
import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__    :		List[Any]          =   get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")



class       _snake_case   (	UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     Optional[int]        =			BartphoTokenizer
 __lowerCAmelCase  :     List[Any]        =			False
 __lowerCAmelCase  :     Any        =			True


 def      lowercase__     (					self):


   '''simple docstring'''




   super().setUp()

   lowercase__  :   Dict    		=      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
   lowercase__  :   Any    		=      dict(zip(SCREAMING_SNAKE_CASE_					,  range(len(SCREAMING_SNAKE_CASE_))))
   lowercase__  :   Union[str, Any]    		=      {"""unk_token""": """<unk>"""}

   lowercase__  :   Dict    		=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["""monolingual_vocab_file"""])
   with open(self.monolingual_vocab_file					,  """w"""					,  encoding="""utf-8""") as fp:
     for token in vocab_tokens:
       fp.write(f'{token} {vocab_tokens[token]}\n')

   lowercase__  :   str    		=      BartphoTokenizer(SCREAMING_SNAKE_CASE_					,  self.monolingual_vocab_file					,  **self.special_tokens_map)
   tokenizer.save_pretrained(self.tmpdirname)


 def      lowercase__     (					self					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   kwargs.update(self.special_tokens_map)
   return BartphoTokenizer.from_pretrained(self.tmpdirname					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      """This is a là test"""
   lowercase__  :   Optional[int]    		=      """This is a<unk><unk> test"""
   return input_text, output_text



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      BartphoTokenizer(SCREAMING_SNAKE_CASE_					,  self.monolingual_vocab_file					,  **self.special_tokens_map)
   lowercase__  :   int    		=      """This is a là test"""
   lowercase__  :   List[Any]    		=      """▁This ▁is ▁a ▁l à ▁t est""".split()
   lowercase__  :   Optional[Any]    		=      tokenizer.tokenize(SCREAMING_SNAKE_CASE_)
   self.assertListEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      tokens + [tokenizer.unk_token]
   lowercase__  :   Optional[int]    		=      [4, 5, 6, 3, 3, 7, 8, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)					,  SCREAMING_SNAKE_CASE_)








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Dict    		=      ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

  lowercase__  :   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(lowercase_   ):
    os.makedirs(lowercase_   )

  lowercase__  :   Optional[Any]    		=      model.state_dict()

  def to_tf_var_name(lowercase_   ):
    for patt, repl in iter(lowercase_   ):
      lowercase__  :   str    		=      name.replace(lowercase_ , lowercase_   )
    return F'bert/{name}'

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

    lowercase__  :   List[Any]    		=      tf.train.Saver(tf.trainable_variables()   )
    saver.save(lowercase_ , os.path.join(lowercase_ , model_name.replace("""-""" , """_"""   ) + """.ckpt"""   )   )

def 		UpperCamelCase	(      lowercase_=None   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      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"""   )
  lowercase__  :   List[str]    		=      parser.parse_args(lowercase_   )

  lowercase__  :   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=lowercase_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name   )


if __name__ == "__main__":
     main()






