






'''simple docstring'''


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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						Any			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = inspect.getfile(accelerate.test_utils			)
									UpperCamelCase__         = os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ["""scripts""", """test_script.py"""]			)
									UpperCamelCase__         = os.path.sep.join(
									    mod_file.split(os.path.sep			)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]			)
									UpperCamelCase__         = os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ["""scripts""", """test_ops.py"""]			)






			@require_multi_gpu
			def 				A      (   self							:						Optional[int]			)							->    str:

									'''simple docstring'''







									print(f"Found {torch.cuda.device_count()} devices."			)
									UpperCamelCase__         = ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
									with patch_environment(omp_num_threads=1			):
															execute_subprocess_async(lowercase ,      env=os.environ.copy()			)






			@require_multi_gpu
			def 				A      (   self							:						Any			)							->    Tuple:

									'''simple docstring'''







									print(f"Found {torch.cuda.device_count()} devices."			)
									UpperCamelCase__         = ["""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(lowercase ,      env=os.environ.copy()			)






			@require_multi_gpu
			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__			)]
									with patch_environment(omp_num_threads=1			):
															execute_subprocess_async(lowercase ,      env=os.environ.copy()			)







			@require_multi_gpu
			def 				A      (   self							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"			)
									UpperCamelCase__         = ["""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(lowercase ,      env=os.environ.copy()			)


if __name__ == "__main__":
					lowerCamelCase_				: List[Any]    	=							Accelerator()
					lowerCamelCase_				: Tuple    	=							(accelerator.state.process_index + 2, 10)
					lowerCamelCase_				: Union[str, Any]    	=							torch.randint(0, 10, shape).to(accelerator.device)

					lowerCamelCase_				: List[str]    	=							''''''

					lowerCamelCase_				: int    	=							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)."

					lowerCamelCase_				: Union[str, Any]    	=							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."
					lowerCamelCase_				: List[str]    	=							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)








'''simple docstring'''


from collections import defaultdict







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = 1
						UpperCamelCase__         = True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(_A					)
						if ret % 2 == 0:
												cuts.append(_A					)
						return ret







def 	__magic_name__( ):
						'''simple docstring'''





						dfs(1					)


if __name__ == "__main__":
					lowerCamelCase_	,     lowerCamelCase_				: int    	=							10, 9
					lowerCamelCase_				: int    	=							defaultdict(list)
					lowerCamelCase_				: dict[int, bool]    	=							{}
					lowerCamelCase_				: list[int]    	=							[]
					lowerCamelCase_				: Union[str, Any]    	=							0
					lowerCamelCase_				: Tuple    	=							[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)
