
from __future__ import annotations





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Dict		,    lowerCAmelCase__							:int     )  ->None:
										lowercase         =      order

										# a_{0} ... a_{k}
										lowercase         =      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										lowercase         =      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										lowercase         =      [0.0] * self.order
										# y[n-1] ... y[n-k]
										lowercase         =      [0.0] * self.order





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:list[float]		,    lowerCAmelCase__							:list[float]     )  ->None:
										if len(lowerCAmelCase__     ) < self.order:
														lowercase         =      [1.0, *a_coeffs]

										if len(lowerCAmelCase__     ) != self.order + 1:
														lowercase         =      (
														    F'''Expected a_coeffs to have {self.order + 1} elements '''
														    F'''for {self.order}-order filter, got {len(lowerCAmelCase__     )}'''
														)
														raise ValueError(lowerCAmelCase__     )

										if len(lowerCAmelCase__     ) != self.order + 1:
														lowercase         =      (
														    F'''Expected b_coeffs to have {self.order + 1} elements '''
														    F'''for {self.order}-order filter, got {len(lowerCAmelCase__     )}'''
														)
														raise ValueError(lowerCAmelCase__     )

										lowercase         =      a_coeffs
										lowercase         =      b_coeffs





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:float     )  ->float:
										lowercase         =      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]
														)

										lowercase         =      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										lowercase         =      self.input_history[:-1]
										lowercase         =      self.output_history[:-1]

										lowercase         =      sample
										lowercase         =      result

										return result


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

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


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

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

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

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

				# Patch sys.argv
				lowercase         =      [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()
