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Don't fail when `LocalEntryNotFoundError` during `processor_config.json` loading (#28709) 

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2024-01-26 09:02:32 +01:00 committed by GitHub
parent 2875195887
commit 142ce68389
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1 changed files with 9 additions and 11 deletions
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20
src/transformers/processing_utils.py
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@ -317,6 +317,7 @@ class ProcessorMixin(PushToHubMixin):
user_agent=user_agent,
revision=revision,
subfolder=subfolder,
_raise_exceptions_for_missing_entries=False,
)
except EnvironmentError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
@ -331,6 +332,13 @@ class ProcessorMixin(PushToHubMixin):
f" directory containing a {PROCESSOR_NAME} file"
)
# Existing processors on the Hub created before #27761 being merged don't have `processor_config.json` (if not
# updated afterward), and we need to keep `from_pretrained` work. So here it fallbacks to the empty dict.
# (`cached_file` called using `_raise_exceptions_for_missing_entries=False` to avoid exception)
# However, for models added in the future, we won't get the expected error if this file is missing.
if resolved_processor_file is None:
return {}, kwargs
try:
# Load processor dict
with open(resolved_processor_file, "r", encoding="utf-8") as reader:
@ -456,17 +464,7 @@ class ProcessorMixin(PushToHubMixin):
kwargs["token"] = token
args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
# Existing processors on the Hub created before #27761 being merged don't have `processor_config.json` (if not
# updated afterward), and we need to keep `from_pretrained` work. So here it fallbacks to the empty dict.
# However, for models added in the future, we won't get the expected error if this file is missing.
try:
processor_dict, kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
except EnvironmentError as e:
if "does not appear to have a file named processor_config.json." in str(e):
processor_dict, kwargs = {}, kwargs
else:
raise
processor_dict, kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
return cls.from_args_and_dict(args, processor_dict, **kwargs)