


"""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_    ,		lowerCAmelCase_=()    ,		lowerCAmelCase_=None    ,		lowerCAmelCase_="no"    ,		lowerCAmelCase_="29500"  ):




					'''simple docstring'''


					__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_    ,		lowerCAmelCase_=()    ,		lowerCAmelCase_=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=__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'''




def 	__lowerCamelCase			(      __lowerCAmelCase       :							int		)		->  int:
   assert isinstance(__lowerCAmelCase      ,    __lowerCAmelCase		), F'''The input value of [n={number}] is not an integer'''

   if number == 1:
      return 2
   elif number < 1:
      snake_case 						=   F'''The input value of [n={number}] has to be > 0'''
      raise ValueError(__lowerCAmelCase		)
   else:
      snake_case 						=   sylvester(number - 1		)
      snake_case 						=   num - 1
      snake_case 						=   num
      return lower * upper + 1


if __name__ == "__main__":
 print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""")

