'''simple docstring'''






import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class     A__							(       lowerCamelCase_					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Optional[Any]    				=     CTRLTokenizer
   UpperCamelCase_       :						Dict    				=     False
   UpperCamelCase_       :						List[str]    				=     False




   def 			_lowerCAmelCase		( self     :		Optional[Any]			)       ->    Union[str, Any]:







      """simple docstring"""




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _UpperCAmelCase							:	Optional[int]							=   ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
      _UpperCAmelCase							:	Any							=   dict(zip(_lowercase						,     range(len(_lowercase			)			)			)			)
      _UpperCAmelCase							:	Tuple							=   ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
      _UpperCAmelCase							:	Any							=   {"unk_token": "<unk>"}

      _UpperCAmelCase							:	List[str]							=   os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["vocab_file"]			)
      _UpperCAmelCase							:	List[Any]							=   os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["merges_file"]			)
      with open(self.vocab_file						,     "w"						,     encoding="utf-8"			) as fp:
         fp.write(json.dumps(_lowercase			) + "\n"			)
      with open(self.merges_file						,     "w"						,     encoding="utf-8"			) as fp:
         fp.write("\n".join(_lowercase			)			)




   def 			_lowerCAmelCase		( self     :		List[Any]						,     **lowerCAmelCase__     :		int			)       ->    Union[str, Any]:







      """simple docstring"""




      kwargs.update(self.special_tokens_map			)
      return CTRLTokenizer.from_pretrained(self.tmpdirname						,     **_lowercase			)




   def 			_lowerCAmelCase		( self     :		str						,     lowerCAmelCase__     :		Dict			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   "adapt react readapt apt"
      _UpperCAmelCase							:	Dict							=   "adapt react readapt apt"
      return input_text, output_text




   def 			_lowerCAmelCase		( self     :		Union[str, Any]			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   CTRLTokenizer(self.vocab_file						,     self.merges_file						,     **self.special_tokens_map			)
      _UpperCAmelCase							:	Any							=   "adapt react readapt apt"
      _UpperCAmelCase							:	Dict							=   "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
      _UpperCAmelCase							:	Optional[Any]							=   tokenizer.tokenize(_lowercase			)
      self.assertListEqual(_lowercase						,     _lowercase			)

      _UpperCAmelCase							:	str							=   tokens + [tokenizer.unk_token]

      _UpperCAmelCase							:	int							=   [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase			)						,     _lowercase			)
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      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=__UpperCamelCase ,     default=1 ,     help="""Number of TPU cores to use (1 or 8)."""	)

  # positional
  parser.add_argument(
      """training_script""" ,     type=__UpperCamelCase ,     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=__UpperCamelCase	)

  return parser.parse_args()



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [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()
