Tensorflow how to change hub.Module() to local folder - tensorflow

Question

how can I change:
BERT_MODEL = "https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1"
def create_tokenizer_from_hub_module():
"""Get the vocab file and casing info from the Hub module."""
with tf.Graph().as_default():
bert_module = hub.Module(BERT_MODEL)
tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
with tf.Session() as sess:
vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
tokenization_info["do_lower_case"]])
return bert.tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
tokenizer = create_tokenizer_from_hub_module()
So that I can load a local BERT model without the hub.Module() call as it doesn't work with a local path.
I downloaded a different TF1 pre-trained model from a different website, unzipped it and stored in /test/module/.
If I change above BERT_MODEL = "/test/module" how would I need to change the rest? I now get string errors as tokenization_info = bert_module(signature="tokenization_info", as_dict=True) doesn't work.
Help please I am new to TF - note I need to use TF1, not TF2.
Note: on suggestion below I get:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-11-a98e44536f87> in <module>()
9 return vocab_file, do_lower_case
10
---> 11 print(get_bert_tokenizer_info("/tmp/local_copy"))
12 # Will print: (b'/tmp/local_copy/assets/vocab.txt', False)
4 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_hub/registry.py in __call__(self, *args, **kwargs)
43 raise RuntimeError(
44 "Missing implementation that supports: %s(*%r, **%r)" % (
---> 45 self._name, args, kwargs))
46
47
RuntimeError: Missing implementation that supports: loader(*('/tmp/local_copy',), **{})

Answer 1

hub.Module works with local uncompressed paths, so you can change BERT_MODEL to another path and reuse the same code.
Example:
Create local copy of the module:
mkdir /tmp/local_copy
wget "https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1?tf-hub-format=compressed" -O "module.tar.gz"
tar -C /tmp/local_copy -xzvf module.tar.gz
Use the local copy of the module:
import tensorflow as tf
import tensorflow_hub as hub
def get_bert_tokenizer_info(bert_module):
"""Get the vocab file and casing info from the Hub module."""
with tf.Graph().as_default():
bert_module = hub.Module(bert_module)
tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
with tf.Session() as sess:
vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
tokenization_info["do_lower_case"]])
return vocab_file, do_lower_case
print(get_bert_tokenizer_info("/tmp/local_copy"))
# Will print: (b'/tmp/local_copy/assets/vocab.txt', False)