

"""simple docstring"""


import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=() ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE="no" ,			__SCREAMING_SNAKE_CASE="29500"			)     ->     Optional[Any]:
		__lowerCAmelCase:	Any   				=      False
		__lowerCAmelCase:	int   				=      False
		if any(key.startswith("KAGGLE"			) for key in os.environ.keys()			):
				__lowerCAmelCase:	Tuple   				=      True
		elif "IPython" in sys.modules:
				__lowerCAmelCase:	Dict   				=      "google.colab" in str(sys.modules["IPython"].get_ipython()			)

		try:
				__lowerCAmelCase:	Union[str, Any]   				=      PrecisionType(mixed_precision.lower()			)
		except ValueError:
				raise ValueError(
				    F"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."			)

		if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME" ,			__SCREAMING_SNAKE_CASE			) is not None):
				# TPU launch
				import torch_xla.distributed.xla_multiprocessing as xmp

				if len(AcceleratorState._shared_state			) > 0:
						raise ValueError(
						    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
						    "your training function. Restart your notebook and make sure no cells initializes an "
						    "`Accelerator`."			)
				if num_processes is None:
						__lowerCAmelCase:	Union[str, Any]   				=      8

				__lowerCAmelCase:	Union[str, Any]   				=      PrepareForLaunch(__SCREAMING_SNAKE_CASE ,			distributed_type="TPU"			)
				print(F"Launching a training on {num_processes} TPU cores."			)
				xmp.spawn(__SCREAMING_SNAKE_CASE ,			args=__SCREAMING_SNAKE_CASE ,			nprocs=__SCREAMING_SNAKE_CASE ,			start_method="fork"			)
		elif in_colab:
				# No need for a distributed launch otherwise as it's either CPU or one GPU.
				if torch.cuda.is_available():
						print("Launching training on one GPU."			)
				else:
						print("Launching training on one CPU."			)
				function(*__SCREAMING_SNAKE_CASE			)
		else:
				if num_processes is None:
						raise ValueError(
						    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."			)

				if num_processes > 1:
						# Multi-GPU launch
						from torch.multiprocessing import start_processes
						from torch.multiprocessing.spawn import ProcessRaisedException

						if len(AcceleratorState._shared_state			) > 0:
								raise ValueError(
								    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
								    "inside your training function. Restart your notebook and make sure no cells initializes an "
								    "`Accelerator`."			)

						if torch.cuda.is_initialized():
								raise ValueError(
								    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
								    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
								    "function."			)

						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=__SCREAMING_SNAKE_CASE ,			master_addr="127.0.01" ,			master_port=__SCREAMING_SNAKE_CASE ,			mixed_precision=__SCREAMING_SNAKE_CASE			):
								__lowerCAmelCase:	Optional[int]   				=      PrepareForLaunch(__SCREAMING_SNAKE_CASE ,			distributed_type="MULTI_GPU"			)
								print(F"Launching training on {num_processes} GPUs."			)
								try:
										start_processes(__SCREAMING_SNAKE_CASE ,			args=__SCREAMING_SNAKE_CASE ,			nprocs=__SCREAMING_SNAKE_CASE ,			start_method="fork"			)
								except ProcessRaisedException as e:
										if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
												raise RuntimeError(
												    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
												    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
												    "Please review your imports and test them when running the `notebook_launcher()` to identify "
												    "which one is problematic."			) from e

				else:
						# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
						if is_mps_available():
								__lowerCAmelCase:	List[Any]   				=      "1"
								print("Launching training on MPS."			)
						elif torch.cuda.is_available():
								print("Launching training on one GPU."			)
						else:
								print("Launching training on CPU."			)
						function(*__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=() ,			__SCREAMING_SNAKE_CASE=2			)     ->     List[Any]:
		from torch.multiprocessing import start_processes

		with tempfile.NamedTemporaryFile() as tmp_file:
				# torch.distributed will expect a few environment variable to be here. We set the ones common to each
				# process here (the other ones will be set be the launcher).
				with patch_environment(
				    world_size=__SCREAMING_SNAKE_CASE ,			master_addr="127.0.01" ,			master_port="29500" ,			accelerate_mixed_precision="no" ,			accelerate_debug_rdv_file=tmp_file.name ,			accelerate_use_cpu="yes" ,			):
						__lowerCAmelCase:	Union[str, Any]   				=      PrepareForLaunch(__SCREAMING_SNAKE_CASE ,			debug=__SCREAMING_SNAKE_CASE			)
						start_processes(__SCREAMING_SNAKE_CASE ,			args=__SCREAMING_SNAKE_CASE ,			nprocs=__SCREAMING_SNAKE_CASE ,			start_method="fork"			)






"""simple docstring"""


from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1_0**-1_0			)     ->     float:
		__lowerCAmelCase:	Union[str, Any]   				=      a
		while True:
				__lowerCAmelCase:	Optional[int]   				=      Decimal(__SCREAMING_SNAKE_CASE			) - (
				    Decimal(eval(__SCREAMING_SNAKE_CASE			)			) / Decimal(eval(str(diff(__SCREAMING_SNAKE_CASE			)			)			)			)  # noqa: S307
				)
				# This number dictates the accuracy of the answer
				if abs(eval(__SCREAMING_SNAKE_CASE			)			) < precision:  # noqa: S307
						return float(__SCREAMING_SNAKE_CASE			)


# 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
			print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
			# Find Square Root of 5
			print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
			# Exponential Roots
			print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')


