"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__   =					{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					["DeiTFeatureExtractor"]
					SCREAMING_SNAKE_CASE__   =					["DeiTImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DeiTForImageClassification",
					    "DeiTForImageClassificationWithTeacher",
					    "DeiTForMaskedImageModeling",
					    "DeiTModel",
					    "DeiTPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDeiTForImageClassification",
					    "TFDeiTForImageClassificationWithTeacher",
					    "TFDeiTForMaskedImageModeling",
					    "TFDeiTModel",
					    "TFDeiTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_deit import DeiTFeatureExtractor
										from .image_processing_deit import DeiTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deit import (
										    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DeiTForImageClassification,
										    DeiTForImageClassificationWithTeacher,
										    DeiTForMaskedImageModeling,
										    DeiTModel,
										    DeiTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deit import (
										    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDeiTForImageClassification,
										    TFDeiTForImageClassificationWithTeacher,
										    TFDeiTForMaskedImageModeling,
										    TFDeiTModel,
										    TFDeiTPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_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 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch.nn.Linear(10						,      10      )
										snake_case        =     torch.optim.SGD(model.parameters()						,      0.1      )
										snake_case        =     Accelerator()
										snake_case        =     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()


