from typing import TYPE_CHECKING

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


lowerCAmelCase__					:       Optional[int]		=       {
    '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
    '''processing_donut''': ['''DonutProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__					:       Dict		=       [
		    '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DonutSwinModel''',
		    '''DonutSwinPreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__					:       Optional[int]		=       ['''DonutFeatureExtractor''']
		lowerCAmelCase__					:       Tuple		=       ['''DonutImageProcessor''']


if TYPE_CHECKING:
		from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
		from .processing_donut import DonutProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_donut_swin import (
				    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DonutSwinModel,
				    DonutSwinPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_donut import DonutFeatureExtractor
				from .image_processing_donut import DonutImageProcessor

else:
		import sys

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



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment





class 			__snake_case  (      unittest.TestCase     ):




					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				inspect.getfile(accelerate.test_utils		)
									snake_case__ :     Tuple 							=				os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ['scripts', 'test_sync.py']		)




					@require_cpu
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									debug_launcher(test_sync.main   ,	num_processes=1		)




					@require_cpu
					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									debug_launcher(test_sync.main		)




					@require_single_gpu
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									test_sync.main()




					@require_multi_gpu
					def       __a      (       self		)					->  str:



									'''simple docstring'''







									print(F"""Found {torch.cuda.device_count()} devices."""		)
									snake_case__ :     Any 							=				['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()		)


