





"""simple docstring"""


import torch
from torch import autograd



class    lowercase_ (   autograd.Function							):

       @staticmethod
       def 						_SCREAMING_SNAKE_CASE(	__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= inputs.numel()
        snake_case__										= int(0.005 * nb_elems   ) + 1
        if sigmoid:
         snake_case__										= (torch.sigmoid(__SCREAMING_SNAKE_CASE   ) > threshold).type(inputs.type()   )
        else:
         snake_case__										= (inputs > threshold).type(inputs.type()   )
        if mask.sum() < nb_min:
         # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
         snake_case__										= inputs.flatten().kthvalue(max(nb_elems - nb_min      ,    1   )   ).values
         snake_case__										= (inputs > k_threshold).type(inputs.type()   )
        return mask

       @staticmethod
       def 						_SCREAMING_SNAKE_CASE(	__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return gradOutput, None, None



class    lowercase_ (   autograd.Function							):

       @staticmethod
       def 						_SCREAMING_SNAKE_CASE(	__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= inputs.clone()
        snake_case__							,      snake_case__										= inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE   )
        snake_case__										= int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        snake_case__										= mask.flatten()
        snake_case__										= 0
        snake_case__										= 1
        return mask

       @staticmethod
       def 						_SCREAMING_SNAKE_CASE(	__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return gradOutput, None



class    lowercase_ (   lowerCAmelCase_							):

       @staticmethod
       def 						_SCREAMING_SNAKE_CASE(	__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= inputs.clone()
        snake_case__							,      snake_case__										= inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE   )
        snake_case__										= int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        snake_case__										= mask.flatten()
        snake_case__										= 0
        snake_case__										= 1
        return mask








'''simple docstring'''







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

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				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=lowercase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""	)

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

			return parser.parse_args()





def    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

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

			# Patch sys.argv
			__lowerCAmelCase											=				[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()

