
"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

   UpperCAmelCase_					:					Union[str, Any]			     =    (
       ('''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  )

   UpperCAmelCase_					:					Dict			     =    model.state_dict()

   def to_tf_var_name(__lowerCamelCase  ):
      for patt, repl in iter(__a  ):
         UpperCAmelCase_					:					Optional[int]			     =    name.replace(__a,		__a  )
      return f"""bert/{name}"""

   def create_tf_var(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
      UpperCAmelCase_					:					List[str]			     =    tf.dtypes.as_dtype(tensor.dtype  )
      UpperCAmelCase_					:					Optional[int]			     =    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:
         UpperCAmelCase_					:					Any			     =    to_tf_var_name(__a  )
         UpperCAmelCase_					:					Any			     =    state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose  ):
            UpperCAmelCase_					:					List[str]			     =    torch_tensor.T
         UpperCAmelCase_					:					List[Any]			     =    create_tf_var(tensor=__a,		name=__a,		session=__a  )
         tf.keras.backend.set_value(__a,		__a  )
         UpperCAmelCase_					:					Optional[int]			     =    session.run(__a  )
         print(f"""Successfully created {tf_name}: {np.allclose(__a,		__a  )}"""  )

      UpperCAmelCase_					:					str			     =    tf.train.Saver(tf.trainable_variables()  )
      saver.save(__a,		os.path.join(__a,		model_name.replace("-",		"_"  ) + ".ckpt"  )  )







def 	__a     (		__lowerCamelCase=None  ):
   UpperCAmelCase_					:					List[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"  )
   UpperCAmelCase_					:					Dict			     =    parser.parse_args(__a  )

   UpperCAmelCase_					:					Dict			     =    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()










"""simple docstring"""






from __future__ import annotations




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  int = 0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  int    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''encrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''decrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")


