




'''simple docstring'''




import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						lowerCAmelCase_   ( lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Dict				     =						MPNetTokenizer
  lowerCAmelCase_				:					Dict				     =						MPNetTokenizerFast
  lowerCAmelCase_				:					str				     =						True
  lowerCAmelCase_				:					int				     =						True


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   super().setUp()

   UpperCAmelCase__										=    [
       """[UNK]""",
       """[CLS]""",
       """[SEP]""",
       """[PAD]""",
       """[MASK]""",
       """want""",
       """##want""",
       """##ed""",
       """wa""",
       """un""",
       """runn""",
       """##ing""",
       """,""",
       """low""",
       """lowest""",
   ]
   UpperCAmelCase__										=    os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""] )
   with open(self.vocab_file						,		"""w"""						,		encoding="""utf-8""" ) as vocab_writer:
    vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    """UNwant\u00E9d,running"""
   UpperCAmelCase__										=    """unwanted, running"""
   return input_text, output_text


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    self.tokenizer_class(self.vocab_file )

   UpperCAmelCase__										=    tokenizer.tokenize("""UNwant\u00E9d,running""" )
   self.assertListEqual(_UpperCAmelCase						,		["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
   self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase )						,		[9, 6, 7, 12, 10, 11] )



  @slow
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    self.tokenizer_class.from_pretrained("""microsoft/mpnet-base""" )

   UpperCAmelCase__										=    tokenizer.encode("""sequence builders"""						,		add_special_tokens=_UpperCAmelCase )
   UpperCAmelCase__										=    tokenizer.encode("""multi-sequence build"""						,		add_special_tokens=_UpperCAmelCase )

   UpperCAmelCase__										=    tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase )
   UpperCAmelCase__										=    tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						,		_UpperCAmelCase )

   assert encoded_sentence == [0] + text + [2]
   assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]






'''simple docstring'''




import argparse

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


logging.set_verbosity_info()

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 print(F'''Building PyTorch model from configuration: {config}'''       )
 UpperCAmelCase__										=    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_       				=      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_       				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
