From ceb54584de9561a7e59affdc295b33700f76424f Mon Sep 17 00:00:00 2001 From: Alpamys Date: Sun, 5 Jul 2026 16:54:43 +0500 Subject: [PATCH] style(prm): ruff import-order + lowercase locals in test fixture (v0.71.30) --- tests/test_v07130.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/tests/test_v07130.py b/tests/test_v07130.py index f877abc..6515742 100644 --- a/tests/test_v07130.py +++ b/tests/test_v07130.py @@ -230,13 +230,14 @@ def _make_fake_scorer(aggregate="min"): reward_head averages H → each step boundary scores its token position. Fake tokenizer: one token per whitespace word (>=1). """ + from types import SimpleNamespace + import torch from torch import nn - from types import SimpleNamespace from soup_cli.utils.prm_reward import PRMScorer - H = 8 + hidden = 8 class _FakeTok: pad_token = "" @@ -246,17 +247,17 @@ def _make_fake_scorer(aggregate="min"): n = max(1, len(text.split())) if text else 0 return {"input_ids": [1] * n} - head = nn.Linear(H, 1, bias=True) + head = nn.Linear(hidden, 1, bias=True) with torch.no_grad(): - head.weight.copy_(torch.ones(1, H) / H) + head.weight.copy_(torch.ones(1, hidden) / hidden) head.bias.zero_() class _FakeModel: reward_head = head def __call__(self, input_ids, output_hidden_states=False): - T = input_ids.shape[1] - hs = torch.arange(T).float().reshape(1, T, 1).repeat(1, 1, H) + seq_len = input_ids.shape[1] + hs = torch.arange(seq_len).float().reshape(1, seq_len, 1).repeat(1, 1, hidden) return SimpleNamespace(hidden_states=[hs]) scorer = PRMScorer("./prm", aggregate=aggregate, device="cpu")