"""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)