





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		List[Any]		=							{
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		int		=							[
       'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'NllbMoeForConditionalGeneration',
       'NllbMoeModel',
       'NllbMoePreTrainedModel',
       'NllbMoeTop2Router',
       'NllbMoeSparseMLP',
   ]


if TYPE_CHECKING:
   from .configuration_nllb_moe import (
       NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
       NllbMoeConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_nllb_moe import (
          NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
          NllbMoeForConditionalGeneration,
          NllbMoeModel,
          NllbMoePreTrainedModel,
          NllbMoeSparseMLP,
          NllbMoeTopaRouter,
      )


else:
   import sys

   UpperCAmelCase					:		Any		=							_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 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





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


