
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  ):




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       inspect.getfile(accelerate.test_utils			)
		lowercase	    =       os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_script.py']			)
		lowercase	    =       os.path.sep.join(
		    mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_distributed_data_loop.py']			)
		lowercase	    =       os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ['scripts', 'test_ops.py']			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		print(F'''Found {torch.cuda.device_count()} devices.'''			)
		lowercase	    =       ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
		with patch_environment(omp_num_threads=1			):
			execute_subprocess_async(snake_case						,       env=os.environ.copy()			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		print(F'''Found {torch.cuda.device_count()} devices.'''			)
		lowercase	    =       ['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(snake_case						,       env=os.environ.copy()			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__			)]
		with patch_environment(omp_num_threads=1			):
			execute_subprocess_async(snake_case						,       env=os.environ.copy()			)





	@require_multi_gpu
	def       SCREAMING_SNAKE_CASE__		(			self			):
		print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''			)
		lowercase	    =       ['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(snake_case						,       env=os.environ.copy()			)


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

	UpperCAmelCase       =					''''''

	UpperCAmelCase       =					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)."

	UpperCAmelCase       =					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."
	UpperCAmelCase       =					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


a_      =     {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Swinv2ForImageClassification',
			    'Swinv2ForMaskedImageModeling',
			    'Swinv2Model',
			    'Swinv2PreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)