

"""simple docstring"""




from typing import TYPE_CHECKING

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


A_				:      Union[str, Any]           =			{
    "configuration_encodec": [
        "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EncodecConfig",
    ],
    "feature_extraction_encodec": ["EncodecFeatureExtractor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_				:      List[Any]           =			[
						    "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "EncodecModel",
						    "EncodecPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_encodec import (
						    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    EncodecConfig,
						)
						from .feature_extraction_encodec import EncodecFeatureExtractor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_encodec import (
												    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
												    EncodecModel,
												    EncodecPreTrainedModel,
												)

else:
						import sys

						A_				:      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  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   torch.nn.Linear(1_0     ,					1_0							)
						SCREAMING_SNAKE_CASE__										=   torch.optim.SGD(model.parameters()     ,					0.1							)
						SCREAMING_SNAKE_CASE__										=   Accelerator()
						SCREAMING_SNAKE_CASE__										=   accelerator.prepare(__UpperCAmelCase							)
						try:
									pickle.loads(pickle.dumps(__UpperCAmelCase							)							)
						except Exception as e:
									self.fail(F"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()


