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bea429c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | """Small deterministic checks for the audited source contract, without loading Gemma."""
from __future__ import annotations
import json
import struct
import pytest
from assets import LOCK, fetch_source, sha256
from native_reference import TEMPERATURE, choose, encode, pad_batch, softmax
def test_real_trained_adapter_contains_saved_scalar_head() -> None:
source = fetch_source()
adapter = source / "pretrained-scorer" / "adapter_model.safetensors"
with adapter.open("rb") as stream:
header_length = struct.unpack("<Q", stream.read(8))[0]
header = json.loads(stream.read(header_length))
tensor = header[LOCK["native_serving"]["trained_score_tensor"]]
assert tensor["shape"] == [1, 640]
assert tensor["dtype"] == "BF16"
assert sha256(adapter) == LOCK["files"]["pretrained-scorer/adapter_model.safetensors"]
def test_locked_metrics_match_released_app_calibration() -> None:
source = fetch_source(include_adapter=False)
metrics = json.loads((source / "pretrained-scorer" / "metrics.json").read_text())
assert metrics["temperature"] == TEMPERATURE
assert metrics["max_len"] == LOCK["native_serving"]["max_length"] == 256
adapter_config = json.loads((source / "pretrained-scorer" / "adapter_config.json").read_text())
assert "score" in adapter_config["modules_to_save"]
assert adapter_config["base_model_name_or_path"] == LOCK["base_repo"]
def test_native_rendering_preserves_question_tail_and_right_padding() -> None:
class CharacterTokenizer:
def __call__(self, text: str, add_special_tokens: bool):
assert not add_special_tokens
return {"input_ids": [ord(character) for character in text]}
tokens = encode(CharacterTokenizer(), "state", "question", "option", max_length=28)
assert tokens[-len("\n\nOption:\noption") :] == [ord(c) for c in "\n\nOption:\noption"]
ids, mask = pad_batch([[1, 2, 3], [4]], 0, 4)
assert ids == [[1, 2, 3, 0], [4, 0, 0, 0]]
assert mask == [[1, 1, 1, 0], [1, 0, 0, 0]]
def test_calibrated_choice_and_invalid_temperature() -> None:
result = choose(["first", "second"], [0.0, 2.35])
assert result["selected_index"] == 1
assert result["probabilities"] == pytest.approx([0.2689414214, 0.7310585786])
with pytest.raises(ValueError):
softmax([1.0], temperature=0.0)
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