




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken







@torch.no_grad()
def 					lowerCAmelCase    (      UpperCAmelCase,      UpperCAmelCase,      UpperCAmelCase,      UpperCAmelCase,      UpperCAmelCase			)    ->Dict:






					"""simple docstring"""






					with open(__snake_case			) as metadata_file:
										__magic_name__   :				List[str]						  =				json.load(__snake_case			)
					__magic_name__   :				List[Any]						  =				LukeConfig(use_entity_aware_attention=__snake_case,      **metadata['''model_config''']			)

					# Load in the weights from the checkpoint_path
					__magic_name__   :				Dict						  =				torch.load(__snake_case,      map_location='''cpu'''			)['''module''']

					# Load the entity vocab file
					__magic_name__   :				Optional[int]						  =				load_original_entity_vocab(__snake_case			)
					# add an entry for [MASK2]
					__magic_name__   :				List[str]						  =				max(entity_vocab.values()			) + 1
					config.entity_vocab_size += 1

					__magic_name__   :				Dict						  =				XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']			)

					# Add special tokens to the token vocabulary for downstream tasks
					__magic_name__   :				Any						  =				AddedToken('''<ent>''',      lstrip=__snake_case,      rstrip=__snake_case			)
					__magic_name__   :				Dict						  =				AddedToken('''<ent2>''',      lstrip=__snake_case,      rstrip=__snake_case			)
					tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}			)
					config.vocab_size += 2

					print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''			)
					tokenizer.save_pretrained(__snake_case			)
					with open(os.path.join(__snake_case,      '''tokenizer_config.json'''			),      '''r'''			) as f:
										__magic_name__   :				Union[str, Any]						  =				json.load(__snake_case			)
					__magic_name__   :				List[Any]						  =				'''MLukeTokenizer'''
					with open(os.path.join(__snake_case,      '''tokenizer_config.json'''			),      '''w'''			) as f:
										json.dump(__snake_case,      __snake_case			)

					with open(os.path.join(__snake_case,      MLukeTokenizer.vocab_files_names['''entity_vocab_file''']			),      '''w'''			) as f:
										json.dump(__snake_case,      __snake_case			)

					__magic_name__   :				int						  =				MLukeTokenizer.from_pretrained(__snake_case			)

					# Initialize the embeddings of the special tokens
					__magic_name__   :				Optional[Any]						  =				tokenizer.convert_tokens_to_ids(['''@''']			)[0]
					__magic_name__   :				List[Any]						  =				tokenizer.convert_tokens_to_ids(['''#''']			)[0]

					__magic_name__   :				Union[str, Any]						  =				state_dict['''embeddings.word_embeddings.weight''']
					__magic_name__   :				Optional[Any]						  =				word_emb[ent_init_index].unsqueeze(0			)
					__magic_name__   :				Optional[int]						  =				word_emb[enta_init_index].unsqueeze(0			)
					__magic_name__   :				List[str]						  =				torch.cat([word_emb, ent_emb, enta_emb]			)
					# add special tokens for 'entity_predictions.bias'
					for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
										__magic_name__   :				List[Any]						  =				state_dict[bias_name]
										__magic_name__   :				Any						  =				decoder_bias[ent_init_index].unsqueeze(0			)
										__magic_name__   :				List[str]						  =				decoder_bias[enta_init_index].unsqueeze(0			)
										__magic_name__   :				Tuple						  =				torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

					# Initialize the query layers of the entity-aware self-attention mechanism
					for layer_index in range(config.num_hidden_layers			):
										for matrix_name in ["query.weight", "query.bias"]:
															__magic_name__   :				Any						  =				F'''encoder.layer.{layer_index}.attention.self.'''
															__magic_name__   :				Any						  =				state_dict[prefix + matrix_name]
															__magic_name__   :				Optional[int]						  =				state_dict[prefix + matrix_name]
															__magic_name__   :				List[str]						  =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					__magic_name__   :				Tuple						  =				state_dict['''entity_embeddings.entity_embeddings.weight''']
					__magic_name__   :				str						  =				entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0			)
					__magic_name__   :				Tuple						  =				torch.cat([entity_emb, entity_mask_emb]			)
					# add [MASK2] for 'entity_predictions.bias'
					__magic_name__   :				Any						  =				state_dict['''entity_predictions.bias''']
					__magic_name__   :				int						  =				entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0			)
					__magic_name__   :				int						  =				torch.cat([entity_prediction_bias, entity_mask_bias]			)

					__magic_name__   :				Dict						  =				LukeForMaskedLM(config=__snake_case			).eval()

					state_dict.pop('''entity_predictions.decoder.weight'''			)
					state_dict.pop('''lm_head.decoder.weight'''			)
					state_dict.pop('''lm_head.decoder.bias'''			)
					__magic_name__   :				Any						  =				OrderedDict()
					for key, value in state_dict.items():
										if not (key.startswith('''lm_head'''			) or key.startswith('''entity_predictions'''			)):
															__magic_name__   :				Optional[Any]						  =				state_dict[key]
										else:
															__magic_name__   :				Optional[int]						  =				state_dict[key]

					__magic_name__  ,					__magic_name__   :				Union[str, Any]						  =				model.load_state_dict(__snake_case,      strict=__snake_case			)

					if set(__snake_case			) != {"luke.embeddings.position_ids"}:
										raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''			)
					if set(__snake_case			) != {
					    "lm_head.decoder.weight",
					    "lm_head.decoder.bias",
					    "entity_predictions.decoder.weight",
					}:
										raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''			)

					model.tie_weights()
					assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
					assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

					# Check outputs
					__magic_name__   :				Optional[int]						  =				MLukeTokenizer.from_pretrained(__snake_case,      task='''entity_classification'''			)

					__magic_name__   :				Optional[int]						  =				'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
					__magic_name__   :				str						  =				(0, 9)
					__magic_name__   :				Optional[Any]						  =				tokenizer(__snake_case,      entity_spans=[span],      return_tensors='''pt'''			)

					__magic_name__   :				Optional[int]						  =				model(**__snake_case			)

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										__magic_name__   :				Optional[int]						  =				torch.Size((1, 33, 768)			)
										__magic_name__   :				int						  =				torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]			)

					if not (outputs.last_hidden_state.shape == expected_shape):
										raise ValueError(
										    F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''			)
					if not torch.allclose(outputs.last_hidden_state[0, :3, :3],      __snake_case,      atol=1E-4			):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										__magic_name__   :				str						  =				torch.Size((1, 1, 768)			)
										__magic_name__   :				Optional[Any]						  =				torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]			)

					if not (outputs.entity_last_hidden_state.shape == expected_shape):
										raise ValueError(
										    F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
										    F''' {expected_shape}'''			)
					if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],      __snake_case,      atol=1E-4			):
										raise ValueError

					# Verify masked word/entity prediction
					__magic_name__   :				Optional[int]						  =				MLukeTokenizer.from_pretrained(__snake_case			)
					__magic_name__   :				List[Any]						  =				'''Tokyo is the capital of <mask>.'''
					__magic_name__   :				List[Any]						  =				(24, 30)
					__magic_name__   :				str						  =				tokenizer(__snake_case,      entity_spans=[span],      return_tensors='''pt'''			)

					__magic_name__   :				List[str]						  =				model(**__snake_case			)

					__magic_name__   :				str						  =				encoding['''input_ids'''][0].tolist()
					__magic_name__   :				Dict						  =				input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''			)			)
					__magic_name__   :				Optional[Any]						  =				outputs.logits[0][mask_position_id].argmax(dim=-1			)
					assert "Japan" == tokenizer.decode(__snake_case			)

					__magic_name__   :				Optional[int]						  =				outputs.entity_logits[0][0].argmax().item()
					__magic_name__   :				List[Any]						  =				[
					    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
					]
					assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''			)][0] == "en:Japan"

					# Finally, save our PyTorch model and tokenizer
					print('''Saving PyTorch model to {}'''.format(__snake_case			)			)
					model.save_pretrained(__snake_case			)







def 					lowerCAmelCase    (      UpperCAmelCase			)    ->Optional[int]:






					"""simple docstring"""






					__magic_name__   :				str						  =				['''[MASK]''', '''[PAD]''', '''[UNK]''']

					__magic_name__   :				Optional[int]						  =				[json.loads(__snake_case			) for line in open(__snake_case			)]

					__magic_name__   :				List[str]						  =				{}
					for entry in data:
										__magic_name__   :				Union[str, Any]						  =				entry['''id''']
										for entity_name, language in entry["entities"]:
															if entity_name in SPECIAL_TOKENS:
																				__magic_name__   :				Optional[int]						  =				entity_id
																				break
															__magic_name__   :				str						  =				F'''{language}:{entity_name}'''
															__magic_name__   :				List[Any]						  =				entity_id
					return new_mapping


if __name__ == "__main__":
				lowercase_						=				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
				parser.add_argument(
				    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
				)
				parser.add_argument(
				    '''--entity_vocab_path''',
				    default=None,
				    type=str,
				    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
				)
				lowercase_						=				parser.parse_args()
				convert_luke_checkpoint(
				    args.checkpoint_path,
				    args.metadata_path,
				    args.entity_vocab_path,
				    args.pytorch_dump_folder_path,
				    args.model_size,
				)




'''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 )





