

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

							try:
														SCREAMING_SNAKE_CASE_				=				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'		,lowerCAmelCase__   ) 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:
																					SCREAMING_SNAKE_CASE_				=				8

														SCREAMING_SNAKE_CASE_				=				PrepareForLaunch(lowerCAmelCase__		,distributed_type='TPU'   )
														print(F"""Launching a training on {num_processes} TPU cores."""   )
														xmp.spawn(lowerCAmelCase__		,args=lowerCAmelCase__		,nprocs=lowerCAmelCase__		,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(*lowerCAmelCase__   )
							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=lowerCAmelCase__		,master_addr='127.0.01'		,master_port=lowerCAmelCase__		,mixed_precision=lowerCAmelCase__   ):
																												SCREAMING_SNAKE_CASE_				=				PrepareForLaunch(lowerCAmelCase__		,distributed_type='MULTI_GPU'   )
																												print(F"""Launching training on {num_processes} GPUs."""   )
																												try:
																																			start_processes(lowerCAmelCase__		,args=lowerCAmelCase__		,nprocs=lowerCAmelCase__		,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():
																												SCREAMING_SNAKE_CASE_				=				'1'
																												print('Launching training on MPS.'   )
																					elif torch.cuda.is_available():
																												print('Launching training on one GPU.'   )
																					else:
																												print('Launching training on CPU.'   )
																					function(*lowerCAmelCase__   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Any		,lowerCAmelCase__:					List[Any]=()		,lowerCAmelCase__:					List[Any]=2   )      -> Tuple:
							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=lowerCAmelCase__		,master_addr='127.0.01'		,master_port='29500'		,accelerate_mixed_precision='no'		,accelerate_debug_rdv_file=tmp_file.name		,accelerate_use_cpu='yes'		,):
																					SCREAMING_SNAKE_CASE_				=				PrepareForLaunch(lowerCAmelCase__		,debug=lowerCAmelCase__   )
																					start_processes(lowerCAmelCase__		,args=lowerCAmelCase__		,nprocs=lowerCAmelCase__		,start_method='fork'   )








'''simple docstring'''

SCREAMING_SNAKE_CASE							:				int     	=			8.3_1_4_4_5_9_8




def 							_UpperCamelCase				(   lowerCAmelCase__:					float		,lowerCAmelCase__:					float   )      -> float:
							if temperature < 0:
														raise Exception('Temperature cannot be less than 0 K'   )
							if molar_mass <= 0:
														raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'   )
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
							import doctest

							# run doctest
							doctest.testmod()

							# example
							SCREAMING_SNAKE_CASE							:				List[str]     	=			300
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			28
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			rms_speed_of_molecule(temperature, molar_mass)
							print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")





