






import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						Optional[Any]										=							PhobertTokenizer
      _a							:						Dict										=							False
      def 						lowercase__					(     self   ):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       snake_case__  :    Union[str, Any]							       =["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
       snake_case__  :    List[str]							       =dict(zip(a				,			range(len(a   )   )   )   )
       snake_case__  :    List[Any]							       =["""#version: 0.2""", """l à</w>"""]
       snake_case__  :    List[str]							       ={"""unk_token""": """<unk>"""}

       snake_case__  :    Union[str, Any]							       =os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]   )
       snake_case__  :    Optional[Any]							       =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(a   )   )
      def 						lowercase__					(     self				,			**a   ):
       kwargs.update(self.special_tokens_map   )
       return PhobertTokenizer.from_pretrained(self.tmpdirname				,			**a   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       ="""Tôi là VinAI Research"""
       snake_case__  :    Tuple							       ="""T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
       return input_text, output_text



      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =PhobertTokenizer(self.vocab_file				,			self.merges_file				,			**self.special_tokens_map   )
       snake_case__  :    Any							       ="""Tôi là VinAI Research"""
       snake_case__  :    Dict							       ="""T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
       snake_case__  :    Union[str, Any]							       =tokenizer.tokenize(a   )
       print(a   )
       self.assertListEqual(a				,			a   )

       snake_case__  :    Tuple							       =tokens + [tokenizer.unk_token]

       snake_case__  :    Dict							       =[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(a   )				,			a   )













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   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(
           """--big_bird_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT 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."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





