"""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}")