






"""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			(		_snake_case				,	_snake_case=()				,	_snake_case=None				,	_snake_case="no"				,	_snake_case="29500"							):







						"""simple docstring"""






						UpperCAmelCase          =	False
						UpperCAmelCase          =	False
						if any(key.startswith("""KAGGLE"""							) for key in os.environ.keys()							):
												UpperCAmelCase          =	True
						elif "IPython" in sys.modules:
												UpperCAmelCase          =	"""google.colab""" in str(sys.modules["""IPython"""].get_ipython()							)

						try:
												UpperCAmelCase          =	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"""				,	_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:
																		UpperCAmelCase          =	8

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






def _a			(		_snake_case				,	_snake_case=()				,	_snake_case=2							):







						"""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=_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"""				,	):
																		UpperCAmelCase          =	PrepareForLaunch(_snake_case				,	debug=_snake_case							)
																		start_processes(_snake_case				,	args=_snake_case				,	nprocs=_snake_case				,	start_method="""fork"""							)











"""simple docstring"""







def _a			(		_snake_case = 6008_5147_5143							):







      """simple docstring"""






      try:
            UpperCAmelCase          =	int(_snake_case							)
      except (TypeError, ValueError):
            raise TypeError("""Parameter n must be int or castable to int."""							)
      if n <= 0:
            raise ValueError("""Parameter n must be greater than or equal to one."""							)
      UpperCAmelCase          =	1
      UpperCAmelCase          =	2
      while i * i <= n:
            while n % i == 0:
                  UpperCAmelCase          =	i
                  n //= i
            i += 1
      if n > 1:
            UpperCAmelCase          =	n
      return int(_snake_case							)


if __name__ == "__main__":
  print(F"""{solution() = }""")



