"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


_a				:		Tuple	   =       '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

_a				:		List[str]	   =       '\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

_a				:		Union[str, Any]	   =       '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	bool      ,_lowerCamelCase						:	Optional[Dict[int, int]] = None      ,_lowerCamelCase						:	bool = False      ,)	->				Optional[int]:
  if label_map is not None:
    for old_id, new_id in label_map.items():
      _lowerCAmelCase						:	int        =					new_id

    # turn into Numpy arrays
  _lowerCAmelCase						:	Any        =					np.array(_lowerCamelCase     )
  _lowerCAmelCase						:	Tuple        =					np.array(_lowerCamelCase     )

  if reduce_labels:
    _lowerCAmelCase						:	int        =					255
    _lowerCAmelCase						:	Any        =					label - 1
    _lowerCAmelCase						:	Tuple        =					255

  _lowerCAmelCase						:	Optional[Any]        =					label != ignore_index
  _lowerCAmelCase						:	Optional[int]        =					np.not_equal(_lowerCamelCase      ,_lowerCamelCase     )
  _lowerCAmelCase						:	List[Any]        =					pred_label[mask]
  _lowerCAmelCase						:	List[Any]        =					np.array(_lowerCamelCase     )[mask]

  _lowerCAmelCase						:	Dict        =					pred_label[pred_label == label]

  _lowerCAmelCase						:	int        =					np.histogram(_lowerCamelCase      ,bins=_lowerCamelCase      ,range=(0, num_labels - 1)     )[0]
  _lowerCAmelCase						:	int        =					np.histogram(_lowerCamelCase      ,bins=_lowerCamelCase      ,range=(0, num_labels - 1)     )[0]
  _lowerCAmelCase						:	Union[str, Any]        =					np.histogram(_lowerCamelCase      ,bins=_lowerCamelCase      ,range=(0, num_labels - 1)     )[0]

  _lowerCAmelCase						:	Any        =					area_pred_label + area_label - area_intersect

  return area_intersect, area_union, area_pred_label, area_label





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	str      ,_lowerCamelCase						:	bool      ,_lowerCamelCase						:	Optional[Dict[int, int]] = None      ,_lowerCamelCase						:	bool = False      ,)	->				List[str]:
  _lowerCAmelCase						:	Any        =					np.zeros((num_labels,)      ,dtype=np.floataa     )
  _lowerCAmelCase						:	List[Any]        =					np.zeros((num_labels,)      ,dtype=np.floataa     )
  _lowerCAmelCase						:	Any        =					np.zeros((num_labels,)      ,dtype=np.floataa     )
  _lowerCAmelCase						:	List[Any]        =					np.zeros((num_labels,)      ,dtype=np.floataa     )
  for result, gt_seg_map in zip(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					intersect_and_union(
        _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
    total_area_intersect += area_intersect
    total_area_union += area_union
    total_area_pred_label += area_pred_label
    total_area_label += area_label
  return total_area_intersect, total_area_union, total_area_pred_label, total_area_label





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	Dict      ,_lowerCamelCase						:	bool      ,_lowerCamelCase						:	Optional[int] = None      ,_lowerCamelCase						:	Optional[Dict[int, int]] = None      ,_lowerCamelCase						:	bool = False      ,)	->				Optional[Any]:
  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					total_intersect_and_union(
      _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )

  # compute metrics
  _lowerCAmelCase						:	int        =					{}

  _lowerCAmelCase						:	Tuple        =					total_area_intersect.sum() / total_area_label.sum()
  _lowerCAmelCase						:	Optional[int]        =					total_area_intersect / total_area_union
  _lowerCAmelCase						:	Optional[int]        =					total_area_intersect / total_area_label

  _lowerCAmelCase						:	Dict        =					np.nanmean(_lowerCamelCase     )
  _lowerCAmelCase						:	List[str]        =					np.nanmean(_lowerCamelCase     )
  _lowerCAmelCase						:	List[Any]        =					all_acc
  _lowerCAmelCase						:	int        =					iou
  _lowerCAmelCase						:	int        =					acc

  if nan_to_num is not None:
    _lowerCAmelCase						:	Dict        =					{metric: np.nan_to_num(_lowerCamelCase      ,nan=_lowerCamelCase     ) for metric, metric_value in metrics.items()}

  return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,     _KWARGS_DESCRIPTION		)
class 						__A							(			datasets.Metric		):






     def    __A  (		self  ):
       return datasets.MetricInfo(
           description=_DESCRIPTION    ,					citation=_CITATION    ,					inputs_description=_KWARGS_DESCRIPTION    ,					features=datasets.Features(
               # 1st Seq - height dim, 2nd - width dim
               {
                   """predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""  )  )  ),
                   """references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""  )  )  ),
               }  )    ,					reference_urls=[
               """https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
           ]    ,					)






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = False    ,					):
       _lowerCAmelCase						:	List[Any]        =					mean_iou(
           results=a__    ,					gt_seg_maps=a__    ,					num_labels=a__    ,					ignore_index=a__    ,					nan_to_num=a__    ,					label_map=a__    ,					reduce_labels=a__    ,					)
       return iou_result





"""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__  )




