"""simple docstring"""







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

      UpperCAmelCase          =	(
          ("""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(_snake_case							):
            os.makedirs(_snake_case							)

      UpperCAmelCase          =	model.state_dict()

      def to_tf_var_name(_snake_case							):
            for patt, repl in iter(_snake_case							):
                  UpperCAmelCase          =	name.replace(_snake_case				,	_snake_case							)
            return F'''bert/{name}'''

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

      tf.reset_default_graph()
      with tf.Session() as session:
            for var_name in state_dict:
                  UpperCAmelCase          =	to_tf_var_name(_snake_case							)
                  UpperCAmelCase          =	state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose							):
                        UpperCAmelCase          =	torch_tensor.T
                  UpperCAmelCase          =	create_tf_var(tensor=_snake_case				,	name=_snake_case				,	session=_snake_case							)
                  tf.keras.backend.set_value(_snake_case				,	_snake_case							)
                  UpperCAmelCase          =	session.run(_snake_case							)
                  print(F'''Successfully created {tf_name}: {np.allclose(_snake_case				,	_snake_case							)}'''							)

            UpperCAmelCase          =	tf.train.Saver(tf.trainable_variables()							)
            saver.save(_snake_case				,	os.path.join(_snake_case				,	model_name.replace("""-"""				,	"""_"""							) + """.ckpt"""							)							)

def _a			(		_snake_case=None							):







      """simple docstring"""






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

      UpperCAmelCase          =	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=_snake_case				,	ckpt_dir=args.tf_cache_dir				,	model_name=args.model_name							)


if __name__ == "__main__":
  main()











"""simple docstring"""







import numpy as np

def _a			(		_snake_case							):







      """simple docstring"""






      return 1 / (1 + np.exp(-vector							))


if __name__ == "__main__":
  import doctest

  doctest.testmod()



