"""simple docstring"""




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_a				:		str	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Any		 =		"linear"
     _UpperCamelCase							:      List[Any]		 =		"cosine"
     _UpperCamelCase							:      Dict		 =		"cosine_with_restarts"
     _UpperCamelCase							:      Optional[Any]		 =		"polynomial"
     _UpperCamelCase							:      Any		 =		"constant"
     _UpperCamelCase							:      List[Any]		 =		"constant_with_warmup"
     _UpperCamelCase							:      Tuple		 =		"piecewise_constant"







def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int = -1     )	->				int:
  return LambdaLR(_lowerCamelCase      ,lambda _lowerCamelCase     : 1      ,last_epoch=_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int = -1     )	->				List[str]:

  def lr_lambda(_lowerCamelCase						:	int     ):
    if current_step < num_warmup_steps:
      return float(_lowerCamelCase     ) / float(max(1.0      ,_lowerCamelCase     )     )
    return 1.0

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,last_epoch=_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	str      ,_lowerCamelCase						:	int = -1     )	->				List[Any]:
  _lowerCAmelCase						:	Tuple        =					{}
  _lowerCAmelCase						:	List[str]        =					step_rules.split(""","""     )
  for rule_str in rule_list[:-1]:
    _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					rule_str.split(""":"""     )
    _lowerCAmelCase						:	int        =					int(_lowerCamelCase     )
    _lowerCAmelCase						:	str        =					float(_lowerCamelCase     )
    _lowerCAmelCase						:	List[str]        =					value
  _lowerCAmelCase						:	Any        =					float(rule_list[-1]     )

  def create_rules_function(_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Union[str, Any]     ):
    def rule_func(_lowerCamelCase						:	int     ) -> float:
      _lowerCAmelCase						:	int        =					sorted(rules_dict.keys()     )
      for i, sorted_step in enumerate(_lowerCamelCase     ):
        if steps < sorted_step:
          return rules_dict[sorted_steps[i]]
      return last_lr_multiple

    return rule_func

  _lowerCAmelCase						:	List[str]        =					create_rules_function(_lowerCamelCase      ,_lowerCamelCase     )

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,last_epoch=_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Union[str, Any]=-1     )	->				Optional[int]:

  def lr_lambda(_lowerCamelCase						:	int     ):
    if current_step < num_warmup_steps:
      return float(_lowerCamelCase     ) / float(max(1      ,_lowerCamelCase     )     )
    return max(
        0.0      ,float(num_training_steps - current_step     ) / float(max(1      ,num_training_steps - num_warmup_steps     )     )     )

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	float = 0.5      ,_lowerCamelCase						:	int = -1     )	->				Union[str, Any]:

  def lr_lambda(_lowerCamelCase						:	Optional[int]     ):
    if current_step < num_warmup_steps:
      return float(_lowerCamelCase     ) / float(max(1      ,_lowerCamelCase     )     )
    _lowerCAmelCase						:	Any        =					float(current_step - num_warmup_steps     ) / float(max(1      ,num_training_steps - num_warmup_steps     )     )
    return max(0.0      ,0.5 * (1.0 + math.cos(math.pi * float(_lowerCamelCase     ) * 2.0 * progress     ))     )

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	int = 1      ,_lowerCamelCase						:	int = -1     )	->				Tuple:

  def lr_lambda(_lowerCamelCase						:	Optional[Any]     ):
    if current_step < num_warmup_steps:
      return float(_lowerCamelCase     ) / float(max(1      ,_lowerCamelCase     )     )
    _lowerCAmelCase						:	Optional[int]        =					float(current_step - num_warmup_steps     ) / float(max(1      ,num_training_steps - num_warmup_steps     )     )
    if progress >= 1.0:
      return 0.0
    return max(0.0      ,0.5 * (1.0 + math.cos(math.pi * ((float(_lowerCamelCase     ) * progress) % 1.0)     ))     )

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	str=1e-7      ,_lowerCamelCase						:	List[str]=1.0      ,_lowerCamelCase						:	Tuple=-1     )	->				Union[str, Any]:
  _lowerCAmelCase						:	str        =					optimizer.defaults["""lr"""]
  if not (lr_init > lr_end):
    raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"     )

  def lr_lambda(_lowerCamelCase						:	int     ):
    if current_step < num_warmup_steps:
      return float(_lowerCamelCase     ) / float(max(1      ,_lowerCamelCase     )     )
    elif current_step > num_training_steps:
      return lr_end / lr_init  # as LambdaLR multiplies by lr_init
    else:
      _lowerCAmelCase						:	Optional[Any]        =					lr_init - lr_end
      _lowerCAmelCase						:	List[Any]        =					num_training_steps - num_warmup_steps
      _lowerCAmelCase						:	List[str]        =					1 - (current_step - num_warmup_steps) / decay_steps
      _lowerCAmelCase						:	Optional[Any]        =					lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )


_a				:		Dict	   =       {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, SchedulerType]      ,_lowerCamelCase						:	Optimizer      ,_lowerCamelCase						:	Optional[str] = None      ,_lowerCamelCase						:	Optional[int] = None      ,_lowerCamelCase						:	Optional[int] = None      ,_lowerCamelCase						:	int = 1      ,_lowerCamelCase						:	float = 1.0      ,_lowerCamelCase						:	int = -1      ,)	->				List[str]:
  _lowerCAmelCase						:	str        =					SchedulerType(_lowerCamelCase     )
  _lowerCAmelCase						:	Any        =					TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(_lowerCamelCase      ,last_epoch=_lowerCamelCase     )

  if name == SchedulerType.PIECEWISE_CONSTANT:
    return schedule_func(_lowerCamelCase      ,step_rules=_lowerCamelCase      ,last_epoch=_lowerCamelCase     )

  # All other schedulers require `num_warmup_steps`
  if num_warmup_steps is None:
    raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument."     )

  if name == SchedulerType.CONSTANT_WITH_WARMUP:
    return schedule_func(_lowerCamelCase      ,num_warmup_steps=_lowerCamelCase      ,last_epoch=_lowerCamelCase     )

  # All other schedulers require `num_training_steps`
  if num_training_steps is None:
    raise ValueError(f"{name} requires `num_training_steps`, please provide that argument."     )

  if name == SchedulerType.COSINE_WITH_RESTARTS:
    return schedule_func(
        _lowerCamelCase      ,num_warmup_steps=_lowerCamelCase      ,num_training_steps=_lowerCamelCase      ,num_cycles=_lowerCamelCase      ,last_epoch=_lowerCamelCase      ,)

  if name == SchedulerType.POLYNOMIAL:
    return schedule_func(
        _lowerCamelCase      ,num_warmup_steps=_lowerCamelCase      ,num_training_steps=_lowerCamelCase      ,power=_lowerCamelCase      ,last_epoch=_lowerCamelCase      ,)

  return schedule_func(
      _lowerCamelCase      ,num_warmup_steps=_lowerCamelCase      ,num_training_steps=_lowerCamelCase      ,last_epoch=_lowerCamelCase     )





"""simple docstring"""




import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment





def 							SCREAMING_SNAKE_CASE					(      )	->				Tuple:
  _lowerCAmelCase						:	Tuple        =					torch.nn.Linear(2      ,4     )
  _lowerCAmelCase						:	Union[str, Any]        =					torch.optim.AdamW(model.parameters()      ,lr=1.0     )
  _lowerCAmelCase						:	Tuple        =					torch.optim.lr_scheduler.OneCycleLR(_lowerCamelCase      ,max_lr=0.01      ,steps_per_epoch=2      ,epochs=1     )
  _lowerCAmelCase						:	Tuple        =					DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
  _lowerCAmelCase						:	List[Any]        =					DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

  return model, optimizer, scheduler, train_dl, valid_dl





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any     )	->				int:
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]     )	->				Any:
  _lowerCAmelCase						:	List[str]        =					torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
  model.load_state_dict(_lowerCamelCase     )






class 						__A							(			SCREAMING_SNAKE_CASE_		):






     @require_cuda
     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					Accelerator()
       assert PartialState._shared_state["_cpu"] is False
       assert PartialState._shared_state["device"].type == "cuda"
       with self.assertRaises(a__  ):
         _lowerCAmelCase						:	Tuple        =					Accelerator(cpu=a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					Accelerator()
       _lowerCAmelCase						:	Any        =					GradientState()
       assert state.num_steps == 1
       _lowerCAmelCase						:	Optional[int]        =					4
       assert state.num_steps == 4

       assert state.sync_gradients is True
       _lowerCAmelCase						:	Dict        =					False
       assert state.sync_gradients is False
       GradientState._reset_state()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					create_components()

       (
           (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     
       )						:	int        =					accelerator.prepare(a__    ,					a__    ,					a__    ,					a__    ,					a__  )

       self.assertTrue(prepared_model in accelerator._models  )
       self.assertTrue(prepared_optimizer in accelerator._optimizers  )
       self.assertTrue(prepared_scheduler in accelerator._schedulers  )
       self.assertTrue(prepared_train_dl in accelerator._dataloaders  )
       self.assertTrue(prepared_valid_dl in accelerator._dataloaders  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	List[Any]        =					create_components()
       accelerator.prepare(a__    ,					a__    ,					a__    ,					a__    ,					a__  )
       accelerator.free_memory()

       self.assertTrue(len(accelerator._models  ) == 0  )
       self.assertTrue(len(accelerator._optimizers  ) == 0  )
       self.assertTrue(len(accelerator._schedulers  ) == 0  )
       self.assertTrue(len(accelerator._dataloaders  ) == 0  )






     def    __A  (		self  ):
       PartialState._reset_state()

       # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
       def noop(*a__    ,					**a__  ):
         pass

       with patch("""torch.cuda.set_device"""    ,					a__  ), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""  ):
         _lowerCAmelCase						:	Dict        =					Accelerator()
         self.assertEqual(str(accelerator.state.device  )    ,					"""cuda:64"""  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					create_components()
       accelerator.prepare(a__    ,					a__    ,					a__    ,					a__    ,					a__  )

       _lowerCAmelCase						:	List[Any]        =					get_signature(a__  )

       with tempfile.TemporaryDirectory() as tmpdirname:
         accelerator.save_state(a__  )

         # make sure random weights don't match
         load_random_weights(a__  )
         self.assertTrue(abs(model_signature - get_signature(a__  )  ) > 1e-3  )

         # make sure loaded weights match
         accelerator.load_state(a__  )
         self.assertTrue(abs(model_signature - get_signature(a__  )  ) < 1e-3  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					create_components()
       accelerator.prepare(a__    ,					a__    ,					a__    ,					a__    ,					a__  )

       _lowerCAmelCase						:	Optional[Any]        =					get_signature(a__  )

       # saving hook
       def save_config(a__    ,					a__    ,					a__  ):
         _lowerCAmelCase						:	Dict        =					{"""class_name""": models[0].__class__.__name__}

         with open(os.path.join(a__    ,					"""data.json"""  )    ,					"""w"""  ) as f:
           json.dump(a__    ,					a__  )

        # loading hook
       def load_config(a__    ,					a__  ):
         with open(os.path.join(a__    ,					"""data.json"""  )    ,					"""r"""  ) as f:
           _lowerCAmelCase						:	int        =					json.load(a__  )

         _lowerCAmelCase						:	str        =					config["""class_name"""]

       _lowerCAmelCase						:	Union[str, Any]        =					accelerator.register_save_state_pre_hook(a__  )
       _lowerCAmelCase						:	int        =					accelerator.register_load_state_pre_hook(a__  )

       with tempfile.TemporaryDirectory() as tmpdirname:
         accelerator.save_state(a__  )

         # make sure random weights don't match with hooks
         load_random_weights(a__  )
         self.assertTrue(abs(model_signature - get_signature(a__  )  ) > 1e-3  )

         # random class name to verify correct one is loaded
         _lowerCAmelCase						:	Dict        =					"""random"""

         # make sure loaded weights match with hooks
         accelerator.load_state(a__  )
         self.assertTrue(abs(model_signature - get_signature(a__  )  ) < 1e-3  )

         # mode.class_name is loaded from config
         self.assertTrue(model.class_name == model.__class__.__name__  )

       # remove hooks
       save_hook.remove()
       load_hook.remove()

       with tempfile.TemporaryDirectory() as tmpdirname:
         accelerator.save_state(a__  )

         # make sure random weights don't match with hooks removed
         load_random_weights(a__  )
         self.assertTrue(abs(model_signature - get_signature(a__  )  ) > 1e-3  )

         # random class name to verify correct one is loaded
         _lowerCAmelCase						:	Optional[Any]        =					"""random"""

         # make sure loaded weights match with hooks removed
         accelerator.load_state(a__  )
         self.assertTrue(abs(model_signature - get_signature(a__  )  ) < 1e-3  )

         # mode.class_name is NOT loaded from config
         self.assertTrue(model.class_name != model.__class__.__name__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					create_components()
       _lowerCAmelCase						:	Any        =					None

       # This should work
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					accelerator.prepare(
           a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  )
       self.assertTrue(dummy_obj is None  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					create_components()
       _lowerCAmelCase						:	Optional[int]        =					[1, 2, 3]

       # This should work
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					accelerator.prepare(
           a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  )
       self.assertEqual(
           getattr(a__    ,					"""_is_accelerate_prepared"""    ,					a__  )    ,					a__    ,					"""Dummy object should have `_is_accelerate_prepared` set to `True`"""    ,					)
       self.assertEqual(
           getattr(a__    ,					"""_is_accelerate_prepared"""    ,					a__  )    ,					a__    ,					"""Model is missing `_is_accelerator_prepared` or is set to `False`"""    ,					)
       self.assertEqual(
           getattr(a__    ,					"""_is_accelerate_prepared"""    ,					a__  )    ,					a__    ,					"""Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""    ,					)
       self.assertEqual(
           getattr(a__    ,					"""_is_accelerate_prepared"""    ,					a__  )    ,					a__    ,					"""Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""    ,					)
       self.assertEqual(
           getattr(a__    ,					"""_is_accelerate_prepared"""    ,					a__  )    ,					a__    ,					"""Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""    ,					)
       self.assertEqual(
           getattr(a__    ,					"""_is_accelerate_prepared"""    ,					a__  )    ,					a__    ,					"""Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""    ,					)






     @slow
     @require_bnb
     def    __A  (		self  ):
       from transformers import AutoModelForCausalLM

       _lowerCAmelCase						:	List[str]        =					AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,					load_in_abit=a__    ,					device_map={"""""": 0}    ,					)
       _lowerCAmelCase						:	List[str]        =					Accelerator()

       # This should work
       _lowerCAmelCase						:	List[Any]        =					accelerator.prepare(a__  )






     @slow
     @require_bnb
     def    __A  (		self  ):
       from transformers import AutoModelForCausalLM

       _lowerCAmelCase						:	Any        =					Accelerator()

       with init_empty_weights():
         _lowerCAmelCase						:	Dict        =					AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""    ,					)
         model.tie_weights()
         _lowerCAmelCase						:	int        =					infer_auto_device_map(a__  )
         _lowerCAmelCase						:	Optional[Any]        =					"""cpu"""

       _lowerCAmelCase						:	Dict        =					AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,					device_map=a__    ,					load_in_abit=a__    ,					llm_inta_enable_fpaa_cpu_offload=a__  )

       # This should not work and get value error
       with self.assertRaises(a__  ):
         _lowerCAmelCase						:	List[str]        =					accelerator.prepare(a__  )






     @slow
     @require_bnb
     @require_multi_gpu
     def    __A  (		self  ):
       from transformers import AutoModelForCausalLM

       _lowerCAmelCase						:	Dict        =					{"""distributed_type""": DistributedType.MULTI_GPU}

       with init_empty_weights():
         _lowerCAmelCase						:	Optional[Any]        =					AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""    ,					)
         model.tie_weights()
         _lowerCAmelCase						:	List[str]        =					infer_auto_device_map(a__  )
         _lowerCAmelCase						:	Union[str, Any]        =					1

       _lowerCAmelCase						:	int        =					AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,					load_in_abit=a__    ,					device_map=a__    ,					)
       _lowerCAmelCase						:	Tuple        =					Accelerator()

       # This should not work and get value error
       with self.assertRaises(a__  ):
         _lowerCAmelCase						:	Optional[int]        =					accelerator.prepare(a__  )

       PartialState._reset_state()






     @slow
     @require_bnb
     @require_multi_gpu
     def    __A  (		self  ):
       from transformers import AutoModelForCausalLM

       with init_empty_weights():
         _lowerCAmelCase						:	Dict        =					AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""    ,					)
         _lowerCAmelCase						:	int        =					infer_auto_device_map(a__  )
         _lowerCAmelCase						:	List[Any]        =					1

       _lowerCAmelCase						:	Optional[Any]        =					AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,					load_in_abit=a__    ,					device_map=a__    ,					)
       _lowerCAmelCase						:	str        =					Accelerator()

       # This should work
       _lowerCAmelCase						:	str        =					accelerator.prepare(a__  )






     @require_cuda
     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					torch.nn.Linear(10    ,					10  )
       _lowerCAmelCase						:	Any        =					torch.optim.SGD(model.parameters()    ,					lr=0.0_1  )
       _lowerCAmelCase						:	List[str]        =					Accelerator(cpu=a__  )
       _lowerCAmelCase						:	Tuple        =					accelerator.prepare(a__  )




