

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def      snake_case_				(__A					:						BertModel ,       __A					:						str ,       __A					:						str					)      ->     Any:
   __lowerCAmelCase  :      Optional[Any]    =						("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

   __lowerCAmelCase  :      Tuple    =						(
       ("""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(__A					):
      os.makedirs(__A					)

   __lowerCAmelCase  :      str    =						model.state_dict()

   def to_tf_var_name(__A					:						str					):
      for patt, repl in iter(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						name.replace(__A ,       __A					)
      return f'''bert/{name}'''

   def create_tf_var(__A					:						np.ndarray ,       __A					:						str ,       __A					:						tf.Session					):
      __lowerCAmelCase  :      Optional[Any]    =						tf.dtypes.as_dtype(tensor.dtype					)
      __lowerCAmelCase  :      List[Any]    =						tf.get_variable(dtype=__A ,       shape=tensor.shape ,       name=__A ,       initializer=tf.zeros_initializer()					)
      session.run(tf.variables_initializer([tf_var]					)					)
      session.run(__A					)
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         __lowerCAmelCase  :      Tuple    =						to_tf_var_name(__A					)
         __lowerCAmelCase  :      Any    =						state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose					):
            __lowerCAmelCase  :      Dict    =						torch_tensor.T
         __lowerCAmelCase  :      str    =						create_tf_var(tensor=__A ,       name=__A ,       session=__A					)
         tf.keras.backend.set_value(__A ,       __A					)
         __lowerCAmelCase  :      Optional[int]    =						session.run(__A					)
         print(f'''Successfully created {tf_name}: {np.allclose(__A ,       __A					)}'''					)

      __lowerCAmelCase  :      Dict    =						tf.train.Saver(tf.trainable_variables()					)
      saver.save(__A ,       os.path.join(__A ,       model_name.replace("""-""" ,       """_"""					) + """.ckpt"""					)					)





def      snake_case_				(__A					:						int=None					)      ->     Optional[int]:
   __lowerCAmelCase  :      str    =						argparse.ArgumentParser()
   parser.add_argument("""--model_name""" ,       type=__A ,       required=__A ,       help="""model name e.g. bert-base-uncased"""					)
   parser.add_argument(
       """--cache_dir""" ,       type=__A ,       default=__A ,       required=__A ,       help="""Directory containing pytorch model"""					)
   parser.add_argument("""--pytorch_model_path""" ,       type=__A ,       required=__A ,       help="""/path/to/<pytorch-model-name>.bin"""					)
   parser.add_argument("""--tf_cache_dir""" ,       type=__A ,       required=__A ,       help="""Directory in which to save tensorflow model"""					)
   __lowerCAmelCase  :      int    =						parser.parse_args(__A					)

   __lowerCAmelCase  :      str    =						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=__A ,       ckpt_dir=args.tf_cache_dir ,       model_name=args.model_name					)


if __name__ == "__main__":
   main()





__UpperCAmelCase								=			"""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

__UpperCAmelCase								=			[{"""type""": """code""", """content""": INSTALL_CONTENT}]
__UpperCAmelCase								=			{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


