"""Markdown and text reports for batch evaluation."""
  
  from __future__ import annotations
  
  from typing import Any, Dict
  

  from .constants import RELEVANCE_GAIN_MAP, RELEVANCE_LV0, RELEVANCE_LV1, RELEVANCE_LV2, RELEVANCE_LV3

  from .metrics import PRIMARY_METRIC_KEYS

  def _append_metric_block(lines: list[str], metrics: Dict[str, Any]) -> None:

      primary_keys = (

          "Primary_Metric_Score",
          *PRIMARY_METRIC_KEYS,

          "ERR@10",

      )

      included = set()
      for key in primary_keys:
          if key in metrics:
              lines.append(f"- {key}: {metrics[key]}")
              included.add(key)
      for key, value in sorted(metrics.items()):
          if key in included:
              continue
          lines.append(f"- {key}: {value}")
  
  

  def _label_level_code(label: str) -> str:
      grade = RELEVANCE_GAIN_MAP.get(label)
      return f"L{grade}" if grade is not None else "?"
  
  
  def _append_case_snapshot(lines: list[str], item: Dict[str, Any]) -> None:
      request_id = str(item.get("request_id") or "").strip()
      if request_id:
          lines.append(f"- Request ID: `{request_id}`")
      seq10 = str(item.get("top_label_sequence_top10") or "").strip()
      if seq10:
          lines.append(f"- Top-10 Labels: `{seq10}`")
      seq20 = str(item.get("top_label_sequence_top20") or "").strip()
      if seq20 and seq20 != seq10:
          lines.append(f"- Top-20 Labels: `{seq20}`")
      top_results = item.get("top_results") or []
      if not top_results:
          return
      lines.append("- Case Snapshot:")
      for result in top_results[:5]:
          rank = int(result.get("rank") or 0)
          label = _label_level_code(str(result.get("label") or ""))
          spu_id = str(result.get("spu_id") or "")
          title = str(result.get("title") or "")
          title_zh = str(result.get("title_zh") or "")
          relevance_score = result.get("relevance_score")
          score_suffix = f" (rel={relevance_score})" if relevance_score not in (None, "") else ""
          lines.append(f"  - #{rank} [{label}] spu={spu_id} {title}{score_suffix}")
          if title_zh:
              lines.append(f"    zh: {title_zh}")
  
  

  def render_batch_report_markdown(payload: Dict[str, Any]) -> str:
      lines = [
          "# Search Batch Evaluation",
          "",
          f"- Batch ID: {payload['batch_id']}",
          f"- Created at: {payload['created_at']}",
          f"- Tenant ID: {payload['tenant_id']}",
          f"- Query count: {len(payload['queries'])}",
          f"- Top K: {payload['top_k']}",
          "",
          "## Aggregate Metrics",
          "",
      ]

      metric_context = payload.get("metric_context") or {}
      if metric_context:
          lines.extend(
              [
                  f"- Primary metric: {metric_context.get('primary_metric', 'N/A')}",

                  f"- Gain scheme (NDCG): {metric_context.get('gain_scheme', {})}",
                  f"- Stop probabilities (ERR): {metric_context.get('stop_prob_scheme', {})}",

                  "",
              ]
          )
      _append_metric_block(lines, payload.get("aggregate_metrics") or {})

      distribution = payload.get("aggregate_distribution") or {}
      if distribution:
          lines.extend(
              [
                  "",
                  "## Label Distribution",
                  "",

                  f"- Fully Relevant: {distribution.get(RELEVANCE_LV3, 0)}",
                  f"- Mostly Relevant: {distribution.get(RELEVANCE_LV2, 0)}",
                  f"- Weakly Relevant: {distribution.get(RELEVANCE_LV1, 0)}",
                  f"- Irrelevant: {distribution.get(RELEVANCE_LV0, 0)}",

              ]
          )
      lines.extend(["", "## Per Query", ""])
      for item in payload.get("per_query") or []:
          lines.append(f"### {item['query']}")
          lines.append("")

          _append_metric_block(lines, item.get("metrics") or {})

          distribution = item.get("distribution") or {}

          lines.append(f"- Fully Relevant: {distribution.get(RELEVANCE_LV3, 0)}")
          lines.append(f"- Mostly Relevant: {distribution.get(RELEVANCE_LV2, 0)}")
          lines.append(f"- Weakly Relevant: {distribution.get(RELEVANCE_LV1, 0)}")
          lines.append(f"- Irrelevant: {distribution.get(RELEVANCE_LV0, 0)}")
          _append_case_snapshot(lines, item)

          lines.append("")
      return "\n".join(lines)