






"""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    lowercase__			(							unittest.TestCase   ):


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							List[str]									=inspect.getfile(accelerate.test_utils					)
										lowerCamelCase_       :							Optional[int]									=os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["scripts", "test_script.py"]					)
										lowerCamelCase_       :							Optional[int]									=os.path.sep.join(
										    mod_file.split(os.path.sep					)[:-1] + ["scripts", "test_distributed_data_loop.py"]					)
										lowerCamelCase_       :							List[str]									=os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["scripts", "test_ops.py"]					)


			@require_multi_gpu
			def 	UpperCAmelCase__						(      self     :  List[str]					):
										print(F"""Found {torch.cuda.device_count()} devices."""					)
										lowerCamelCase_       :							List[str]									=['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
										with patch_environment(omp_num_threads=1					):
																	execute_subprocess_async(UpperCamelCase_   ,       env=os.environ.copy()					)


			@require_multi_gpu
			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										print(F"""Found {torch.cuda.device_count()} devices."""					)
										lowerCamelCase_       :							Tuple									=['''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(UpperCamelCase_   ,       env=os.environ.copy()					)


			@require_multi_gpu
			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							Tuple									=['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__					)]
										with patch_environment(omp_num_threads=1					):
																	execute_subprocess_async(UpperCamelCase_   ,       env=os.environ.copy()					)


			@require_multi_gpu
			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""					)
										lowerCamelCase_       :							Tuple									=['''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(UpperCamelCase_   ,       env=os.environ.copy()					)


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

						A__       :				Union[str, Any] 					=					''

						A__       :				Union[str, Any] 					=					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)."

						A__       :				Dict 					=					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."
						A__       :				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 datetime import datetime as dt
import os

from github import Github


__snake_case							      =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			a      (			)  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= Github(os.environ['''GITHUB_TOKEN''']   )
					UpperCamelCase__   :Tuple												= g.get_repo('''huggingface/transformers'''   )
					UpperCamelCase__   :Union[str, Any]												= repo.get_issues(state='''open'''   )

					for issue in open_issues:
										UpperCamelCase__   :List[Any]												= sorted([comment for comment in issue.get_comments()] ,					key=lambda __a   : i.created_at ,					reverse=__a   )
										UpperCamelCase__   :List[Any]												= comments[0] if len(__a   ) > 0 else None
										if (
										    last_comment is not None
										    and last_comment.user.login == "github-actions[bot]"
										    and (dt.utcnow() - issue.updated_at).days > 7
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
															issue.edit(state='''closed'''   )
										elif (
										    (dt.utcnow() - issue.updated_at).days > 23
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would add stale comment to {issue.number}")
															issue.create_comment(
															    '''This issue has been automatically marked as stale because it has not had '''
															    '''recent activity. If you think this still needs to be addressed '''
															    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
															    '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
															    '''are likely to be ignored.'''   )


if __name__ == "__main__":
		main()