Repository: huggingface/transformers
Pull request: [Tests] Clean up test cases for few models

# What does this PR do?

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Continuation of #38311


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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Changed files:
filename: tests/models/qwen2_5_omni/test_processor_qwen2_5_omni.py
patch: @@ -204,8 +204,7 @@ def test_kwargs_overrides_default_tokenizer_kwargs_audio(self):
     @classmethod
     def setUpClass(cls):
         cls.tmpdirname = tempfile.mkdtemp()
-        processor_kwargs = cls.prepare_processor_dict()
-        processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B", **processor_kwargs)
+        processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
         processor.save_pretrained(cls.tmpdirname)
 
     def get_tokenizer(self, **kwargs):
@@ -220,11 +219,8 @@ def get_video_processor(self, **kwargs):
     def get_feature_extractor(self, **kwargs):
         return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).feature_extractor
 
-    @staticmethod
-    def prepare_processor_dict():
-        return {
-            "chat_template": "{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}
system\nYou are a helpful assistant.
\n{% endif %}
{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}
\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}


{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}


{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}


{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}
\n{% endif %}{% endfor %}{% if add_generation_prompt %}
assistant\n{% endif %}"
-        }
+    def get_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
 
     @classmethod
     def tearDownClass(cls):

filename: tests/models/qwen2_5_vl/test_processor_qwen2_5_vl.py
patch: @@ -58,11 +58,8 @@ def get_image_processor(self, **kwargs):
     def get_video_processor(self, **kwargs):
         return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).video_processor
 
-    @staticmethod
-    def prepare_processor_dict():
-        return {
-            "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}
system\nYou are a helpful assistant.
\n{% endif %}
{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}
\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}


{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}


{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}
\n{% endif %}{% endfor %}{% if add_generation_prompt %}
assistant\n{% endif %}",
-        }  # fmt: skip
+    def get_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
 
     @classmethod
     def tearDownClass(cls):
@@ -340,12 +337,7 @@ def test_apply_chat_template_video_frame_sampling(self):
         self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 160)
 
     def test_kwargs_overrides_custom_image_processor_kwargs(self):
-        processor_components = self.prepare_components()
-        processor_components["image_processor"] = self.get_component("image_processor")
-        processor_components["tokenizer"] = self.get_component("tokenizer")
-        processor_kwargs = self.prepare_processor_dict()
-
-        processor = self.processor_class(**processor_components, **processor_kwargs, use_fast=True)
+        processor = self.get_processor()
         self.skip_processor_without_typed_kwargs(processor)
 
         input_str = self.prepare_text_inputs()

filename: tests/models/qwen2_audio/test_processor_qwen2_audio.py
patch: @@ -36,8 +36,7 @@ def setUpClass(cls):
         cls.checkpoint = "Qwen/Qwen2-Audio-7B-Instruct"
         cls.tmpdirname = tempfile.mkdtemp()
 
-        processor_kwargs = cls.prepare_processor_dict()
-        processor = Qwen2AudioProcessor.from_pretrained(cls.checkpoint, **processor_kwargs)
+        processor = Qwen2AudioProcessor.from_pretrained(cls.checkpoint)
         processor.save_pretrained(cls.tmpdirname)
         cls.audio_token = processor.audio_token
 
@@ -47,16 +46,13 @@ def get_tokenizer(self, **kwargs):
     def get_audio_processor(self, **kwargs):
         return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).audio_processor
 
+    def get_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
+
     @classmethod
     def tearDownClass(cls):
         shutil.rmtree(cls.tmpdirname, ignore_errors=True)
 
-    @staticmethod
-    def prepare_processor_dict():
-        return {
-            "chat_template": "{% set audio_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}
system\nYou are a helpful assistant.
\n{% endif %}
{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}
\n{% else %}{% for content in message['content'] %}{% if 'audio' in content or 'audio_url' in content or content['type'] == 'audio' %}{% set audio_count.value = audio_count.value + 1 %}Audio {{ audio_count.value }}: 


\n{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}
\n{% endif %}{% endfor %}{% if add_generation_prompt %}
assistant\n{% endif %}",
-        }
-
     def test_can_load_various_tokenizers(self):
         processor = Qwen2AudioProcessor.from_pretrained(self.checkpoint)
         tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)

filename: tests/models/qwen2_vl/test_processor_qwen2_vl.py
patch: @@ -61,9 +61,8 @@ def get_image_processor(self, **kwargs):
     def get_video_processor(self, **kwargs):
         return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).video_processor
 
-    @staticmethod
-    def prepare_processor_dict():
-        return {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}
system\nYou are a helpful assistant.
\n{% endif %}
{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}
\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}


{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}


{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}
\n{% endif %}{% endfor %}{% if add_generation_prompt %}
assistant\n{% endif %}"}  # fmt: skip
+    def get_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
 
     @classmethod
     def tearDownClass(cls):
@@ -406,12 +405,7 @@ def _process_messages_for_chat_template(
         self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 21960)
 
     def test_kwargs_overrides_custom_image_processor_kwargs(self):
-        processor_components = self.prepare_components()
-        processor_components["image_processor"] = self.get_component("image_processor")
-        processor_components["tokenizer"] = self.get_component("tokenizer")
-        processor_kwargs = self.prepare_processor_dict()
-
-        processor = self.processor_class(**processor_components, **processor_kwargs, use_fast=True)
+        processor = self.get_processor()
         self.skip_processor_without_typed_kwargs(processor)
 
         input_str = self.prepare_text_inputs()