




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 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  List[str]    =		torch.nn.Linear(10	,  10       )
           _A							:  int    =		torch.optim.SGD(model.parameters()	,  0.1       )
           _A							:  str    =		Accelerator()
           _A							:  List[Any]    =		accelerator.prepare(_a       )
           try:
                 pickle.loads(pickle.dumps(_a       )       )
           except Exception as e:
                 self.fail(F'''Accelerated optimizer pickling failed with {e}'''       )
           AcceleratorState._reset_state()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case            = {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  _snake_case            = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

