






import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def      a_ (      _A      ,   _A=()      ,   _A=None      ,   _A="no"      ,   _A="29500"  )			->			Optional[int]:







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

				try:
								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'      ,   _A  ) 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:
												snake_case__									=     8

								snake_case__									=     PrepareForLaunch(_A      ,   distributed_type='TPU'  )
								print(f'''Launching a training on {num_processes} TPU cores.'''  )
								xmp.spawn(_A      ,   args=_A      ,   nprocs=_A      ,   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(*_A  )
				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=_A      ,   master_addr='127.0.01'      ,   master_port=_A      ,   mixed_precision=_A  ):
																snake_case__									=     PrepareForLaunch(_A      ,   distributed_type='MULTI_GPU'  )
																print(f'''Launching training on {num_processes} GPUs.'''  )
																try:
																				start_processes(_A      ,   args=_A      ,   nprocs=_A      ,   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():
																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(*_A  )


def      a_ (      _A      ,   _A=()      ,   _A=2  )			->			Union[str, Any]:







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












from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      int       ,	UpperCamelCase:      Any						)		->		List[Any]:
											snake_case__									=     data
											snake_case__									=     None


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     None
											snake_case__									=     None
							def __iter__(     self:      Optional[int]						)		->		Iterator[Any]:
											snake_case__									=     self.head
											while self.head:
															yield node.data
															snake_case__									=     node.next
															if node == self.head:
																			break
							def __len__(     self:      int						)		->		int:
											return sum(1 for _ in self						)
							def __repr__(     self:      int						)		->		Optional[int]:
											return "->".join(str(UpperCamelCase						) for item in iter(self						)						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Any						)		->		None:
											self.insert_nth(len(self						)       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Any						)		->		None:
											self.insert_nth(0       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Any						)		->		None:
											if index < 0 or index > len(self						):
															raise IndexError('list index out of range.'						)
											snake_case__									=     Node(UpperCamelCase						)
											if self.head is None:
															snake_case__									=     new_node  # first node points itself
															snake_case__									=     snake_case__									=     new_node
											elif index == 0:  # insert at head
															snake_case__									=     self.head
															snake_case__									=     snake_case__									=     new_node
											else:
															snake_case__									=     self.head
															for _ in range(index - 1						):
																			snake_case__									=     temp.next
															snake_case__									=     temp.next
															snake_case__									=     new_node
															if index == len(self						) - 1:  # insert at tail
																			snake_case__									=     new_node
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											return self.delete_nth(0						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:
											return self.delete_nth(len(self						) - 1						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      int = 0						)		->		Any:
											if not 0 <= index < len(self						):
															raise IndexError('list index out of range.'						)
											snake_case__									=     self.head
											if self.head == self.tail:  # just one node
															snake_case__									=     snake_case__									=     None
											elif index == 0:  # delete head node
															snake_case__									=     self.tail.next.next
															snake_case__									=     self.head.next
											else:
															snake_case__									=     self.head
															for _ in range(index - 1						):
																			snake_case__									=     temp.next
															snake_case__									=     temp.next
															snake_case__									=     temp.next.next
															if index == len(self						) - 1:  # delete at tail
																			snake_case__									=     temp
											return delete_node.data


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		bool:
											return len(self						) == 0


def      a_ (      )			->			None:







				"""simple docstring"""
				snake_case__									=     CircularLinkedList()
				assert len(_A  ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(_A  ) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1  )
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0  )
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5  ):
								assert len(_A  ) == i
								circular_linked_list.insert_nth(_A      ,   i + 1  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )

				circular_linked_list.insert_tail(6  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   7  )  )
				circular_linked_list.insert_head(0  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(0      ,   7  )  )

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )
				assert circular_linked_list.delete_nth(2  ) == 3

				circular_linked_list.insert_nth(2      ,   3  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
							import doctest

							doctest.testmod()




