tests/test_C/test_run.py

"""Tests for harness/C/run.py -- result-record shape and result-file writing."""

import json

from harness import C
from harness.C import run as harness_run

_FAKE_ANSWER = {
    "prompt_text": "<rendered chat template>",
    "answer_text": "4",
    "answer_raw": "<|im_start|>assistant\n4<|im_end|>",
    "input_token_count": 22205,
    "vision_input_shapes": {"pixel_values": [76800, 1536], "image_grid_thw": [64, 3]},
    "output_token_ids": [19, 151645],
    "output_token_count": 2,
    "hit_token_limit": False,
    "eos_token_ids": [151645],
    "generation_seconds": 5.6,
    "device": "cuda",
    "dtype": "bfloat16",
    "library_versions": {"transformers": "5.14.1", "torch": "2.13.0+cu130"},
    "generation_config": {
        "max_new_tokens": 16,
        "do_sample": False,
        "temperature": 0.0,
        "top_p": None,
        "top_k": None,
        "enable_thinking": False,
    },
}

_FAKE_ROW = {
    "id": 7,
    "scene_name": "scene0001_00",
    "dataset": "scannet",
    "question_type": "object_counting",
    "question": "How many chairs?",
    "options": None,
    "ground_truth": "4",
}

_FAKE_SOURCE_INFO = {
    "protocol": "thinking",
    "spatial_code_format": "explicit",
    "input_selection": "selective",
    "frame_count": 64,
    "depth": "metric",
    "tracking": "tracking",
    "spatial_code_path": "/workspace/data/spatial codes/.../scene0001_00.json",
    "video_path": "/root/data/VSI-Bench/scannet/scene0001_00.mp4",
    "frame_indices": [0, 30, 60],
    "frame_timestamps": [0.0, 1.0, 2.0],
}


def test_results_dir_for_matches_established_dimension_nesting():
    root = harness_run.results_dir_for(
        "qwen3.5-4b", "thinking", "explicit", "metric", "tracking", "uniform", 32
    )
    assert root == (
        C.RESULTS_DIR
        / "qwen3.5-4b"
        / "explicit"
        / "metric"
        / "tracking"
        / "uniform"
        / "32"
    )


def test_results_dir_for_honors_explicit_override(tmp_path):
    root = harness_run.results_dir_for(
        "qwen3.5-4b",
        "base",
        "explicit",
        "relative",
        "no tracking",
        "selective",
        16,
        tmp_path,
    )
    assert root == tmp_path


def test_build_record_carries_both_frame_and_spatial_code_provenance():
    record = harness_run._build_record(
        _FAKE_ROW,
        "full prompt text",
        _FAKE_ANSWER,
        "MRA:.5:.95:.05",
        1.0,
        "qwen3.5-4b",
        "/root/models/qwen3.5-4b",
        _FAKE_SOURCE_INFO,
    )
    # Spatial-code provenance (shared with harness.B).
    assert record["spatial_code_format"] == "explicit"
    assert record["input_selection"] == "selective"
    assert record["frame_count"] == 64
    assert record["depth"] == "metric"
    assert record["tracking"] == "tracking"
    assert record["spatial_code_path"] == _FAKE_SOURCE_INFO["spatial_code_path"]
    # Frame provenance (shared with harness.A).
    assert record["video_path"] == _FAKE_SOURCE_INFO["video_path"]
    assert record["frame_indices"] == [0, 30, 60]
    assert record["frame_timestamps_seconds"] == [0.0, 1.0, 2.0]
    # Question/answer fields, same shape as A and B.
    assert record["question"] == "How many chairs?"
    assert record["answer_given"] == "4"
    assert record["vision_input_shapes"] == _FAKE_ANSWER["vision_input_shapes"]
    assert record["condition"] == "extended:explicit:metric:tracking:selective:64"
    assert record["protocol"] == "thinking"
    assert record["score"] == 1.0


def test_write_question_result_writes_one_json_file_per_question(tmp_path):
    path, record = harness_run.write_question_result(
        _FAKE_ROW,
        "full prompt text",
        _FAKE_ANSWER,
        "MRA:.5:.95:.05",
        1.0,
        "qwen3.5-4b",
        "/root/models/qwen3.5-4b",
        _FAKE_SOURCE_INFO,
        results_dir=tmp_path,
    )
    assert path == tmp_path / "scene0001_00" / "7.json"
    on_disk = json.loads(path.read_text())
    assert on_disk == record


def test_build_record_carries_reasoning_fields_when_forced():
    extended_answer = {
        **_FAKE_ANSWER,
        "reasoning_text": "long reasoning about the frames and spatial code",
        "reasoning_raw": "long reasoning about the frames and spatial code<|im_end|>",
        "reasoning_token_ids": list(range(50)),
        "reasoning_token_count": 50,
        "reasoning_hit_limit": True,
        "forced": True,
        "forced_input_token_count": 22300,
    }
    record = harness_run._build_record(
        _FAKE_ROW,
        "full prompt text",
        extended_answer,
        "MRA:.5:.95:.05",
        1.0,
        "qwen3.5-4b",
        "/root/models/qwen3.5-4b",
        _FAKE_SOURCE_INFO,
    )
    assert (
        record["reasoning_text"] == "long reasoning about the frames and spatial code"
    )
    assert record["reasoning_raw"] == "long reasoning about the frames and spatial code<|im_end|>"
    assert record["reasoning_token_ids"] == list(range(50))
    assert record["reasoning_token_count"] == 50
    assert record["reasoning_hit_limit"] is True
    assert record["forced"] is True
    assert record["forced_input_token_count"] == 22300


def test_video_results_use_video_branch():
    assert (
        harness_run.results_dir_for(
            "qwen3.5-4b", "thinking", "explicit", "metric", "tracking", "video", None
        )
        == C.RESULTS_DIR / "qwen3.5-4b" / "explicit" / "metric" / "tracking" / "video"
    )


def test_video_record_has_no_frame_count_in_condition():
    info = dict(_FAKE_SOURCE_INFO, input_selection="video", frame_count=None)
    record = harness_run._build_record(
        _FAKE_ROW, "prompt", _FAKE_ANSWER, "metric", 1.0, "qwen3.5-4b", "/model", info
    )
    assert record["condition"] == "thinking:explicit:metric:tracking:video"
    assert record["frame_count"] is None