


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def       _a	(					SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					Union[str, Any]  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= TaConfig.from_json_file(SCREAMING_SNAKE_CASE  )
 print(F"Building PyTorch model from configuration: {config}"  )
 UpperCamelCase__       :     List[str] 							= TaForConditionalGeneration(SCREAMING_SNAKE_CASE  )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

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


if __name__ == "__main__":
       __UpperCamelCase							:							int    							=					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(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       __UpperCamelCase							:							Optional[int]    							=					parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class    __magic_name__						:
     A:		str



class    __magic_name__						(  unittest.TestCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= get_tests_dir('''fixtures/test_sentencepiece.model'''      )
      UpperCamelCase__       :     Any 							= get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''      )

      UpperCamelCase__       :     List[str] 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     Optional[int] 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						0      )

      UpperCamelCase__       :     Tuple 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     str 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						1      )

      self.assertIn(
          '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
          ''' which is not implemented in the fast tokenizers.'''				,						str(w[0].message      )				,						)


