
"""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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=()	,lowerCAmelCase__=None	,lowerCAmelCase__="no"	,lowerCAmelCase__="29500"						):
			lowerCamelCase_					=  False
			lowerCamelCase_					=  False
			if any(key.startswith('''KAGGLE'''						) for key in os.environ.keys()						):
						lowerCamelCase_					=  True
			elif "IPython" in sys.modules:
						lowerCamelCase_					=  '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()						)

			try:
						lowerCamelCase_					=  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:
									lowerCamelCase_					=  8

						lowerCamelCase_					=  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__						):
												lowerCamelCase_					=  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():
												lowerCamelCase_					=  '''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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=()	,lowerCAmelCase__=2						):
			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'''	,):
									lowerCamelCase_					=  PrepareForLaunch(lowerCAmelCase__	,debug=lowerCAmelCase__						)
									start_processes(lowerCAmelCase__	,args=lowerCAmelCase__	,nprocs=lowerCAmelCase__	,start_method='''fork'''						)




"""simple docstring"""






from math import pow, sqrt


def      UpperCAmelCase__			(							*lowerCAmelCase__	:float						)					->       bool:
      '''simple docstring'''


      lowercase								=      len(lowerCAmelCase__						) > 0 and all(value > 0.0 for value in values						)
      return result


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError("""Input Error: Molar mass values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,						2						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )






def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,						2						) / molar_mass   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )



