




import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


_UpperCAmelCase	:						Optional[Any]			=     logging.getLogger(__name__)




class    __lowerCAmelCase       (							__A):
       _a 					=						'''sequence-classification'''






       def __init__(   self:					Any       ,					_lowerCAmelCase:					Optional[int]				):

         if type(_lowerCAmelCase				) == dict:
           lowercase    :Any      	=						Namespace(**_lowerCAmelCase				)
         lowercase    :int      	=						glue_output_modes[hparams.task]
         lowercase    :Any      	=						glue_tasks_num_labels[hparams.task]

         super().__init__(_lowerCAmelCase       ,					_lowerCAmelCase       ,					self.mode				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					**_lowerCAmelCase:					Any				):
         return self.model(**_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					Union[str, Any]       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :Optional[int]      	=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

         if self.config.model_type not in ["distilbert", "bart"]:
           lowercase    :str      	=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

         lowercase    :Union[str, Any]      	=						self(**_lowerCAmelCase				)
         lowercase    :List[Any]      	=						outputs[0]

         lowercase    :str      	=						self.trainer.lr_schedulers[0]['''scheduler''']
         lowercase    :Union[str, Any]      	=						{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss, "log": tensorboard_logs}






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         lowercase    :Union[str, Any]      	=						self.hparams
         lowercase    :str      	=						processors[args.task]()
         lowercase    :Optional[int]      	=						processor.get_labels()

         for mode in ["train", "dev"]:
           lowercase    :List[str]      	=						self._feature_file(_lowerCAmelCase				)
           if os.path.exists(_lowerCAmelCase				) and not args.overwrite_cache:
             logger.info("Loading features from cached file %s"       ,					_lowerCAmelCase				)
           else:
             logger.info("Creating features from dataset file at %s"       ,					args.data_dir				)
             lowercase    :Optional[int]      	=						(
                 processor.get_dev_examples(args.data_dir				)
                 if mode == '''dev'''
                 else processor.get_train_examples(args.data_dir				)
             )
             lowercase    :Optional[int]      	=						convert_examples_to_features(
                 _lowerCAmelCase       ,					self.tokenizer       ,					max_length=args.max_seq_length       ,					label_list=self.labels       ,					output_mode=args.glue_output_mode       ,					)
             logger.info("Saving features into cached file %s"       ,					_lowerCAmelCase				)
             torch.save(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					bool = False				):
         lowercase    :Tuple      	=						'''dev''' if mode == '''test''' else mode

         lowercase    :Any      	=						self._feature_file(_lowerCAmelCase				)
         logger.info("Loading features from cached file %s"       ,					_lowerCAmelCase				)
         lowercase    :Optional[Any]      	=						torch.load(_lowerCAmelCase				)
         lowercase    :int      	=						torch.tensor([f.input_ids for f in features]       ,					dtype=torch.long				)
         lowercase    :str      	=						torch.tensor([f.attention_mask for f in features]       ,					dtype=torch.long				)
         lowercase    :Tuple      	=						torch.tensor([f.token_type_ids for f in features]       ,					dtype=torch.long				)
         if self.hparams.glue_output_mode == "classification":
           lowercase    :int      	=						torch.tensor([f.label for f in features]       ,					dtype=torch.long				)
         elif self.hparams.glue_output_mode == "regression":
           lowercase    :int      	=						torch.tensor([f.label for f in features]       ,					dtype=torch.float				)

         return DataLoader(
             TensorDataset(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)       ,					batch_size=_lowerCAmelCase       ,					shuffle=_lowerCAmelCase       ,					)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					Dict				):
         lowercase    :Any      	=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

         if self.config.model_type not in ["distilbert", "bart"]:
           lowercase    :Any      	=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

         lowercase    :Union[str, Any]      	=						self(**_lowerCAmelCase				)
         lowercase    :List[Any]      	=						outputs[:2]
         lowercase    :Union[str, Any]      	=						logits.detach().cpu().numpy()
         lowercase    :int      	=						inputs['''labels'''].detach().cpu().numpy()

         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					Dict				):
         lowercase    :Optional[Any]      	=						torch.stack([x["val_loss"] for x in outputs]				).mean().detach().cpu().item()
         lowercase    :List[str]      	=						np.concatenate([x["pred"] for x in outputs]       ,					axis=0				)

         if self.hparams.glue_output_mode == "classification":
           lowercase    :Optional[Any]      	=						np.argmax(_lowerCAmelCase       ,					axis=1				)
         elif self.hparams.glue_output_mode == "regression":
           lowercase    :int      	=						np.squeeze(_lowerCAmelCase				)

         lowercase    :Any      	=						np.concatenate([x["target"] for x in outputs]       ,					axis=0				)
         lowercase    :int      	=						[[] for _ in range(out_label_ids.shape[0]				)]
         lowercase    :Tuple      	=						[[] for _ in range(out_label_ids.shape[0]				)]

         lowercase    :Any      	=						{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)}

         lowercase    :Optional[Any]      	=						dict(results.items()				)
         lowercase    :str      	=						results
         return ret, preds_list, out_label_list






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					list				):
         lowercase    :Dict      	=						self._eval_end(_lowerCAmelCase				)
         lowercase    :Any      	=						ret['''log''']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					Tuple				):
         lowercase    :Union[str, Any]      	=						self._eval_end(_lowerCAmelCase				)
         lowercase    :Optional[Any]      	=						ret['''log''']
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






       @staticmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					str       ,					_lowerCAmelCase:					Any				):
         BaseTransformer.add_model_specific_args(_lowerCAmelCase       ,					_lowerCAmelCase				)
         parser.add_argument(
             "--max_seq_length"       ,					default=1_28       ,					type=_lowerCAmelCase       ,					help=(
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )       ,					)

         parser.add_argument(
             "--task"       ,					default=""       ,					type=_lowerCAmelCase       ,					required=_lowerCAmelCase       ,					help="The GLUE task to run"       ,					)
         parser.add_argument(
             "--gpus"       ,					default=0       ,					type=_lowerCAmelCase       ,					help="The number of GPUs allocated for this, it is by default 0 meaning none"       ,					)

         parser.add_argument(
             "--overwrite_cache"       ,					action="store_true"       ,					help="Overwrite the cached training and evaluation sets"				)

         return parser







def       UpperCAmelCase__  (     ):
  lowercase    :Tuple      	=						argparse.ArgumentParser()
  add_generic_args(__A,			os.getcwd()   )
  lowercase    :Any      	=						GLUETransformer.add_model_specific_args(__A,			os.getcwd()   )
  lowercase    :Optional[Any]      	=						parser.parse_args()

  # If output_dir not provided, a folder will be generated in pwd
  if args.output_dir is None:
    lowercase    :Tuple      	=						os.path.join(
        "./results",			F"{args.task}_{time.strftime('%Y%m%d_%H%M%S'   )}",			)
    os.makedirs(args.output_dir   )

  lowercase    :str      	=						GLUETransformer(__A   )
  lowercase    :Any      	=						generic_train(__A,			__A   )

  # Optionally, predict on dev set and write to output_dir
  if args.do_predict:
    lowercase    :Tuple      	=						sorted(glob.glob(os.path.join(args.output_dir,			"checkpoint-epoch=*.ckpt"   ),			recursive=__A   )   )
    lowercase    :int      	=						model.load_from_checkpoint(checkpoints[-1]   )
    return trainer.test(__A   )


if __name__ == "__main__":
     main()









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       UpperCAmelCase__  (     ):
  lowercase    :List[str]      	=						torch.nn.Linear(2,			4   )
  lowercase    :List[Any]      	=						torch.optim.AdamW(model.parameters(),			lr=1.0   )
  lowercase    :int      	=						torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase,			max_lr=0.01,			steps_per_epoch=2,			epochs=1   )
  lowercase    :Optional[int]      	=						DataLoader(TensorDataset(torch.tensor([1, 2, 3]   )   )   )
  lowercase    :List[str]      	=						DataLoader(TensorDataset(torch.tensor([4, 5, 6]   )   )   )

  return model, optimizer, scheduler, train_dl, valid_dl



def       UpperCAmelCase__  (     lowerCamelCase   ):
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Dict      	=						torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
  model.load_state_dict(lowerCamelCase   )







class    __lowerCAmelCase       (							lowerCAmelCase):






       @require_cuda
       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[str]      	=						Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(_lowerCAmelCase				):
           lowercase    :Any      	=						Accelerator(cpu=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Any      	=						Accelerator()
         lowercase    :Dict      	=						GradientState()
         assert state.num_steps == 1
         lowercase    :Tuple      	=						4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         lowercase    :List[Any]      	=						False
         assert state.sync_gradients is False
         GradientState._reset_state()






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :str      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :Any      	=						create_components()

         (
             (
             lowercase
         )	,    (
             lowercase
         )	,    (
             lowercase
         )	,    (
             lowercase
         )	,    (
             lowercase
         )	,    
         )    :Tuple      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Dict      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :List[str]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					int				):
         PartialState._reset_state()

         # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
         def noop(*_lowerCAmelCase:					List[str]       ,					**_lowerCAmelCase:					Optional[int]				):
           pass

         with patch("torch.cuda.set_device"       ,					_lowerCAmelCase				), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"				):
           lowercase    :List[Any]      	=						Accelerator()
           self.assertEqual(str(accelerator.state.device				)       ,					"cuda:64"				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Tuple      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :Optional[Any]      	=						create_components()
         accelerator.prepare(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)

         lowercase    :Tuple      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :Dict      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :int      	=						create_components()
         accelerator.prepare(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)

         lowercase    :int      	=						get_signature(_lowerCAmelCase				)

         # saving hook
         def save_config(_lowerCAmelCase:					Union[str, Any]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					List[Any]				):
           lowercase    :Dict      	=						{"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:					Union[str, Any]       ,					_lowerCAmelCase:					Union[str, Any]				):
           with open(os.path.join(_lowerCAmelCase       ,					"data.json"				)       ,					"r"				) as f:
             lowercase    :int      	=						json.load(_lowerCAmelCase				)

           lowercase    :Optional[int]      	=						config["class_name"]

         lowercase    :Optional[Any]      	=						accelerator.register_save_state_pre_hook(_lowerCAmelCase				)
         lowercase    :Tuple      	=						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
           lowercase    :Optional[int]      	=						"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
           lowercase    :str      	=						"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 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :List[str]      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :List[Any]      	=						create_components()
         lowercase    :List[str]      	=						None

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






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Union[str, Any]      	=						Accelerator()
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :Union[str, Any]      	=						create_components()
         lowercase    :Union[str, Any]      	=						[1, 2, 3]

         # This should work
         lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :List[str]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         from transformers import AutoModelForCausalLM

         lowercase    :Any      	=						AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"       ,					load_in_abit=_lowerCAmelCase       ,					device_map={"": 0}       ,					)
         lowercase    :int      	=						Accelerator()

         # This should work
         lowercase    :Optional[int]      	=						accelerator.prepare(_lowerCAmelCase				)






       @slow
       @require_bnb
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         from transformers import AutoModelForCausalLM

         lowercase    :Optional[Any]      	=						Accelerator()

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

         lowercase    :str      	=						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				):
           lowercase    :Dict      	=						accelerator.prepare(_lowerCAmelCase				)






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

         lowercase    :List[str]      	=						{"distributed_type": DistributedType.MULTI_GPU}

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

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

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

         PartialState._reset_state()






       @slow
       @require_bnb
       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         from transformers import AutoModelForCausalLM

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

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

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






       @require_cuda
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Optional[int]      	=						torch.nn.Linear(10       ,					10				)
         lowercase    :Optional[int]      	=						torch.optim.SGD(model.parameters()       ,					lr=0.01				)
         lowercase    :List[Any]      	=						Accelerator(cpu=_lowerCAmelCase				)
         lowercase    :List[str]      	=						accelerator.prepare(_lowerCAmelCase				)



