


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       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:int	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		inspect.getfile(accelerate.test_utils	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['''scripts''', '''test_script.py''']	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		os.path.sep.join(
										    mod_file.split(os.path.sep	)[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']	)
										__SCREAMING_SNAKE_CASE						:     int					=		os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['''scripts''', '''test_ops.py''']	)


				@require_multi_gpu
				def 		__magic_name__(       self			:Optional[int]	)		->			Any:
										print(f'''Found {torch.cuda.device_count()} devices.'''	)
										__SCREAMING_SNAKE_CASE						:     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(lowerCAmelCase__			,			env=os.environ.copy()	)


				@require_multi_gpu
				def 		__magic_name__(       self			:Optional[int]	)		->			Tuple:
										print(f'''Found {torch.cuda.device_count()} devices.'''	)
										__SCREAMING_SNAKE_CASE						:     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(lowerCAmelCase__			,			env=os.environ.copy()	)


				@require_multi_gpu
				def 		__magic_name__(       self			:Tuple	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__	)]
										with patch_environment(omp_num_threads=1	):
																execute_subprocess_async(lowerCAmelCase__			,			env=os.environ.copy()	)



				@require_multi_gpu
				def 		__magic_name__(       self			:str	)		->			int:
										print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''	)
										__SCREAMING_SNAKE_CASE						:     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(lowerCAmelCase__			,			env=os.environ.copy()	)


if __name__ == "__main__":
						__lowerCAmelCase	: Dict  				=Accelerator()
						__lowerCAmelCase	: Any  				=(accelerator.state.process_index + 2, 1_0)
						__lowerCAmelCase	: List[Any]  				=torch.randint(0, 1_0, shape).to(accelerator.device)

						__lowerCAmelCase	: Tuple  				=''

						__lowerCAmelCase	: 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)."

						__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	: Optional[int]  				=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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase	: Any  				={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: int  				=[
						    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMSNModel',
						    'ViTMSNForImageClassification',
						    'ViTMSNPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_msn import (
												    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMSNForImageClassification,
												    ViTMSNModel,
												    ViTMSNPreTrainedModel,
												)

else:
						import sys

						__lowerCAmelCase	: Union[str, Any]  				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
