"""Markdown report generation for standardized benchmark outputs."""

from __future__ import annotations

import csv
from pathlib import Path
from typing import Any

from .io import OutputLayout, load_prediction_rows, read_json, write_json
from .plots import TASK_LABELS, TASK_METRICS, write_standard_plots
from .tasks import TASK_REGISTRY


def _model_key(output_dir: Path) -> str:
    config_path = output_dir / "run_config.json"
    if config_path.exists():
        config = read_json(config_path)
        return str(config.get("model") or config.get("model_path") or output_dir.name)
    return output_dir.name


def _model_label(output_dir: Path) -> str:
    key = _model_key(output_dir)
    return output_dir.name if output_dir.name not in {"output", "outputs"} else key


def score_output_dir(output_dir: Path) -> dict[str, Any]:
    layout = OutputLayout(output_dir)
    tasks: dict[str, Any] = {}
    for task_name, task in TASK_REGISTRY.items():
        task_dir = layout.task_dir(task_name)
        rows = load_prediction_rows(task_dir, sort_key=task.sort_key)
        if not rows:
            continue
        parsed_rows = task.parse_rows(rows)
        # Materialize normalized predictions for easier auditing.
        from .io import write_jsonl

        write_jsonl(task_dir / "predictions_scored.jsonl", parsed_rows)
        tasks[task_name] = task.score(parsed_rows)
    return {
        "model_key": _model_key(output_dir),
        "model_label": _model_label(output_dir),
        "output_dir": str(output_dir),
        "tasks": tasks,
    }


def flatten_metrics(model_results: list[dict[str, Any]]) -> list[dict[str, Any]]:
    rows: list[dict[str, Any]] = []
    for model in model_results:
        for task_name, metric_map in TASK_METRICS.items():
            source_task = metric_map["source_task"]
            summary = model["tasks"].get(source_task)
            if not summary:
                continue
            rows.append(
                {
                    "model": model["model_key"],
                    "model_label": model["model_label"],
                    "task": task_name,
                    "task_label": metric_map["label"],
                    "rows": summary.get("rows"),
                    "scored": summary.get("scored"),
                    "errors": summary.get("errors"),
                    "parse_errors": summary.get("parse_errors"),
                    "parse_success_rate": summary.get("parse_success_rate"),
                    "accuracy": summary.get(metric_map.get("accuracy", "")),
                    "balanced_accuracy": summary.get(metric_map.get("balanced_accuracy", "")),
                    "f1": summary.get(metric_map.get("f1", "")),
                    "precision": summary.get(metric_map.get("precision", "")),
                    "recall": summary.get(metric_map.get("recall", "")),
                }
            )
    return rows


def write_metrics_csv(path: Path, rows: list[dict[str, Any]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    if not rows:
        path.write_text("", encoding="utf-8")
        return
    with path.open("w", encoding="utf-8", newline="") as fh:
        writer = csv.DictWriter(fh, fieldnames=list(rows[0]))
        writer.writeheader()
        writer.writerows(rows)


def _pct(value: Any) -> str:
    return f"{float(value) * 100:.1f}%" if isinstance(value, (int, float)) else "n/a"


def write_report_md(report_dir: Path, model_results: list[dict[str, Any]], metric_rows: list[dict[str, Any]], plot_paths: list[Path]) -> None:
    lines = [
        "# Benchmark Report",
        "",
        "This report is generated from raw model outputs. Responses are parsed and scored at report time.",
        "",
        "## Summary Metrics",
        "",
        "| Model | Task | Balanced Accuracy | F1 | Precision | Recall | Parse Success |",
        "|---|---|---:|---:|---:|---:|---:|",
    ]
    for row in metric_rows:
        lines.append(
            "| {model_label} | {task_label} | {balanced_accuracy} | {f1} | {precision} | {recall} | {parse_success_rate} |".format(
                model_label=row["model_label"],
                task_label=row["task_label"],
                balanced_accuracy=_pct(row["balanced_accuracy"]),
                f1=_pct(row["f1"]),
                precision=_pct(row["precision"]),
                recall=_pct(row["recall"]),
                parse_success_rate=_pct(row["parse_success_rate"]),
            )
        )
    lines.extend(["", "## Plots", ""])
    for path in plot_paths:
        rel = path.relative_to(report_dir)
        title = path.stem.replace("_", " ").title()
        lines.extend([f"### {title}", "", f"![{title}]({rel.as_posix()})", ""])
    lines.extend(["## Task Details", ""])
    for model in model_results:
        lines.extend([f"### {model['model_label']}", ""])
        for task_name, summary in model["tasks"].items():
            lines.extend(
                [
                    f"#### {TASK_LABELS.get(task_name, task_name)}",
                    "",
                    f"- Rows: `{summary.get('rows')}`",
                    f"- Scored: `{summary.get('scored')}`",
                    f"- Errors: `{summary.get('errors')}`",
                    f"- Parse errors: `{summary.get('parse_errors')}`",
                    f"- Parse success: `{_pct(summary.get('parse_success_rate'))}`",
                    "",
                ]
            )
    (report_dir / "report.md").write_text("\n".join(lines), encoding="utf-8")


def generate_report(output_dirs: list[Path], report_dir: Path | None = None) -> dict[str, Any]:
    if not output_dirs:
        raise ValueError("Provide at least one output directory.")
    report_dir = report_dir or output_dirs[0]
    report_dir.mkdir(parents=True, exist_ok=True)
    model_results = [score_output_dir(path) for path in output_dirs]
    metric_rows = flatten_metrics(model_results)
    write_json(report_dir / "metrics.json", {"models": model_results, "rows": metric_rows})
    write_metrics_csv(report_dir / "metrics.csv", metric_rows)
    plot_paths = write_standard_plots(model_results, report_dir)
    write_report_md(report_dir, model_results, metric_rows, plot_paths)
    return {"report_dir": str(report_dir), "models": [row["model_key"] for row in model_results], "plots": [str(path) for path in plot_paths]}