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| import random |
| import unittest |
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| from transformers import is_bitsandbytes_available, is_sklearn_available, is_wandb_available |
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| from trl import BaseBinaryJudge, BasePairwiseJudge, is_diffusers_available, is_llm_blender_available |
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| def require_bitsandbytes(test_case): |
| """ |
| Decorator marking a test that requires bitsandbytes. Skips the test if bitsandbytes is not available. |
| """ |
| return unittest.skipUnless(is_bitsandbytes_available(), "test requires bitsandbytes")(test_case) |
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| def require_diffusers(test_case): |
| """ |
| Decorator marking a test that requires diffusers. Skips the test if diffusers is not available. |
| """ |
| return unittest.skipUnless(is_diffusers_available(), "test requires diffusers")(test_case) |
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| def require_no_wandb(test_case): |
| """ |
| Decorator marking a test that requires no wandb. Skips the test if wandb is available. |
| """ |
| return unittest.skipUnless(not is_wandb_available(), "test requires no wandb")(test_case) |
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| def require_sklearn(test_case): |
| """ |
| Decorator marking a test that requires sklearn. Skips the test if sklearn is not available. |
| """ |
| return unittest.skipUnless(is_sklearn_available(), "test requires sklearn")(test_case) |
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| def require_llm_blender(test_case): |
| """ |
| Decorator marking a test that requires llm-blender. Skips the test if llm-blender is not available. |
| """ |
| return unittest.skipUnless(is_llm_blender_available(), "test requires llm-blender")(test_case) |
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|
| class RandomBinaryJudge(BaseBinaryJudge): |
| """ |
| Random binary judge, for testing purposes. |
| """ |
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| def judge(self, prompts, completions, gold_completions=None, shuffle_order=True): |
| return [random.choice([0, 1, -1]) for _ in range(len(prompts))] |
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|
| class RandomPairwiseJudge(BasePairwiseJudge): |
| """ |
| Random pairwise judge, for testing purposes. |
| """ |
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| def judge(self, prompts, completions, shuffle_order=True, return_scores=False): |
| if not return_scores: |
| return [random.randint(0, len(completion) - 1) for completion in completions] |
| else: |
| return [random.random() for _ in range(len(prompts))] |
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