Repository: huggingface/transformers
Pull request: Support FlaxPreTrainedModel to load model checkpoint from local subfolder safetensors



# What does this PR do?
Add support for loading FlaxpreTrainedModel checkpoint in safetensor format from subfolder in local directory.
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Fixes #19603 


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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- [ ] 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.
@gante and @Rocketknight1

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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

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- 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.
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Changed files:
filename: src/transformers/modeling_flax_utils.py
patch: @@ -702,14 +702,19 @@ def from_pretrained(
         if pretrained_model_name_or_path is not None:
             pretrained_model_name_or_path = str(pretrained_model_name_or_path)
             is_local = os.path.isdir(pretrained_model_name_or_path)
-            if os.path.isdir(pretrained_model_name_or_path):
+            if is_local:
                 if os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)):
                     # Load from a Flax checkpoint
                     archive_file = os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
                 elif os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_INDEX_NAME)):
                     # Load from a sharded Flax checkpoint
                     archive_file = os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_INDEX_NAME)
                     is_sharded = True
+                elif is_safetensors_available() and os.path.isfile(
+                    os.path.join(pretrained_model_name_or_path, subfolder, SAFE_WEIGHTS_NAME)
+                ):
+                    # Load from a safetensors checkpoint
+                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, SAFE_WEIGHTS_NAME)
                 elif is_safetensors_available() and os.path.isfile(
                     os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)
                 ):