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| # Copyright 2020-2025 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import random | |
| import unittest | |
| import torch | |
| from transformers import is_bitsandbytes_available, is_comet_available, is_sklearn_available, is_wandb_available | |
| from transformers.testing_utils import torch_device | |
| from transformers.utils import is_rich_available | |
| from trl import BaseBinaryJudge, BasePairwiseJudge | |
| from trl.import_utils import ( | |
| is_diffusers_available, | |
| is_joblib_available, | |
| is_llm_blender_available, | |
| is_mergekit_available, | |
| is_vllm_available, | |
| ) | |
| # transformers.testing_utils contains a require_bitsandbytes function, but relies on pytest markers which we don't use | |
| # in our test suite. We therefore need to implement our own version of this function. | |
| 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) | |
| def require_comet(test_case): | |
| """ | |
| Decorator marking a test that requires Comet. Skips the test if Comet is not available. | |
| """ | |
| return unittest.skipUnless(is_comet_available(), "test requires comet_ml")(test_case) | |
| 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) | |
| 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) | |
| def require_mergekit(test_case): | |
| """ | |
| Decorator marking a test that requires mergekit. Skips the test if mergekit is not available. | |
| """ | |
| return unittest.skipUnless(is_mergekit_available(), "test requires mergekit")(test_case) | |
| def require_rich(test_case): | |
| """ | |
| Decorator marking a test that requires rich. Skips the test if rich is not available. | |
| """ | |
| return unittest.skipUnless(is_rich_available(), "test requires rich")(test_case) | |
| 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() and is_joblib_available(), "test requires sklearn")(test_case) | |
| def require_vllm(test_case): | |
| """ | |
| Decorator marking a test that requires vllm. Skips the test if vllm is not available. | |
| """ | |
| return unittest.skipUnless(is_vllm_available(), "test requires vllm")(test_case) | |
| 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) | |
| def require_3_accelerators(test_case): | |
| """ | |
| Decorator marking a test that requires at least 3 accelerators. Skips the test if 3 accelerators are not available. | |
| """ | |
| torch_accelerator_module = getattr(torch, torch_device, torch.cuda) | |
| return unittest.skipUnless( | |
| torch_accelerator_module.device_count() > 3, f"test requires at least 3 {torch_device}s" | |
| )(test_case) | |
| class RandomBinaryJudge(BaseBinaryJudge): | |
| """ | |
| Random binary judge, for testing purposes. | |
| """ | |
| def judge(self, prompts, completions, gold_completions=None, shuffle_order=True): | |
| return [random.choice([0, 1, -1]) for _ in range(len(prompts))] | |
| class RandomPairwiseJudge(BasePairwiseJudge): | |
| """ | |
| Random pairwise judge, for testing purposes. | |
| """ | |
| 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))] | |