code: 
  
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		ArgumentParser(
  						    description=(
  						        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
  						    )      )
  
  						# Optional arguments for the launch helper
  						parser.add_argument("""--num_cores"""       ,    type=_A       ,    default=1       ,    help="""Number of TPU cores to use (1 or 8)."""      )
  
  						# positional
  						parser.add_argument(
  						    """training_script"""       ,    type=_A       ,    help=(
  						        """The full path to the single TPU training """
  						        """program/script to be launched in parallel, """
  						        """followed by all the arguments for the """
  						        """training script"""
  						    )       ,    )
  
  						# rest from the training program
  						parser.add_argument("""training_script_args"""       ,    nargs=_A      )
  
  						return parser.parse_args()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(_A      )
  
  						# Patch sys.argv
  						a_					=		[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores      )]
  
  						xmp.spawn(mod._mp_fn       ,    args=()       ,    nprocs=args.num_cores      )
  
  
  if __name__ == "__main__":
  	main()
style_context: 
  
  
  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        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		MPNetTokenizer
  							snake_case_        =		MPNetTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		True
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		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 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		"""UNwant\u00E9d,running"""
  													a_					=		"""unwanted, running"""
  													return input_text, output_text
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class(self.vocab_file		)
  
  													a_					=		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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)
  
  													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
  													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)
  
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													assert encoded_sentence == [0] + text + [2]
  													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]