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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					torch.nn.Linear(1_0   ,      1_0			)
  UpperCamelCase_					:						Any =					torch.optim.SGD(model.parameters()   ,      0.1			)
  UpperCamelCase_					:						Union[str, Any] =					Accelerator()
  UpperCamelCase_					:						Union[str, Any] =					accelerator.prepare(snake_case			)
  try:
   pickle.loads(pickle.dumps(snake_case			)			)
  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_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    					=       {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['EncoderDecoderModel']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['TFEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_encoder_decoder import EncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_encoder_decoder import EncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
   import sys

   a_    					=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



