






'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowerCamelCase   :					Any   							=					{'''configuration_glpn''': ['''GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GLPNConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					List[Any]   							=					['''GLPNFeatureExtractor''']
		__lowerCamelCase   :					Any   							=					['''GLPNImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					Optional[Any]   							=					[
		    '''GLPN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GLPNForDepthEstimation''',
		    '''GLPNLayer''',
		    '''GLPNModel''',
		    '''GLPNPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_glpn import GLPNFeatureExtractor
				from .image_processing_glpn import GLPNImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_glpn import (
				    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GLPNForDepthEstimation,
				    GLPNLayer,
				    GLPNModel,
				    GLPNPreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Dict   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




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


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       torch.nn.Linear(10       ,							10				)
										snake_case_				:					Dict   =       torch.optim.SGD(model.parameters()       ,							0.1				)
										snake_case_				:					Tuple   =       Accelerator()
										snake_case_				:					Optional[Any]   =       accelerator.prepare(lowerCAmelCase__				)
										try:
													pickle.loads(pickle.dumps(lowerCAmelCase__				)				)
										except Exception as e:
													self.fail(F'''Accelerated optimizer pickling failed with {e}'''				)
										AcceleratorState._reset_state()




