| import collections |
| import unittest |
| from multisource_core import license_header,code_records,evaluation_record,units,doc_records |
|
|
| META={'source_id':'test_source','source_repo':'org/repo','source_revision':'abc', |
| 'source_path':'model.py','source_url':'https://example.org/model.py','source_group':'example','source_row':-1,'upstream_track':'main'} |
|
|
|
|
| class MultiSourceTests(unittest.TestCase): |
| def test_unknown_license_is_not_inferred_from_mit_dataset_wrapper(self): |
| counters=collections.Counter() |
| self.assertFalse(code_records('import torch\nx=torch.tensor([1,2])',META,counters,default_license='MIT',require_inline=True)) |
| self.assertEqual(counters['missing_original_license_evidence'],1) |
| def test_explicit_spdx_is_preserved(self): |
| code='# Copyright (c) Example Author\n# SPDX-License-Identifier: Apache-2.0\nimport torch\nx=torch.tensor([1,2])' |
| license_id,notice,evidence=license_header(code) |
| self.assertEqual(license_id,'Apache-2.0');self.assertIn('Example Author',notice) |
| self.assertEqual(evidence,'inline_spdx') |
| def test_noncommercial_does_not_become_mit(self): |
| code='# Copyright NVIDIA\n# Nvidia Source Code License-NC\nimport torch\nx=torch.tensor([1,2])' |
| self.assertEqual(license_header(code)[2],'restricted_or_unsupported_header') |
| def test_root_bsd_repository_license_is_used_only_without_conflict(self): |
| rows=code_records('import torch\nx=torch.tensor([1,2])',META,collections.Counter(),default_license='BSD-3-Clause') |
| self.assertTrue(rows);self.assertEqual(rows[0]['license_id'],'BSD-3-Clause') |
| def test_cnn_survives_unsafe_download_entrypoint(self): |
| code='''import torch |
| from torch import nn |
| from torchvision import datasets |
| class Net(nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.conv=nn.Conv2d(3,8,3) |
| def forward(self,x): |
| return self.conv(x) |
| if __name__=='__main__': |
| datasets.CIFAR10('.',download=True) |
| ''' |
| rows=code_records(code,META,collections.Counter(),default_license='BSD-3-Clause') |
| self.assertTrue(any('cnn_from_scratch_candidate' in r['topics'] for r in rows)) |
| self.assertTrue(all('download=True' not in r['code'] for r in rows)) |
| def test_decorator_is_not_lost(self): |
| code='import torch\n@torch.no_grad()\ndef predict(x):\n return torch.softmax(x,dim=-1)' |
| rows=code_records(code,META,collections.Counter(),default_license='BSD-3-Clause') |
| self.assertTrue(rows);self.assertIn('@torch.no_grad()',rows[0]['code']) |
| def test_guarded_import_is_never_hoisted(self): |
| code='try:\n import optional_module\nexcept ImportError:\n pass\nimport torch\ndef f(x):\n return torch.relu(x)' |
| rows=code_records(code,META,collections.Counter(),default_license='BSD-3-Clause') |
| self.assertTrue(rows);self.assertNotIn('optional_module',rows[-1]['code']) |
| def test_benchmark_has_no_training_split_or_recipe_claim(self): |
| r=evaluation_record(META,'case1','An intentionally failing shape problem.','MIT','import torch\nx=torch.tensor([1,2])') |
| self.assertTrue(r['benchmark_content']);self.assertEqual(r['split'],'test');self.assertEqual(r['intended_use'],'evaluation') |
| self.assertIn('not a working recipe',r['text']) |
| def test_secret_and_process_calls_rejected(self): |
| rows=code_records("import subprocess\nsubprocess.run(['whoami'])",META,collections.Counter(),default_license='MIT') |
| self.assertFalse(rows) |
| def test_documentation_does_not_split_python_fences(self): |
| text='PyTorch tensor training documentation. '*12+'\n\n```python\nprint(123)\n```\n\nMore PyTorch explanation. '*8 |
| rows=doc_records(text,META,'BSD-3-Clause',collections.Counter()) |
| self.assertTrue(rows);self.assertTrue(all('```' not in r['text'] for r in rows)) |
|
|
|
|
| if __name__=='__main__':unittest.main() |
|
|
