"""Tests for dataset aggregation and derived thresholds."""

from __future__ import annotations

import json
from pathlib import Path

from swe_data_processing.summary import build_summary, percentile


def _metric(sample_id: str, tokens: int, errors: int) -> dict:
    return {
        "sample_id": sample_id,
        "resolved": 1,
        "source_dataset": "test",
        "length": {
            "turn_count": 10,
            "canonical_chars": 100,
            "token_count": tokens,
        },
        "tools": {
            "tool_call_count": 10,
            "failed_tool_call_count": errors,
            "failed_tool_call_rate": errors / 10,
            "longest_consecutive_failure_run": 0,
            "error_type_counts": {"nonzero_exit": errors} if errors else {},
            "error_tool_counts": {"bash": errors} if errors else {},
            "error_positions": {
                "early_count": 0,
                "early_fraction": 0.0,
                "occupied_bins_5": 0,
            },
        },
        "structure": {
            "invalid_turn_count": 0,
            "malformed_tool_definition_count": 0,
            "malformed_tool_call_count": 0,
            "unknown_tool_call_count": 0,
            "missing_tool_result_count": 0,
            "orphan_tool_result_count": 0,
        },
    }


def test_percentile_interpolates() -> None:
    assert percentile([0, 10], 0.5) == 5


def test_summary_reports_token_tail_and_p99_threshold(tmp_path: Path) -> None:
    path = tmp_path / "metrics.jsonl"
    rows = [_metric(f"s-{index}", 10_000, index) for index in range(10)]
    rows.append(_metric("long", 300_000, 0))
    path.write_text("".join(json.dumps(row) + "\n" for row in rows), encoding="utf-8")
    summary, threshold = build_summary(path)
    assert summary["sample_count"] == 11
    assert summary["length"]["buckets"]["GT_262144"]["samples"] == 1
    assert summary["length"]["longest_sample_token_contribution"]["0.001"][
        "token_share"
    ] > 0.7
    assert threshold >= 8