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13e8fdecda
* add first draft. * fix * updates. * updates. * updates * updates * updates. * fix-copies * lora constants. * add tests * Apply suggestions from code review Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com> * docstrings. --------- Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>
40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
import os
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import tempfile
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import unittest
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import torch
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from diffusers.loaders.lora_base import LoraBaseMixin
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class UtilityMethodDeprecationTests(unittest.TestCase):
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def test_fetch_state_dict_cls_method_raises_warning(self):
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state_dict = torch.nn.Linear(3, 3).state_dict()
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with self.assertWarns(FutureWarning) as warning:
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_ = LoraBaseMixin._fetch_state_dict(
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state_dict,
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weight_name=None,
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use_safetensors=False,
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local_files_only=True,
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cache_dir=None,
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force_download=False,
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proxies=None,
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token=None,
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revision=None,
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subfolder=None,
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user_agent=None,
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allow_pickle=None,
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)
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warning_message = str(warning.warnings[0].message)
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assert "Using the `_fetch_state_dict()` method from" in warning_message
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def test_best_guess_weight_name_cls_method_raises_warning(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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state_dict = torch.nn.Linear(3, 3).state_dict()
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torch.save(state_dict, os.path.join(tmpdir, "pytorch_lora_weights.bin"))
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with self.assertWarns(FutureWarning) as warning:
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_ = LoraBaseMixin._best_guess_weight_name(pretrained_model_name_or_path_or_dict=tmpdir)
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warning_message = str(warning.warnings[0].message)
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assert "Using the `_best_guess_weight_name()` method from" in warning_message
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