
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class        UpperCAmelCase_			( unittest.TestCase  ,       _A     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Union[str, Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       load_tool("""text-classification"""    )
    self.tool.setup()
    __magic_name__				     =       load_tool("""text-classification"""			,							remote=UpperCamelCase__    )
  def 							_lowercase							( self			: int    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       self.tool("""That's quite cool"""			,							["""positive""", """negative"""]    )
    self.assertEqual(UpperCamelCase__			,							"""positive"""    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       self.remote_tool("""That's quite cool"""			,							["""positive""", """negative"""]    )
    self.assertEqual(UpperCamelCase__			,							"""positive"""    )
  def 							_lowercase							( self			: Tuple    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.tool(text="""That's quite cool"""			,							labels=["""positive""", """negative"""]    )
    self.assertEqual(UpperCamelCase__			,							"""positive"""    )





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







    """simple docstring"""






    __magic_name__				     =       self.remote_tool(text="""That's quite cool"""			,							labels=["""positive""", """negative"""]    )
    self.assertEqual(UpperCamelCase__			,							"""positive"""    )



import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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 a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

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

  # Patch sys.argv
  __magic_name__				     =       [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()

