added file to convert pytorch->tf

pytorch_pretrained_bert/convert_hf_checkpoint_to_tf.py

@@ -21,26 +21,6 @@ import numpy as np
 from pytorch_pretrained_bert.modeling import BertConfig, BertModel
 
 
-# def __get_var_names(config):
-#
-#     models = {
-#         'BertModel': BertModel(config),
-#         'BertForMaskedLM': BertForMaskedLM(config),
-#         'BertForPreTraining': BertForPreTraining(config),
-#         'BertForMultipleChoice': BertForMultipleChoice(config, num_choices=100),
-#         'BertForNextSentencePrediction': BertForNextSentencePrediction(config),
-#         'BertForSequenceClassification': BertForSequenceClassification(config, num_labels=100),
-#         'BertForQuestionAnswering': BertForQuestionAnswering(config)
-#     }
-#
-#     for name, model in models.items():
-#         state_dict = model.state_dict()
-#         torch_vars = []
-#         for var_ in state_dict:
-#             torch_vars.append(var_ + ', ' + str(tuple(state_dict[var_].shape)))
-#         json.dump(torch_vars, fp=open('torch_var_names_{}.json'.format(name), 'w'), indent=3)
-
-
 
 def convert_hf_checkpoint_to_tf(model:BertModel, ckpt_dir:str):
 
@@ -58,8 +38,7 @@ def convert_hf_checkpoint_to_tf(model:BertModel, ckpt_dir:str):
         N BertForQuestionAnswering
 
     Note:
-        TF isn't & shouldn't be a package-level requirement; this
-        feature is requested enough  to warrant a local import.
+        To keep TF out of package-level requirements, tf is imported locally.
     """
 
     import tensorflow as tf