )
      args.dataset_id = dataset.dataset_id
      args.queries_file = str(dataset.query_file)
      args.tenant_id = dataset.tenant_id
      args.language = dataset.language
      return dataset
  
  

  def build_cli_parser() -> argparse.ArgumentParser:
      parser = argparse.ArgumentParser(description="Search evaluation annotation builder and web UI")
      sub = parser.add_subparsers(dest="command", required=True)
  
      build = sub.add_parser("build", help="Build pooled annotation set for queries")

      build.add_argument(
          "--tenant-id",
          default=None,
          help="Tenant id (default: search_evaluation.default_tenant_id in config.yaml).",
      )

      build.add_argument("--dataset-id", default=None, help="Named evaluation dataset id from config.yaml.")

      build.add_argument(
          "--queries-file",
          default=None,

          help="Legacy override for query list file. Prefer --dataset-id.",

      )
      build.add_argument(
          "--search-depth",
          type=int,
          default=None,
          help="Default: search_evaluation.build_search_depth.",
      )
      build.add_argument(
          "--rerank-depth",
          type=int,
          default=None,
          help="Default: search_evaluation.build_rerank_depth.",
      )
      build.add_argument(
          "--annotate-search-top-k",
          type=int,
          default=None,
          help="Default: search_evaluation.annotate_search_top_k.",
      )
      build.add_argument(
          "--annotate-rerank-top-k",
          type=int,
          default=None,
          help="Default: search_evaluation.annotate_rerank_top_k.",
      )

      build.add_argument(
          "--search-recall-top-k",
          type=int,
          default=None,

          help="Rebuild mode only: top-K search hits enter recall pool with score 1 (default when --force-refresh-labels: 200).",

      )
      build.add_argument(
          "--rerank-high-threshold",
          type=float,
          default=None,
          help="Rebuild only: count rerank scores above this on non-pool docs (default 0.5).",
      )
      build.add_argument(
          "--rerank-high-skip-count",
          type=int,
          default=None,
          help="Rebuild only: skip query if more than this many non-pool docs have rerank score > threshold (default 1000).",
      )
      build.add_argument("--rebuild-llm-batch-size", type=int, default=None, help="Rebuild only: LLM batch size (default 50).")

      build.add_argument("--rebuild-min-batches", type=int, default=None, help="Rebuild only: min LLM batches before early stop (default 10).")

      build.add_argument("--rebuild-max-batches", type=int, default=None, help="Rebuild only: max LLM batches (default 40).")
      build.add_argument(
          "--rebuild-irrelevant-stop-ratio",
          type=float,
          default=None,

          help="Rebuild only: bad batch requires irrelevant_ratio > this (default: search_evaluation.rebuild_irrelevant_stop_ratio).",

      )
      build.add_argument(
          "--rebuild-irrel-low-combined-stop-ratio",
          type=float,
          default=None,

          help="Rebuild only: bad batch requires (irrelevant+low)/n > this (default 0.959).",

      )
      build.add_argument(
          "--rebuild-irrelevant-stop-streak",
          type=int,
          default=None,

          help="Rebuild only: consecutive bad batches (both thresholds strict >) before early stop (default 3).",

      )

      build.add_argument(
          "--language",
          default=None,
          help="Default: search_evaluation.default_language.",
      )

      build.add_argument(

          "--reset-artifacts",
          action="store_true",

          help="Delete dataset-specific query_builds/audits before starting. Shared SQLite cache is preserved.",

      )

      build.add_argument("--force-refresh-rerank", action="store_true")
      build.add_argument("--force-refresh-labels", action="store_true")

      add_judge_llm_args(build)

      add_intent_llm_args(build)

  
      batch = sub.add_parser("batch", help="Run batch evaluation against live search")

      batch.add_argument("--tenant-id", default=None, help="Default: search_evaluation.default_tenant_id.")

      batch.add_argument("--dataset-id", default=None, help="Named evaluation dataset id from config.yaml.")
      batch.add_argument("--queries-file", default=None, help="Legacy override for query list file. Prefer --dataset-id.")

      batch.add_argument("--top-k", type=int, default=None, help="Default: search_evaluation.batch_top_k.")
      batch.add_argument("--language", default=None, help="Default: search_evaluation.default_language.")

      batch.add_argument("--force-refresh-labels", action="store_true")

      add_judge_llm_args(batch)

      add_intent_llm_args(batch)

  
      audit = sub.add_parser("audit", help="Audit annotation quality for queries")

      audit.add_argument("--tenant-id", default=None, help="Default: search_evaluation.default_tenant_id.")

      audit.add_argument("--dataset-id", default=None, help="Named evaluation dataset id from config.yaml.")
      audit.add_argument("--queries-file", default=None, help="Legacy override for query list file. Prefer --dataset-id.")

      audit.add_argument("--top-k", type=int, default=None, help="Default: search_evaluation.audit_top_k.")
      audit.add_argument("--language", default=None, help="Default: search_evaluation.default_language.")
      audit.add_argument(

          "--limit-suspicious",
          type=int,
          default=None,
          help="Default: search_evaluation.audit_limit_suspicious.",
      )

      audit.add_argument("--force-refresh-labels", action="store_true")

      add_judge_llm_args(audit)

      add_intent_llm_args(audit)

  
      serve = sub.add_parser("serve", help="Serve evaluation web UI on port 6010")

      serve.add_argument("--tenant-id", default=None, help="Default: search_evaluation.default_tenant_id.")

      serve.add_argument("--dataset-id", default=None, help="Initial evaluation dataset id from config.yaml.")
      serve.add_argument("--queries-file", default=None, help="Legacy initial query file override. Prefer --dataset-id.")

      serve.add_argument("--host", default=None, help="Default: search_evaluation.web_host.")
      serve.add_argument("--port", type=int, default=None, help="Default: search_evaluation.web_port.")

      add_judge_llm_args(serve)

      add_intent_llm_args(serve)

  
      return parser
  
  
  def run_build(args: argparse.Namespace) -> None:

      dataset = _resolve_dataset_from_args(args)

      if args.reset_artifacts:

          _reset_build_artifacts(dataset.dataset_id)

      framework = SearchEvaluationFramework(tenant_id=args.tenant_id, **framework_kwargs_from_args(args))

      queries = list(dataset.queries)

      summary = []

      rebuild_kwargs = {}
      if args.force_refresh_labels:
          rebuild_kwargs = {

              "search_recall_top_k": args.search_recall_top_k,
              "rerank_high_threshold": args.rerank_high_threshold,
              "rerank_high_skip_count": args.rerank_high_skip_count,
              "rebuild_llm_batch_size": args.rebuild_llm_batch_size,
              "rebuild_min_batches": args.rebuild_min_batches,
              "rebuild_max_batches": args.rebuild_max_batches,
              "rebuild_irrelevant_stop_ratio": args.rebuild_irrelevant_stop_ratio,
              "rebuild_irrel_low_combined_stop_ratio": args.rebuild_irrel_low_combined_stop_ratio,
              "rebuild_irrelevant_stop_streak": args.rebuild_irrelevant_stop_streak,

          }

      total_q = len(queries)
      for q_index, query in enumerate(queries, start=1):
          _cli_log.info("[build] (%s/%s) starting query=%r", q_index, total_q, query)

          try:
              result = framework.build_query_annotation_set(
                  query=query,

                  dataset=dataset,

                  search_depth=args.search_depth,
                  rerank_depth=args.rerank_depth,
                  annotate_search_top_k=args.annotate_search_top_k,
                  annotate_rerank_top_k=args.annotate_rerank_top_k,
                  language=args.language,
                  force_refresh_rerank=args.force_refresh_rerank,
                  force_refresh_labels=args.force_refresh_labels,
                  **rebuild_kwargs,
              )
          except Exception:
              _cli_log.exception("[build] failed query=%r index=%s/%s", query, q_index, total_q)
              raise

          summary.append(
              {
                  "query": result.query,
                  "search_total": result.search_total,
                  "search_depth": result.search_depth,
                  "rerank_corpus_size": result.rerank_corpus_size,
                  "annotated_count": result.annotated_count,
                  "output_json_path": str(result.output_json_path),
              }
          )

          _cli_log.info(
              "[build] query=%r search_total=%s search_depth=%s corpus=%s annotated=%s output=%s",
              result.query,
              result.search_total,
              result.search_depth,
              result.rerank_corpus_size,
              result.annotated_count,
              result.output_json_path,

          )
      out_path = ensure_dir(framework.artifact_root / "query_builds") / f"build_summary_{utc_timestamp()}.json"

      out_path = query_builds_dir(framework.artifact_root, dataset.dataset_id) / f"build_summary_{utc_timestamp()}.json"

      out_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")

      _cli_log.info("[done] summary=%s", out_path)

  
  
  def run_batch(args: argparse.Namespace) -> None:

      dataset = _resolve_dataset_from_args(args, require_enabled=True)

      framework = SearchEvaluationFramework(tenant_id=args.tenant_id, **framework_kwargs_from_args(args))

      queries = list(dataset.queries)
      _cli_log.info("[batch] dataset_id=%s queries_file=%s count=%s", dataset.dataset_id, args.queries_file, len(queries))

      try:
          payload = framework.batch_evaluate(
              queries=queries,

              dataset=dataset,

              top_k=args.top_k,
              auto_annotate=True,
              language=args.language,
              force_refresh_labels=args.force_refresh_labels,
          )
      except Exception:
          _cli_log.exception("[batch] failed while evaluating query list from %s", args.queries_file)
          raise

      _cli_log.info("[done] batch_id=%s aggregate_metrics=%s", payload["batch_id"], payload["aggregate_metrics"])

  
  
  def run_audit(args: argparse.Namespace) -> None:

      dataset = _resolve_dataset_from_args(args, require_enabled=True)

      framework = SearchEvaluationFramework(tenant_id=args.tenant_id, **framework_kwargs_from_args(args))

      queries = list(dataset.queries)

      audit_items = []
      for query in queries:
          item = framework.audit_live_query(
              query=query,
              top_k=args.top_k,
              language=args.language,
              auto_annotate=not args.force_refresh_labels,
          )
          if args.force_refresh_labels:
              live_payload = framework.search_client.search(query=query, size=max(args.top_k, 100), from_=0, language=args.language)
              framework.annotate_missing_labels(
                  query=query,
                  docs=list(live_payload.get("results") or [])[: args.top_k],
                  force_refresh=True,
              )
              item = framework.audit_live_query(
                  query=query,
                  top_k=args.top_k,
                  language=args.language,
                  auto_annotate=False,
              )
          audit_items.append(
              {
                  "query": query,
                  "metrics": item["metrics"],
                  "distribution": item["distribution"],
                  "suspicious_count": len(item["suspicious"]),
                  "suspicious_examples": item["suspicious"][: args.limit_suspicious],
              }
          )

          _cli_log.info(
              "[audit] query=%r suspicious=%s metrics=%s",
              query,
              len(item["suspicious"]),
              item["metrics"],

          )
  
      summary = {
          "created_at": utc_now_iso(),
          "tenant_id": args.tenant_id,

          "dataset": dataset.summary(),

          "top_k": args.top_k,
          "query_count": len(queries),
          "total_suspicious": sum(item["suspicious_count"] for item in audit_items),
          "queries": audit_items,
      }

      out_path = audits_dir(framework.artifact_root, dataset.dataset_id) / f"audit_{utc_timestamp()}.json"

      out_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")

      _cli_log.info("[done] audit=%s", out_path)

  
  
  def run_serve(args: argparse.Namespace) -> None:

      dataset = _resolve_dataset_from_args(args, require_enabled=True)

      framework = SearchEvaluationFramework(tenant_id=args.tenant_id, **framework_kwargs_from_args(args))

      app = create_web_app(framework, initial_dataset_id=dataset.dataset_id)

      import uvicorn
  
      uvicorn.run(app, host=args.host, port=args.port, log_level="info")
  
  
  def main() -> None:

      se = get_app_config().search_evaluation
      log_file = setup_eval_logging(se.eval_log_dir)

      parser = build_cli_parser()
      args = parser.parse_args()

      _apply_search_evaluation_cli_defaults(args)

      logging.getLogger("search_eval").info(
          "CLI start command=%s tenant_id=%s log_file=%s",
          args.command,
          getattr(args, "tenant_id", ""),

          log_file.resolve(),

      )

      if args.command == "build":
          run_build(args)
          return
      if args.command == "batch":
          run_batch(args)
          return
      if args.command == "audit":
          run_audit(args)
          return
      if args.command == "serve":
          run_serve(args)
          return
      raise SystemExit(f"unknown command: {args.command}")