
'''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_	(   _lowercase			,       _lowercase=()			,       _lowercase=None			,       _lowercase="no"			,       _lowercase="29500"				)     ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, Any]     =		False
					lowerCamelCase_					:      List[Any]     =		False
					if any(key.startswith('''KAGGLE'''				) for key in os.environ.keys()				):
										lowerCamelCase_					:      Dict     =		True
					elif "IPython" in sys.modules:
										lowerCamelCase_					:      List[Any]     =		'''google.colab''' in str(sys.modules['''IPython'''].get_ipython()				)

					try:
										lowerCamelCase_					:      Optional[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'''			,       _lowercase				) 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_					:      Optional[Any]     =		8

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






def lowercase_	(   _lowercase			,       _lowercase=()			,       _lowercase=2				)     ->							Dict:
					'''simple docstring'''

					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=_lowercase			,       master_addr='''127.0.01'''			,       master_port='''29500'''			,       accelerate_mixed_precision='''no'''			,       accelerate_debug_rdv_file=tmp_file.name			,       accelerate_use_cpu='''yes'''			,       ):
															lowerCamelCase_					:      int     =		PrepareForLaunch(_lowercase			,       debug=_lowercase				)
															start_processes(_lowercase			,       args=_lowercase			,       nprocs=_lowercase			,       start_method='''fork'''				)




'''simple docstring'''


from __future__ import annotations

from math import pi, sqrt




def lowercase_	(   _lowercase			,       _lowercase				)     ->							tuple:
					'''simple docstring'''

					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''				)

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''				)

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance				)))				),
										)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


