

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      inspect.getfile(accelerate.test_utils		)
										_UpperCAmelCase    :						int							=      os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_script.py"]		)
										_UpperCAmelCase    :						List[str]							=      os.path.sep.join(
										    mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_distributed_data_loop.py"]		)
										_UpperCAmelCase    :						Any							=      os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_ops.py"]		)


						@require_multi_gpu
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										print(f'Found {torch.cuda.device_count()} devices.'		)
										_UpperCAmelCase    :						Optional[Any]							=      ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
										with patch_environment(omp_num_threads=1		):
														execute_subprocess_async(A_  ,   env=os.environ.copy()		)


						@require_multi_gpu
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										print(f'Found {torch.cuda.device_count()} devices.'		)
										_UpperCAmelCase    :						Union[str, Any]							=      ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
										print(f'Command: {cmd}'		)
										with patch_environment(omp_num_threads=1		):
														execute_subprocess_async(A_  ,   env=os.environ.copy()		)


						@require_multi_gpu
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
										with patch_environment(omp_num_threads=1		):
														execute_subprocess_async(A_  ,   env=os.environ.copy()		)





						@require_multi_gpu
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'		)
										_UpperCAmelCase    :						Dict							=      ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
										with patch_environment(omp_num_threads=1  ,   cuda_visible_devices="0,1"		):
														execute_subprocess_async(A_  ,   env=os.environ.copy()		)


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE_            = Accelerator()
				SCREAMING_SNAKE_CASE_            = (accelerator.state.process_index + 2, 10)
				SCREAMING_SNAKE_CASE_            = torch.randint(0, 10, shape).to(accelerator.device)

				SCREAMING_SNAKE_CASE_            = ""

				SCREAMING_SNAKE_CASE_            = accelerator.pad_across_processes(tensor)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
								error_msg += "Padding was not done with the right value (0)."

				SCREAMING_SNAKE_CASE_            = accelerator.pad_across_processes(tensor, pad_first=True)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				SCREAMING_SNAKE_CASE_            = accelerator.state.num_processes - accelerator.state.process_index - 1
				if not torch.equal(tensora[index:], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[:index] == 0):
								error_msg += "Padding was not done with the right value (0)."

				# Raise error at the end to make sure we don't stop at the first failure.
				if len(error_msg) > 0:
								raise ValueError(error_msg)





from typing import Any

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      data
										_UpperCAmelCase    :						Any							=      None







class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.head
										while temp is not None:
														print(temp.data  ,   end=" "		)
														_UpperCAmelCase    :						str							=      temp.next
										print()


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      Node(A_		)
										_UpperCAmelCase    :						Tuple							=      self.head
										_UpperCAmelCase    :						Tuple							=      new_node





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if node_data_a == node_data_a:
														return
										else:
														_UpperCAmelCase    :						int							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						Tuple							=      node_a.next

														_UpperCAmelCase    :						Dict							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						List[Any]							=      node_a.next

														if node_a is None or node_a is None:
																		return

														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      node_a.data, node_a.data


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = LinkedList()
					for i in range(5, 0, -1):
										ll.push(i)

					ll.print_list()

					ll.swap_nodes(1, 4)
					print('After swapping')
					ll.print_list()


