





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   )      ->   Any:
 __lowerCAmelCase					:	List[Any]      =       ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

 __lowerCAmelCase					:	Optional[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					:	Dict      =       model.state_dict()

 def to_tf_var_name(SCREAMING_SNAKE_CASE		:List[str]   ):
  for patt, repl in iter(SCREAMING_SNAKE_CASE   ):
   __lowerCAmelCase					:	Union[str, Any]      =       name.replace(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  return F'''bert/{name}'''

 def create_tf_var(SCREAMING_SNAKE_CASE		:Optional[Any]   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:str   ):
  __lowerCAmelCase					:	str      =       tf.dtypes.as_dtype(tensor.dtype   )
  __lowerCAmelCase					:	str      =       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					:	Dict      =       to_tf_var_name(SCREAMING_SNAKE_CASE   )
   __lowerCAmelCase					:	Dict      =       state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose   ):
    __lowerCAmelCase					:	Tuple      =       torch_tensor.T
   __lowerCAmelCase					:	Tuple      =       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					:	Optional[int]      =       session.run(SCREAMING_SNAKE_CASE   )
   print(F'''Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )}'''   )

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple=None   )      ->   int:
 __lowerCAmelCase					:	Any      =       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					:	Union[str, Any]      =       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()






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()