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fix spelling errors (#38608)

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* fix errors test_modeling_mllama.py

* fix error test_modeling_video_llava.py

* fix errors test_processing_common.py
This commit is contained in:
David Klank 2025-06-05 15:57:23 +03:00 committed by GitHub
parent 0f833528c9
commit fa921ad854
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GPG Key ID: B5690EEEBB952194
3 changed files with 5 additions and 5 deletions
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6
tests/models/mllama/test_modeling_mllama.py
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@ -366,15 +366,15 @@ class MllamaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTester
def test_assisted_decoding_with_num_logits_to_keep(self):
pass
@unittest.skip(reason="Mllama uses self.weights dirrectly causing device mismatch when offloading`")
@unittest.skip(reason="Mllama uses self.weights directly causing device mismatch when offloading`")
def test_cpu_offload(self):
pass
@unittest.skip(reason="Mllama uses self.weights dirrectly causing device mismatch when offloading`")
@unittest.skip(reason="Mllama uses self.weights directly causing device mismatch when offloading`")
def test_disk_offload_bin(self):
pass
@unittest.skip(reason="Mllama uses self.weights dirrectly causing device mismatch when offloading`")
@unittest.skip(reason="Mllama uses self.weights directly causing device mismatch when offloading`")
def test_disk_offload_safetensors(self):
pass

2
tests/models/video_llava/test_modeling_video_llava.py
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@ -399,7 +399,7 @@ class VideoLlavaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTe
for model_class in self.all_model_classes:
model = model_class(config).to(torch_device)
curr_input_dict = copy.deepcopy(input_dict)
_ = model(**curr_input_dict) # successfull forward with no modifications
_ = model(**curr_input_dict) # successful forward with no modifications
# remove one image but leave the image token in text
curr_input_dict["pixel_values_images"] = curr_input_dict["pixel_values_images"][-1:, ...]

2
tests/test_processing_common.py
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@ -915,7 +915,7 @@ class ProcessorTesterMixin:
)
@require_av
@parameterized.expand([(1, "pt"), (2, "pt")]) # video processor suports only torchvision
@parameterized.expand([(1, "pt"), (2, "pt")]) # video processor supports only torchvision
def test_apply_chat_template_video(self, batch_size: int, return_tensors: str):
self._test_apply_chat_template(
"video", batch_size, return_tensors, "videos_input_name", "video_processor", MODALITY_INPUT_DATA["videos"]