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

  import logging

  from pathlib import Path

  from typing import Any, Dict

  from .constants import (

      DEFAULT_INTENT_ENABLE_THINKING,
      DEFAULT_INTENT_MODEL,

      DEFAULT_QUERY_FILE,

      DEFAULT_REBUILD_IRREL_LOW_COMBINED_STOP_RATIO,

      DEFAULT_REBUILD_IRRELEVANT_STOP_RATIO,
      DEFAULT_REBUILD_IRRELEVANT_STOP_STREAK,
      DEFAULT_REBUILD_LLM_BATCH_SIZE,
      DEFAULT_REBUILD_MAX_LLM_BATCHES,
      DEFAULT_REBUILD_MIN_LLM_BATCHES,
      DEFAULT_RERANK_HIGH_SKIP_COUNT,
      DEFAULT_RERANK_HIGH_THRESHOLD,
      DEFAULT_SEARCH_RECALL_TOP_K,
  )

  from .constants import EVAL_LOG_FILE

  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 add_judge_llm_args(p: argparse.ArgumentParser) -> None:
      p.add_argument(
          "--judge-model",
          default=None,
          metavar="MODEL",
          help="Judge LLM model (default: eval_framework.constants.DEFAULT_JUDGE_MODEL).",
      )
      p.add_argument(
          "--enable-thinking",
          action=argparse.BooleanOptionalAction,
          default=None,
          help="enable_thinking for DashScope (default: DEFAULT_JUDGE_ENABLE_THINKING).",
      )
      p.add_argument(
          "--dashscope-batch",
          action=argparse.BooleanOptionalAction,
          default=None,
          help="DashScope Batch File API vs sync chat (default: DEFAULT_JUDGE_DASHSCOPE_BATCH).",
      )
  
  

  def add_intent_llm_args(p: argparse.ArgumentParser) -> None:
      p.add_argument(
          "--intent-model",
          default=None,
          metavar="MODEL",
          help=f"Query-intent LLM model before relevance judging (default: {DEFAULT_INTENT_MODEL!r}).",
      )
      p.add_argument(
          "--intent-enable-thinking",
          action=argparse.BooleanOptionalAction,
          default=None,
          help=f"enable_thinking for intent model (default: {DEFAULT_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 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="163")
      build.add_argument("--queries-file", default=str(DEFAULT_QUERY_FILE))
      build.add_argument("--search-depth", type=int, default=1000)
      build.add_argument("--rerank-depth", type=int, default=10000)
      build.add_argument("--annotate-search-top-k", type=int, default=120)
      build.add_argument("--annotate-rerank-top-k", type=int, default=200)

      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 0.939).",

      )
      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="en")
      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="163")
      batch.add_argument("--queries-file", default=str(DEFAULT_QUERY_FILE))
      batch.add_argument("--top-k", type=int, default=100)
      batch.add_argument("--language", default="en")
      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="163")
      audit.add_argument("--queries-file", default=str(DEFAULT_QUERY_FILE))
      audit.add_argument("--top-k", type=int, default=100)
      audit.add_argument("--language", default="en")
      audit.add_argument("--limit-suspicious", type=int, default=5)
      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="163")
      serve.add_argument("--queries-file", default=str(DEFAULT_QUERY_FILE))
      serve.add_argument("--host", default="0.0.0.0")
      serve.add_argument("--port", type=int, default=6010)

      add_judge_llm_args(serve)

      add_intent_llm_args(serve)

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

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

      queries = framework.queries_from_file(Path(args.queries_file))
      summary = []

      rebuild_kwargs = {}
      if args.force_refresh_labels:
          rebuild_kwargs = {
              "search_recall_top_k": args.search_recall_top_k if args.search_recall_top_k is not None else DEFAULT_SEARCH_RECALL_TOP_K,
              "rerank_high_threshold": args.rerank_high_threshold if args.rerank_high_threshold is not None else DEFAULT_RERANK_HIGH_THRESHOLD,
              "rerank_high_skip_count": args.rerank_high_skip_count if args.rerank_high_skip_count is not None else DEFAULT_RERANK_HIGH_SKIP_COUNT,
              "rebuild_llm_batch_size": args.rebuild_llm_batch_size if args.rebuild_llm_batch_size is not None else DEFAULT_REBUILD_LLM_BATCH_SIZE,
              "rebuild_min_batches": args.rebuild_min_batches if args.rebuild_min_batches is not None else DEFAULT_REBUILD_MIN_LLM_BATCHES,
              "rebuild_max_batches": args.rebuild_max_batches if args.rebuild_max_batches is not None else DEFAULT_REBUILD_MAX_LLM_BATCHES,
              "rebuild_irrelevant_stop_ratio": args.rebuild_irrelevant_stop_ratio
              if args.rebuild_irrelevant_stop_ratio is not None
              else DEFAULT_REBUILD_IRRELEVANT_STOP_RATIO,

              "rebuild_irrel_low_combined_stop_ratio": args.rebuild_irrel_low_combined_stop_ratio
              if args.rebuild_irrel_low_combined_stop_ratio is not None
              else DEFAULT_REBUILD_IRREL_LOW_COMBINED_STOP_RATIO,

              "rebuild_irrelevant_stop_streak": args.rebuild_irrelevant_stop_streak
              if args.rebuild_irrelevant_stop_streak is not None
              else DEFAULT_REBUILD_IRRELEVANT_STOP_STREAK,
          }

      for query in queries:
          result = framework.build_query_annotation_set(
              query=query,
              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,

          )
          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.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:

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

      queries = framework.queries_from_file(Path(args.queries_file))
      payload = framework.batch_evaluate(
          queries=queries,
          top_k=args.top_k,
          auto_annotate=True,
          language=args.language,
          force_refresh_labels=args.force_refresh_labels,
      )

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

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

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

      queries = framework.queries_from_file(Path(args.queries_file))
      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,
          "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 = ensure_dir(framework.artifact_root / "audits") / 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:

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

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

      setup_eval_logging()

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

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