




"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     a     =				(
         ("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)

     a     =				model.state_dict()

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

     def create_tf_var(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase):
          a     =				tf.dtypes.as_dtype(tensor.dtype)
          a     =				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:
               a     =				to_tf_var_name(__A)
               a     =				state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose):
                    a     =				torch_tensor.T
               a     =				create_tf_var(tensor=__A		, name=__A		, session=__A)
               tf.keras.backend.set_value(__A		, __A)
               a     =				session.run(__A)
               print(f'''Successfully created {tf_name}: {np.allclose(__A		, __A)}''')

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase=None)      ->					str:
     a     =				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")
     a     =				parser.parse_args(__A)

     a     =				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()







lowercase__				:      List[Any]	   = {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase__				:      str	   = {value: key for key, value in encode_dict.items()}

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				""
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception("encode() accepts only letters of the alphabet and spaces")
     return encoded

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     if set(__UpperCamelCase) - {"A", "B", " "} != set():
          raise Exception("decode() accepts only 'A', 'B' and spaces")
     a     =				""
     for word in coded.split():
          while len(__UpperCamelCase) != 0:
               decoded += decode_dict[word[:5]]
               a     =				word[5:]
          decoded += " "
     return decoded.strip()


if __name__ == "__main__":
   from doctest import testmod

   testmod()

