





"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

  lowerCAmelCase__     :int											=				(
      ('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(_SCREAMING_SNAKE_CASE     ):
    os.makedirs(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Optional[int]											=				model.state_dict()

  def to_tf_var_name(_SCREAMING_SNAKE_CASE     ):
    for patt, repl in iter(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :List[Any]											=				name.replace(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    return F"bert/{name}"

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

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowerCAmelCase__     :int											=				to_tf_var_name(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :Union[str, Any]											=				state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose     ):
        lowerCAmelCase__     :int											=				torch_tensor.T
      lowerCAmelCase__     :int											=				create_tf_var(tensor=_SCREAMING_SNAKE_CASE	,  name=_SCREAMING_SNAKE_CASE	,  session=_SCREAMING_SNAKE_CASE     )
      tf.keras.backend.set_value(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :Tuple											=				session.run(_SCREAMING_SNAKE_CASE     )
      print(F"Successfully created {tf_name}: {np.allclose(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )}"     )

    lowerCAmelCase__     :Tuple											=				tf.train.Saver(tf.trainable_variables()     )
    saver.save(_SCREAMING_SNAKE_CASE	,  os.path.join(_SCREAMING_SNAKE_CASE	,  model_name.replace('-'	,  '_'     ) + '.ckpt'     )     )
def 					__A  (_SCREAMING_SNAKE_CASE=None     )   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				argparse.ArgumentParser()
  parser.add_argument('--model_name'	,  type=_SCREAMING_SNAKE_CASE	,  required=_SCREAMING_SNAKE_CASE	,  help='model name e.g. bert-base-uncased'     )
  parser.add_argument(
      '--cache_dir'	,  type=_SCREAMING_SNAKE_CASE	,  default=_SCREAMING_SNAKE_CASE	,  required=_SCREAMING_SNAKE_CASE	,  help='Directory containing pytorch model'     )
  parser.add_argument('--pytorch_model_path'	,  type=_SCREAMING_SNAKE_CASE	,  required=_SCREAMING_SNAKE_CASE	,  help='/path/to/<pytorch-model-name>.bin'     )
  parser.add_argument('--tf_cache_dir'	,  type=_SCREAMING_SNAKE_CASE	,  required=_SCREAMING_SNAKE_CASE	,  help='Directory in which to save tensorflow model'     )
  lowerCAmelCase__     :Optional[int]											=				parser.parse_args(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Union[str, Any]											=				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=_SCREAMING_SNAKE_CASE	,  ckpt_dir=args.tf_cache_dir	,  model_name=args.model_name     )


if __name__ == "__main__":
  main()







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A											=						{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""MLukeTokenizer"""]

if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mluke import MLukeTokenizer


else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
