



'''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 	__magic_name__						(    unittest.TestCase   ):







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







							@require_multi_gpu
							def 							__lowercase			(							self					:       Tuple		):
									print(F"""Found {torch.cuda.device_count()} devices."""		)
									_a     :      Dict        =							['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 							__lowercase			(							self					:       Tuple		):
									print(F"""Found {torch.cuda.device_count()} devices."""		)
									_a     :      Dict        =							['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 							__lowercase			(							self					:       List[str]		):
									_a     :      str        =							['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 							__lowercase			(							self					:       Tuple		):
									print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""		)
									_a     :      Optional[int]        =							['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__":
							__lowerCAmelCase									=   Accelerator()
							__lowerCAmelCase									=   (accelerator.state.process_index + 2, 10)
							__lowerCAmelCase									=   torch.randint(0, 10, shape).to(accelerator.device)

							__lowerCAmelCase									=   ''''''

							__lowerCAmelCase									=   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									=   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									=   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase									=   {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''SwinForImageClassification''',
							    '''SwinForMaskedImageModeling''',
							    '''SwinModel''',
							    '''SwinPreTrainedModel''',
							    '''SwinBackbone''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFSwinForImageClassification''',
							    '''TFSwinForMaskedImageModeling''',
							    '''TFSwinModel''',
							    '''TFSwinPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swin import (
														    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinBackbone,
														    SwinForImageClassification,
														    SwinForMaskedImageModeling,
														    SwinModel,
														    SwinPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_swin import (
														    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSwinForImageClassification,
														    TFSwinForMaskedImageModeling,
														    TFSwinModel,
														    TFSwinPreTrainedModel,
														)

else:
							import sys

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




