


'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 			_lowercase       (				):
 __A  :		Optional[int]       =			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 			_lowercase       (				):
 __A  :		List[str]       =			parse_args()

 # Import training_script as a module.
 __A  :		str       =			Path(args.training_script			)
 sys.path.append(str(script_fpath.parent.resolve()			)			)
 __A  :		List[str]       =			script_fpath.stem
 __A  :		Any       =			importlib.import_module(_UpperCAmelCase			)

 # Patch sys.argv
 __A  :		Dict       =			[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()










'''simple docstring'''
from __future__ import annotations

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			order

  # a_{0} ... a_{k}
  __A  :		str       =			[1.0] + [0.0] * order
  # b_{0} ... b_{k}
  __A  :		Any       =			[1.0] + [0.0] * order

  # x[n-1] ... x[n-k]
  __A  :		Optional[int]       =			[0.0] * self.order
  # y[n-1] ... y[n-k]
  __A  :		Dict       =			[0.0] * self.order


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if len(__lowercase						) < self.order:
   __A  :		List[Any]       =			[1.0, *a_coeffs]

  if len(__lowercase						) != self.order + 1:
   __A  :		Tuple       =			(
       F"""Expected a_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  if len(__lowercase						) != self.order + 1:
   __A  :		List[Any]       =			(
       F"""Expected b_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  __A  :		Optional[int]       =			a_coeffs
  __A  :		Optional[int]       =			b_coeffs







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			0.0

  # Start at index 1 and do index 0 at the end.
  for i in range(1      ,      self.order + 1						):
   result += (
       self.b_coeffs[i] * self.input_history[i - 1]
       - self.a_coeffs[i] * self.output_history[i - 1]
   )

  __A  :		Optional[int]       =			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

  __A  :		List[str]       =			self.input_history[:-1]
  __A  :		Optional[int]       =			self.output_history[:-1]

  __A  :		Optional[int]       =			sample
  __A  :		Dict       =			result

  return result






