





'''simple docstring'''






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

      _lowerCAmelCase					=				(
          ("""layer.""", """layer_"""),
          ("""word_embeddings.weight""", """word_embeddings"""),
          ("""position_embeddings.weight""", """position_embeddings"""),
          ("""token_type_embeddings.weight""", """token_type_embeddings"""),
          (""".""", """/"""),
          ("""LayerNorm/weight""", """LayerNorm/gamma"""),
          ("""LayerNorm/bias""", """LayerNorm/beta"""),
          ("""weight""", """kernel"""),
      )

      if not os.path.isdir(lowerCAmelCase    ):
            os.makedirs(lowerCAmelCase    )

      _lowerCAmelCase					=				model.state_dict()

      def to_tf_var_name(lowerCAmelCase    ):
            for patt, repl in iter(lowerCAmelCase    ):
                  _lowerCAmelCase					=				name.replace(lowerCAmelCase				,      lowerCAmelCase    )
            return f"bert/{name}"

      def create_tf_var(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):
            _lowerCAmelCase					=				tf.dtypes.as_dtype(tensor.dtype    )
            _lowerCAmelCase					=				tf.get_variable(dtype=lowerCAmelCase				,      shape=tensor.shape				,      name=lowerCAmelCase				,      initializer=tf.zeros_initializer()    )
            session.run(tf.variables_initializer([tf_var]    )    )
            session.run(lowerCAmelCase    )
            return tf_var

      tf.reset_default_graph()
      with tf.Session() as session:
            for var_name in state_dict:
                  _lowerCAmelCase					=				to_tf_var_name(lowerCAmelCase    )
                  _lowerCAmelCase					=				state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose    ):
                        _lowerCAmelCase					=				torch_tensor.T
                  _lowerCAmelCase					=				create_tf_var(tensor=lowerCAmelCase				,      name=lowerCAmelCase				,      session=lowerCAmelCase    )
                  tf.keras.backend.set_value(lowerCAmelCase				,      lowerCAmelCase    )
                  _lowerCAmelCase					=				session.run(lowerCAmelCase    )
                  print(f"Successfully created {tf_name}: {np.allclose(lowerCAmelCase				,      lowerCAmelCase    )}"    )

            _lowerCAmelCase					=				tf.train.Saver(tf.trainable_variables()    )
            saver.save(lowerCAmelCase				,      os.path.join(lowerCAmelCase				,      model_name.replace("""-"""				,      """_"""    ) + """.ckpt"""    )    )
def   UpperCamelCase__ (						lowerCAmelCase=None    ):





      """simple docstring"""


      _lowerCAmelCase					=				argparse.ArgumentParser()
      parser.add_argument("""--model_name"""				,      type=lowerCAmelCase				,      required=lowerCAmelCase				,      help="""model name e.g. bert-base-uncased"""    )
      parser.add_argument(
          """--cache_dir"""				,      type=lowerCAmelCase				,      default=lowerCAmelCase				,      required=lowerCAmelCase				,      help="""Directory containing pytorch model"""    )
      parser.add_argument("""--pytorch_model_path"""				,      type=lowerCAmelCase				,      required=lowerCAmelCase				,      help="""/path/to/<pytorch-model-name>.bin"""    )
      parser.add_argument("""--tf_cache_dir"""				,      type=lowerCAmelCase				,      required=lowerCAmelCase				,      help="""Directory in which to save tensorflow model"""    )
      _lowerCAmelCase					=				parser.parse_args(lowerCAmelCase    )

      _lowerCAmelCase					=				BertModel.from_pretrained(
          pretrained_model_name_or_path=args.model_name				,      state_dict=torch.load(args.pytorch_model_path    )				,      cache_dir=args.cache_dir				,      )

      convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase				,      ckpt_dir=args.tf_cache_dir				,      model_name=args.model_name    )


if __name__ == "__main__":
      main()













'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				""""""
      for word_or_phrase in separated:
            if not isinstance(lowerCAmelCase				,      lowerCAmelCase    ):
                  raise Exception("""join() accepts only strings to be joined"""    )
            joined += word_or_phrase + separator
      return joined.strip(lowerCAmelCase    )


if __name__ == "__main__":
      from doctest import testmod

      testmod()






