fix(transformers_4.54_base): supplement condition of taking model as processor

[wcrzlh]

What does this PR do?

Fixes # (issue)
✅ supplement condition of taking model as processor on hf transformers 4.54 base

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[gemini-code-assist]

Summary of Changes

Hello @wcrzlh, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request, authored by wcrzlh, addresses a fix in the mindone/transformers library related to how Processor classes handle the loading of their subcomponents (like image processors and tokenizers). The primary intent is to improve the condition for taking a model as a processor by refining the dynamic module loading mechanism. Key changes include the introduction of a new static method get_possibly_dynamic_module to centralize and standardize the process of finding and instantiating these subcomponents, prioritizing mindone.transformers implementations while providing a fallback to upstream transformers modules. Additionally, a specific warning has been added for image processors when the use_fast parameter is not explicitly set, indicating an upcoming change in default behavior. The changes aim to make the component loading more robust and explicit.

Highlights

• Intent: This PR fixes and refines the logic for how Processor classes load their subcomponents (like image processors and tokenizers) from pretrained models. The goal is to ensure that mindone.transformers specific implementations are prioritized, with a graceful fallback to upstream transformers implementations, and to provide better handling for use_fast parameter in image processors.
• Key Changes: The main changes involve refactoring the component loading mechanism within _get_arguments_from_pretrained by introducing a new static helper method get_possibly_dynamic_module. This new method centralizes the logic for dynamically importing module classes, first attempting to load from mindone.transformers and then falling back to transformers_module with a warning. Additionally, a specific warning has been added for image processors when the use_fast parameter is unset, anticipating a change in default behavior in a future version.
• Detailed Changes: 1. Refactored _get_arguments_from_pretrained: The logic for determining the attribute_class (e.g., image processor, tokenizer) has been simplified. Instead of repetitive if 'ImageProcess' in class_name checks and sys.path manipulations, it now consistently calls the new cls.get_possibly_dynamic_module() method.

2. Added image_processor specific warning: A logger.warning_once is now issued when attribute_name is image_processor and use_fast is None, informing users about the upcoming default change to use_fast=True in v4.52.
3. Introduced get_possibly_dynamic_module static method: This new method handles the dynamic loading of module classes:
   • It first attempts to import the module from mindone.transformers by manipulating sys.path to include the parent directory.
   • If the module is not found in mindone.transformers (caught by AttributeError), it logs a warning about falling back to 🤗 Transformers and potential PyTorch backend mismatches.
   • It then attempts to retrieve the module from transformers_module.
   • It gracefully handles module_name is None by returning None.

• Reviewer Notes: Reviewers should focus on the correctness of the module loading fallback logic in get_possibly_dynamic_module, ensuring that mindone.transformers components are correctly prioritized and that the fallback to transformers_module is robust. Also, verify the clarity and necessity of the new warning message regarding use_fast for image processors.

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[gemini-code-assist]

Code Review

I've reviewed the changes and the refactoring in processing_utils.py is a significant improvement in terms of code clarity and robustness. The new get_possibly_dynamic_module method is much better than the previous approach of string-matching module names. I have one suggestion to fix a potential bug in the sys.path manipulation logic.

[gemini-code-assist]

There appears to be a bug in the sys.path manipulation and a confusing variable name here.

1. Path issue: The code adds the mindone directory to sys.path, but to import mindone.transformers, the parent directory of mindone (the project root) should be added. The current implementation will likely fail with an ImportError in environments where the project root is not already in sys.path.

2. Naming issue: The variable mindone_tsf_path is misleading as it holds a module object, not a path.

I suggest refactoring this block to correctly set the path and use a clearer variable name.

Suggested change

	sub_path = os.path.abspath(os.path.dirname(__file__))
	sub_path = str(Path(sub_path).parent)
	sys.path.insert(0, sub_path)
	mindone_transformers_module = importlib.import_module("mindone.transformers")
	if not hasattr(mindone_transformers_module, module_name):
	raise ValueError(
	f"Expect to have `{module_name}` registered in `mindone.transformers`, but failed to load it!"
	)
	return getattr(mindone_transformers_module, module_name)
	else:
	if sub_path not in sys.path:
	sys.path.insert(0, sub_path)
	mindone_tsf_path = importlib.import_module("mindone.transformers")
	return getattr(mindone_tsf_path, module_name)
	project_root = str(Path(__file__).resolve().parent.parent.parent)
	if project_root not in sys.path:
	sys.path.insert(0, project_root)
	mindone_transformers_module = importlib.import_module("mindone.transformers")
	return getattr(mindone_transformers_module, module_name)