"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """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(lowercase						):
    os.makedirs(lowercase						)

  _UpperCAmelCase         =					model.state_dict()

  def to_tf_var_name(lowercase						):
    for patt, repl in iter(lowercase						):
      _UpperCAmelCase         =					name.replace(lowercase      ,lowercase						)
    return f'''bert/{name}'''

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

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      _UpperCAmelCase         =					to_tf_var_name(lowercase						)
      _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=lowercase      ,name=lowercase      ,session=lowercase						)
      tf.keras.backend.set_value(lowercase      ,lowercase						)
      _UpperCAmelCase         =					session.run(lowercase						)
      print(f'''Successfully created {tf_name}: {np.allclose(lowercase      ,lowercase						)}'''						)

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






def 			__UpperCAmelCase    (	lowercase=None						):







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

  _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=lowercase      ,ckpt_dir=args.tf_cache_dir      ,model_name=args.model_name						)


if __name__ == "__main__":
    main()

"""simple docstring"""


import warnings


warnings.warn(
    """memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
    """`from accelerate import find_executable_batch_size` to avoid this warning.""",
    FutureWarning,
)
