




import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMSNForImageClassification, ViTMSNModel
				from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor





class 						A__							:






	def __init__(      self							,	lowerCamelCase							,	lowerCamelCase=13							,	lowerCamelCase=30							,	lowerCamelCase=2							,	lowerCamelCase=3							,	lowerCamelCase=True							,	lowerCamelCase=True							,	lowerCamelCase=32							,	lowerCamelCase=5							,	lowerCamelCase=4							,	lowerCamelCase=37							,	lowerCamelCase="gelu"							,	lowerCamelCase=0.1							,	lowerCamelCase=0.1							,	lowerCamelCase=10							,	lowerCamelCase=0.0_2							,	lowerCamelCase=None							,	) ->  Any:




						"""simple docstring"""






						__magic_name__   :				int						  =				parent
						__magic_name__   :				List[str]						  =				batch_size
						__magic_name__   :				Union[str, Any]						  =				image_size
						__magic_name__   :				Optional[int]						  =				patch_size
						__magic_name__   :				Tuple						  =				num_channels
						__magic_name__   :				str						  =				is_training
						__magic_name__   :				Union[str, Any]						  =				use_labels
						__magic_name__   :				List[str]						  =				hidden_size
						__magic_name__   :				Dict						  =				num_hidden_layers
						__magic_name__   :				Any						  =				num_attention_heads
						__magic_name__   :				Dict						  =				intermediate_size
						__magic_name__   :				Optional[Any]						  =				hidden_act
						__magic_name__   :				Any						  =				hidden_dropout_prob
						__magic_name__   :				List[str]						  =				attention_probs_dropout_prob
						__magic_name__   :				Union[str, Any]						  =				type_sequence_label_size
						__magic_name__   :				Any						  =				initializer_range
						__magic_name__   :				Optional[Any]						  =				scope

						# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						__magic_name__   :				List[Any]						  =				(image_size // patch_size) ** 2
						__magic_name__   :				List[str]						  =				num_patches + 1






	def 					lowercase (      self		) ->  Dict:




						"""simple docstring"""






						__magic_name__   :				List[Any]						  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						__magic_name__   :				Optional[int]						  =				None
						if self.use_labels:
											__magic_name__   :				List[Any]						  =				ids_tensor([self.batch_size]							,	self.type_sequence_label_size		)

						__magic_name__   :				Any						  =				self.get_config()

						return config, pixel_values, labels






	def 					lowercase (      self		) ->  Dict:




						"""simple docstring"""






						return ViTMSNConfig(
						    image_size=self.image_size							,	patch_size=self.patch_size							,	num_channels=self.num_channels							,	hidden_size=self.hidden_size							,	num_hidden_layers=self.num_hidden_layers							,	num_attention_heads=self.num_attention_heads							,	intermediate_size=self.intermediate_size							,	hidden_act=self.hidden_act							,	hidden_dropout_prob=self.hidden_dropout_prob							,	attention_probs_dropout_prob=self.attention_probs_dropout_prob							,	initializer_range=self.initializer_range							,	)






	def 					lowercase (      self							,	lowerCamelCase							,	lowerCamelCase							,	lowerCamelCase		) ->  List[Any]:




						"""simple docstring"""






						__magic_name__   :				List[str]						  =				ViTMSNModel(config=_lowerCamelCase		)
						model.to(_lowerCamelCase		)
						model.eval()
						__magic_name__   :				Any						  =				model(_lowerCamelCase		)
						self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)		)






	def 					lowercase (      self							,	lowerCamelCase							,	lowerCamelCase							,	lowerCamelCase		) ->  Tuple:




						"""simple docstring"""






						__magic_name__   :				str						  =				self.type_sequence_label_size
						__magic_name__   :				Any						  =				ViTMSNForImageClassification(_lowerCamelCase		)
						model.to(_lowerCamelCase		)
						model.eval()
						__magic_name__   :				Tuple						  =				model(_lowerCamelCase							,	labels=_lowerCamelCase		)
						print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''		)
						print('''Labels: {labels}'''		)
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						__magic_name__   :				Tuple						  =				1
						__magic_name__   :				int						  =				ViTMSNForImageClassification(_lowerCamelCase		)
						model.to(_lowerCamelCase		)
						model.eval()

						__magic_name__   :				int						  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						__magic_name__   :				Optional[int]						  =				model(_lowerCamelCase		)
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)		)






	def 					lowercase (      self		) ->  Optional[int]:




						"""simple docstring"""






						__magic_name__   :				Optional[Any]						  =				self.prepare_config_and_inputs()
						__magic_name__  ,					__magic_name__  ,					__magic_name__   :				Tuple						  =				config_and_inputs
						__magic_name__   :				Union[str, Any]						  =				{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_torch
class 						A__							(  __a						,	__a						,	unittest.TestCase					):
	lowerCamelCase__						:					Optional[Any]							       =(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
	lowerCamelCase__						:					List[str]							       =(
	    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
	    if is_torch_available()
	    else {}
	)

	lowerCamelCase__						:					int							       =False
	lowerCamelCase__						:					Union[str, Any]							       =False
	lowerCamelCase__						:					Optional[int]							       =False
	lowerCamelCase__						:					List[Any]							       =False






	def 					lowercase (      self		) ->  Any:




						"""simple docstring"""






						__magic_name__   :				List[Any]						  =				ViTMSNModelTester(self		)
						__magic_name__   :				List[str]						  =				ConfigTester(self							,	config_class=_lowerCamelCase							,	has_text_modality=_lowerCamelCase							,	hidden_size=37		)






	def 					lowercase (      self		) ->  Tuple:




						"""simple docstring"""






						self.config_tester.run_common_tests()






	@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''		)
	def 					lowercase (      self		) ->  Dict:




						"""simple docstring"""






						pass






	def 					lowercase (      self		) ->  Union[str, Any]:




						"""simple docstring"""






						__magic_name__  ,					__magic_name__   :				Union[str, Any]						  =				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__magic_name__   :				Union[str, Any]						  =				model_class(_lowerCamelCase		)
											self.assertIsInstance(model.get_input_embeddings()							,	(nn.Module)		)
											__magic_name__   :				List[str]						  =				model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_lowerCamelCase							,	nn.Linear		)		)






	def 					lowercase (      self		) ->  Tuple:




						"""simple docstring"""






						__magic_name__  ,					__magic_name__   :				List[str]						  =				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__magic_name__   :				Dict						  =				model_class(_lowerCamelCase		)
											__magic_name__   :				Any						  =				inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__magic_name__   :				Any						  =				[*signature.parameters.keys()]

											__magic_name__   :				Optional[int]						  =				['''pixel_values''']
											self.assertListEqual(arg_names[:1]							,	_lowerCamelCase		)






	def 					lowercase (      self		) ->  Any:




						"""simple docstring"""






						__magic_name__   :				int						  =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_lowerCamelCase		)






	def 					lowercase (      self		) ->  Tuple:




						"""simple docstring"""






						__magic_name__   :				Optional[Any]						  =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase		)






	@slow
	def 					lowercase (      self		) ->  List[str]:




						"""simple docstring"""






						for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__magic_name__   :				Optional[int]						  =				ViTMSNModel.from_pretrained(_lowerCamelCase		)
											self.assertIsNotNone(_lowerCamelCase		)







def 					lowerCAmelCase    (      )    ->Tuple:






					"""simple docstring"""






					__magic_name__   :				Union[str, Any]						  =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					return image





@require_torch
@require_vision
class 						A__							(  unittest.TestCase					):






	@cached_property
	def 					lowercase (      self		) ->  Any:




						"""simple docstring"""






						return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''		) if is_vision_available() else None






	@slow
	def 					lowercase (      self		) ->  Optional[int]:




						"""simple docstring"""






						torch.manual_seed(2		)
						__magic_name__   :				int						  =				ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''		).to(_lowerCamelCase		)

						__magic_name__   :				List[str]						  =				self.default_image_processor
						__magic_name__   :				List[Any]						  =				prepare_img()
						__magic_name__   :				Optional[int]						  =				image_processor(images=_lowerCamelCase							,	return_tensors='''pt'''		).to(_lowerCamelCase		)

						# forward pass
						with torch.no_grad():
											__magic_name__   :				List[Any]						  =				model(**_lowerCamelCase		)

						# verify the logits
						__magic_name__   :				Optional[int]						  =				torch.Size((1, 1000)		)
						self.assertEqual(outputs.logits.shape							,	_lowerCamelCase		)

						__magic_name__   :				Union[str, Any]						  =				torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]		).to(_lowerCamelCase		)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]							,	_lowerCamelCase							,	atol=1e-4		)		)




'''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 snake_case_      (					)			->					str:
   lowerCAmelCase_			=      torch.nn.Linear(2	,   4)
   lowerCAmelCase_			=      torch.optim.AdamW(model.parameters()	,   lr=1.0)
   lowerCAmelCase_			=      torch.optim.lr_scheduler.OneCycleLR(__snake_case	,   max_lr=0.0_1	,   steps_per_epoch=2	,   epochs=1)
   lowerCAmelCase_			=      DataLoader(TensorDataset(torch.tensor([1, 2, 3])))
   lowerCAmelCase_			=      DataLoader(TensorDataset(torch.tensor([4, 5, 6])))

   return model, optimizer, scheduler, train_dl, valid_dl



def snake_case_      (					__snake_case  :							Any)			->					List[Any]:
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def snake_case_      (					__snake_case  :							Optional[int])			->					Any:
   lowerCAmelCase_			=      torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
   model.load_state_dict(__snake_case)






class     __UpperCAmelCase       ( __a		):





  @require_cuda
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     assert PartialState._shared_state["_cpu"] is False
     assert PartialState._shared_state["device"].type == "cuda"
     with self.assertRaises(_lowerCamelCase ):
        lowerCAmelCase_			=      Accelerator(cpu=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_			=      GradientState()
     assert state.num_steps == 1
     lowerCAmelCase_			=      4
     assert state.num_steps == 4

     assert state.sync_gradients is True
     lowerCAmelCase_			=      False
     assert state.sync_gradients is False
     GradientState._reset_state()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()

     (
         (
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,
     )			=      accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     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 						UpperCAmelCase_	(	self ):
     PartialState._reset_state()

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

     with patch('''torch.cuda.set_device'''    ,     _lowerCamelCase ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64''' ):
        lowerCAmelCase_			=      Accelerator()
        self.assertEqual(str(accelerator.state.device )    ,     '''cuda:64''' )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      get_signature(_lowerCamelCase )

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

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

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      get_signature(_lowerCamelCase )

     # saving hook
     def save_config(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      {'''class_name''': models[0].__class__.__name__}

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

        # loading hook
     def load_config(_lowerCamelCase    ,     _lowerCamelCase ):
        with open(os.path.join(_lowerCamelCase    ,     '''data.json''' )    ,     '''r''' ) as f:
           lowerCAmelCase_			=      json.load(_lowerCamelCase )

        lowerCAmelCase_			=      config['''class_name''']

     lowerCAmelCase_			=      accelerator.register_save_state_pre_hook(_lowerCamelCase )
     lowerCAmelCase_			=      accelerator.register_load_state_pre_hook(_lowerCamelCase )

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

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

        # random class name to verify correct one is loaded
        lowerCAmelCase_			=      '''random'''

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

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

        # random class name to verify correct one is loaded
        lowerCAmelCase_			=      '''random'''

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

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     lowerCAmelCase_			=      None

     # This should work
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.prepare(
         _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     self.assertTrue(dummy_obj is None )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     lowerCAmelCase_			=      [1, 2, 3]

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





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

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     load_in_abit=_lowerCamelCase    ,     device_map={'''''': 0}    ,     )
     lowerCAmelCase_			=      Accelerator()

     # This should work
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





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

     lowerCAmelCase_			=      Accelerator()

     with init_empty_weights():
        lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''    ,     )
        model.tie_weights()
        lowerCAmelCase_			=      infer_auto_device_map(_lowerCamelCase )
        lowerCAmelCase_			=      '''cpu'''

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     device_map=_lowerCamelCase    ,     load_in_abit=_lowerCamelCase    ,     llm_inta_enable_fpaa_cpu_offload=_lowerCamelCase )

     # This should not work and get value error
     with self.assertRaises(_lowerCamelCase ):
        lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





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

     lowerCAmelCase_			=      {'''distributed_type''': DistributedType.MULTI_GPU}

     with init_empty_weights():
        lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''    ,     )
        model.tie_weights()
        lowerCAmelCase_			=      infer_auto_device_map(_lowerCamelCase )
        lowerCAmelCase_			=      1

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     load_in_abit=_lowerCamelCase    ,     device_map=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      Accelerator()

     # This should not work and get value error
     with self.assertRaises(_lowerCamelCase ):
        lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )

     PartialState._reset_state()





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

     with init_empty_weights():
        lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''    ,     )
        lowerCAmelCase_			=      infer_auto_device_map(_lowerCamelCase )
        lowerCAmelCase_			=      1

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     load_in_abit=_lowerCamelCase    ,     device_map=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      Accelerator()

     # This should work
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )







  @require_cuda
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      torch.nn.Linear(10    ,     10 )
     lowerCAmelCase_			=      torch.optim.SGD(model.parameters()    ,     lr=0.01 )
     lowerCAmelCase_			=      Accelerator(cpu=_lowerCamelCase )
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





