






'''simple docstring'''



import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =PhobertTokenizer
      __a	       =False




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __a					  =     ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
         __a					  =     dict(zip(lowerCamelCase			, range(len(lowerCamelCase   )   )   )   )
         __a					  =     ['#version: 0.2', 'l à</w>']
         __a					  =     {'unk_token': '<unk>'}

         __a					  =     os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES['vocab_file']   )
         __a					  =     os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES['merges_file']   )

         with open(self.vocab_file			, 'w'			, encoding='utf-8'   ) as fp:
            for token in vocab_tokens:
               fp.write(F"""{token} {vocab_tokens[token]}\n"""   )
         with open(self.merges_file			, 'w'			, encoding='utf-8'   ) as fp:
            fp.write('\n'.join(lowerCamelCase   )   )




      def 			__UpperCamelCase				(	self			, **lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         kwargs.update(self.special_tokens_map   )
         return PhobertTokenizer.from_pretrained(self.tmpdirname			, **lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     'Tôi là VinAI Research'
         __a					  =     'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
         return input_text, output_text






      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         __a					  =     PhobertTokenizer(self.vocab_file			, self.merges_file			, **self.special_tokens_map   )
         __a					  =     'Tôi là VinAI Research'
         __a					  =     'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
         __a					  =     tokenizer.tokenize(lowerCamelCase   )
         print(lowerCamelCase   )
         self.assertListEqual(lowerCamelCase			, lowerCamelCase   )

         __a					  =     tokens + [tokenizer.unk_token]

         __a					  =     [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase   )			, lowerCamelCase   )







'''simple docstring'''



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__     )     )
   torch.save(model.state_dict(),       SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--rembert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained RemBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)