



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

import torch_xla.distributed.xla_multiprocessing as xmp


def      UpperCamelCase(    ):
		lowerCAmelCase_     :	List[Any]							   =					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      UpperCamelCase(    ):
		lowerCAmelCase_     :	Union[str, Any]							   =					parse_args()

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

		# Patch sys.argv
		lowerCAmelCase_     :	List[str]							   =					[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()








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

	while True:
		if diff_function(a__       ) != 0:
			__A :			Optional[int]		    =			prev_guess - multiplicity * func(a__       ) / diff_function(
			    a__       )
		else:
			raise ZeroDivisionError("""Could not find root"""       ) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess       ) < precision:
			return next_guess

		__A :			List[Any]		    =			next_guess


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

			# Find root of polynomial
			# Find fourth Root of 5
			print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

			# Find value of e
			print(
			    '''The root of log(y) - 1 = 0 is ''',
			    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
			)

			# Exponential Roots
			print(
			    '''The root of exp(x) - 1 = 0 is''',
			    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
			)

			# Find root of cos(x)
			print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")





