
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch

    from transformers.generation import DisjunctiveConstraint




@require_torch
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Tuple    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       [[1, 2, 4], [1, 2, 3, 4]]
    __magic_name__				     =       DisjunctiveConstraint(UpperCamelCase__    )
    self.assertTrue(isinstance(dc.token_ids			,							UpperCamelCase__    )    )

    with self.assertRaises(UpperCamelCase__    ):
      DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]    )    )

    with self.assertRaises(UpperCamelCase__    ):
      DisjunctiveConstraint([torch.LongTensor([1, 2, 4]    ), torch.LongTensor([1, 2, 3, 4, 5]    )]    )
  def 							_lowercase							( self			: int    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       [[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(UpperCamelCase__    ):
      DisjunctiveConstraint(UpperCamelCase__    )  # fails here
  def 							_lowercase							( self			: Optional[Any]    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       [[1, 2, 3], [1, 2, 4]]

    __magic_name__				     =       DisjunctiveConstraint(UpperCamelCase__    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(1    )
    __magic_name__				     =       stepped is True and completed is False and reset is False
    self.assertTrue(UpperCamelCase__    )
    self.assertTrue(not dc.completed    )
    self.assertTrue(dc.current_seq == [1]    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(2    )
    __magic_name__				     =       stepped is True and completed is False and reset is False
    self.assertTrue(UpperCamelCase__    )
    self.assertTrue(not dc.completed    )
    self.assertTrue(dc.current_seq == [1, 2]    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(3    )
    __magic_name__				     =       stepped is True and completed is True and reset is False
    self.assertTrue(UpperCamelCase__    )
    self.assertTrue(dc.completed    )  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]    )





  def 							_lowercase							( self			: Dict    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    __magic_name__				     =       DisjunctiveConstraint(UpperCamelCase__    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(1    )
    self.assertTrue(not dc.completed    )
    self.assertTrue(dc.current_seq == [1]    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(2    )
    self.assertTrue(not dc.completed    )
    self.assertTrue(dc.current_seq == [1, 2]    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(4    )
    self.assertTrue(not dc.completed    )
    self.assertTrue(dc.current_seq == [1, 2, 4]    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(5    )
    self.assertTrue(dc.completed    )  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]    )

    dc.reset()

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(1    )
    self.assertTrue(not dc.completed    )
    self.assertTrue(dc.remaining() == 3    )
    self.assertTrue(dc.current_seq == [1]    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(2    )
    self.assertTrue(not dc.completed    )
    self.assertTrue(dc.remaining() == 2    )
    self.assertTrue(dc.current_seq == [1, 2]    )

    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       dc.update(5    )
    self.assertTrue(dc.completed    )  # Completed!
    self.assertTrue(dc.remaining() == 0    )
    self.assertTrue(dc.current_seq == [1, 2, 5]    )







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

import torch_xla.distributed.xla_multiprocessing as xmp





def 		__A		(	)      ->       Any:
	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=__lowerCamelCase							,			default=1							,			help="""Number of TPU cores to use (1 or 8)."""    )

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

	return parser.parse_args()





def 		__A		(	)      ->       Union[str, Any]:
	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(__lowerCamelCase    )

	# 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()

