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c8a2b25f91
Mistaken use of De Morgan's law. Fixed "not (X or Y)" to correct "not (X and Y)" check to raise a ValueError. Added corresponding test to check "positive int or None" condition. Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
import os
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import tempfile
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import unittest
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from transformers import TrainingArguments
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class TestTrainingArguments(unittest.TestCase):
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def test_default_output_dir(self):
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"""Test that output_dir defaults to 'trainer_output' when not specified."""
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args = TrainingArguments(output_dir=None)
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self.assertEqual(args.output_dir, "trainer_output")
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def test_custom_output_dir(self):
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"""Test that output_dir is respected when specified."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(output_dir=tmp_dir)
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self.assertEqual(args.output_dir, tmp_dir)
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def test_output_dir_creation(self):
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"""Test that output_dir is created only when needed."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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output_dir = os.path.join(tmp_dir, "test_output")
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# Directory should not exist before creating args
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self.assertFalse(os.path.exists(output_dir))
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# Create args with save_strategy="no" - should not create directory
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args = TrainingArguments(
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output_dir=output_dir,
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do_train=True,
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save_strategy="no",
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report_to=None,
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)
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self.assertFalse(os.path.exists(output_dir))
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# Now set save_strategy="steps" - should create directory when needed
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args.save_strategy = "steps"
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args.save_steps = 1
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self.assertFalse(os.path.exists(output_dir)) # Still shouldn't exist
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# Directory should be created when actually needed (e.g. in Trainer)
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def test_torch_empty_cache_steps_requirements(self):
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"""Test that torch_empty_cache_steps is a positive integer or None."""
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# None is acceptable (feature is disabled):
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args = TrainingArguments(torch_empty_cache_steps=None)
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self.assertIsNone(args.torch_empty_cache_steps)
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# non-int is unacceptable:
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with self.assertRaises(ValueError):
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TrainingArguments(torch_empty_cache_steps=1.0)
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with self.assertRaises(ValueError):
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TrainingArguments(torch_empty_cache_steps="none")
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# negative int is unacceptable:
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with self.assertRaises(ValueError):
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TrainingArguments(torch_empty_cache_steps=-1)
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# zero is unacceptable:
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with self.assertRaises(ValueError):
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TrainingArguments(torch_empty_cache_steps=0)
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# positive int is acceptable:
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args = TrainingArguments(torch_empty_cache_steps=1)
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self.assertEqual(args.torch_empty_cache_steps, 1)
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