from transformers import AutoModel, AutoTokenizer, AutoConfig

# Load a custom model from the Hub
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-base-de", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-de")

# Save them locally
model.save_pretrained("tmp")
tokenizer.save_pretrained("tmp")

# Now load that model again, via a Config
config = AutoConfig.from_pretrained("tmp", trust_remote_code=True)
print(config.__class__)
# <class 'transformers_modules.jinaai.jina-bert-implementation.f3ec4cf7de7e561007f27c9efc7148b0bd713f81.configuration_bert.JinaBertConfig'>
local_model = AutoModel.from_pretrained("tmp", config=config, trust_remote_code=True)

# And save it again
local_model.save_pretrained("tmp_2")
tokenizer.save_pretrained("tmp_2")

# Now load that model again, via a Config
config_2 = AutoConfig.from_pretrained("tmp_2", trust_remote_code=True)
print(config_2.__class__)
# <class 'transformers_modules.tmp_2.configuration_bert.JinaBertConfig'>
# The second time around, the config is not the same as the first time
local_model_2 = AutoModel.from_pretrained("tmp_2", config=config_2, trust_remote_code=True)
# ValueError: The model class you are passing has a `config_class` attribute that is not consistent
# with the config class you passed (model has
# <class 'transformers_modules.jinaai.jina-bert-implementation.f3ec4cf7de7e561007f27c9efc7148b0bd713f81.configuration_bert.JinaBertConfig'>
# and you passed <class 'transformers_modules.tmp_2.configuration_bert.JinaBertConfig'>. Fix one of those so they match!