


"""simple docstring"""







import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowercase    =				logging.getLogger(__name__)


__lowercase    =				list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__lowercase    =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 						_A  :







				"""simple docstring"""




				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={
				        """help""": (
				            """The model checkpoint for weights initialization. Leave None if you want to train a model from"""
				            """ scratch."""
				        )
				    }   ,)
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a__  )}   ,)
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,)






@dataclass
class 						_A  :







				"""simple docstring"""




				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """The input training data file (a text file)."""}  )
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={
				        """help""": (
				            """The input training data files (multiple files in glob format). """
				            """Very often splitting large files to smaller files can prevent tokenizer going out of memory"""
				        )
				    }   ,)
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}   ,)
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """An optional input train ref data file for whole word mask in Chinese."""}   ,)
				UpperCAmelCase	:							Optional[str]     =	field(
				    default=a__   ,metadata={"""help""": """An optional input eval ref data file for whole word mask in Chinese."""}   ,)
				UpperCAmelCase	:							bool     =	field(
				    default=a__   ,metadata={"""help""": """Whether distinct lines of text in the dataset are to be handled as distinct sequences."""}   ,)

				UpperCAmelCase	:							bool     =	field(
				    default=a__   ,metadata={"""help""": """Train with masked-language modeling loss instead of language modeling."""}  )
				UpperCAmelCase	:							bool     =	field(default=a__   ,metadata={"""help""": """Whether ot not to use whole word mask."""}  )
				UpperCAmelCase	:							float     =	field(
				    default=0.15   ,metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}  )
				UpperCAmelCase	:							float     =	field(
				    default=1 / 6   ,metadata={
				        """help""": (
				            """Ratio of length of a span of masked tokens to surrounding context length for permutation language"""
				            """ modeling."""
				        )
				    }   ,)
				UpperCAmelCase	:							int     =	field(
				    default=5   ,metadata={"""help""": """Maximum length of a span of masked tokens for permutation language modeling."""}  )

				UpperCAmelCase	:							int     =	field(
				    default=-1   ,metadata={
				        """help""": (
				            """Optional input sequence length after tokenization."""
				            """The training dataset will be truncated in block of this size for training."""
				            """Default to the model max input length for single sentence inputs (take into account special tokens)."""
				        )
				    }   ,)
				UpperCAmelCase	:							bool     =	field(
				    default=a__   ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )






def 					lowercase      (    A_  ,       A_  ,       A_ = False  ,       A_ = None  ,       )->				Optional[int]:
		'''simple docstring'''







		def _dataset(A_  ,       A_=None	):
				if args.line_by_line:
						if ref_path is not None:
								if not args.whole_word_mask or not args.mlm:
										raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask"	)
								return LineByLineWithRefDataset(
								    tokenizer=_lowercase  ,       file_path=_lowercase  ,       block_size=args.block_size  ,       ref_path=_lowercase  ,       )

						return LineByLineTextDataset(tokenizer=_lowercase  ,       file_path=_lowercase  ,       block_size=args.block_size	)
				else:
						return TextDataset(
						    tokenizer=_lowercase  ,       file_path=_lowercase  ,       block_size=args.block_size  ,       overwrite_cache=args.overwrite_cache  ,       cache_dir=_lowercase  ,       )

		if evaluate:
				return _dataset(args.eval_data_file  ,       args.eval_ref_file	)
		elif args.train_data_files:
				return ConcatDataset([_dataset(_lowercase	) for f in glob(args.train_data_files	)]	)
		else:
				return _dataset(args.train_data_file  ,       args.train_ref_file	)


def 					lowercase      (    )->				Optional[int]:
		'''simple docstring'''







		a						:		Optional[int]											=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
		a						:		Tuple											=      parser.parse_args_into_dataclasses()

		if data_args.eval_data_file is None and training_args.do_eval:
				raise ValueError(
				    "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
				    "or remove the --do_eval argument."	)
		if (
		    os.path.exists(training_args.output_dir	)
		    and os.listdir(training_args.output_dir	)
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
				    " --overwrite_output_dir to overcome."	)

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,       datefmt="%m/%d/%Y %H:%M:%S"  ,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,       )
		logger.warning(
		    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"  ,       training_args.local_rank  ,       training_args.device  ,       training_args.n_gpu  ,       bool(training_args.local_rank != -1	)  ,       training_args.fpaa  ,       )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank	):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("Training/evaluation parameters %s"  ,       _lowercase	)

		# Set seed
		set_seed(training_args.seed	)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		if model_args.config_name:
				a						:		List[str]											=      AutoConfig.from_pretrained(model_args.config_name  ,       cache_dir=model_args.cache_dir	)
		elif model_args.model_name_or_path:
				a						:		Optional[Any]											=      AutoConfig.from_pretrained(model_args.model_name_or_path  ,       cache_dir=model_args.cache_dir	)
		else:
				a						:		List[Any]											=      CONFIG_MAPPING[model_args.model_type]()
				logger.warning("You are instantiating a new config instance from scratch."	)

		if model_args.tokenizer_name:
				a						:		Any											=      AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,       cache_dir=model_args.cache_dir	)
		elif model_args.model_name_or_path:
				a						:		Dict											=      AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,       cache_dir=model_args.cache_dir	)
		else:
				raise ValueError(
				    "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
				    " script, save it,and load it from here, using --tokenizer_name"	)

		if model_args.model_name_or_path:
				a						:		Tuple											=      AutoModelWithLMHead.from_pretrained(
				    model_args.model_name_or_path  ,       from_tf=bool(".ckpt" in model_args.model_name_or_path	)  ,       config=_lowercase  ,       cache_dir=model_args.cache_dir  ,       )
		else:
				logger.info("Training new model from scratch"	)
				a						:		Any											=      AutoModelWithLMHead.from_config(_lowercase	)

		model.resize_token_embeddings(len(_lowercase	)	)

		if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
				raise ValueError(
				    "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
				    "--mlm flag (masked language modeling)."	)

		if data_args.block_size <= 0:
				a						:		int											=      tokenizer.max_len
				# Our input block size will be the max possible for the model
		else:
				a						:		int											=      min(data_args.block_size  ,       tokenizer.max_len	)

		# Get datasets

		a						:		int											=      (
		    get_dataset(_lowercase  ,       tokenizer=_lowercase  ,       cache_dir=model_args.cache_dir	) if training_args.do_train else None
		)
		a						:		List[Any]											=      (
		    get_dataset(_lowercase  ,       tokenizer=_lowercase  ,       evaluate=_lowercase  ,       cache_dir=model_args.cache_dir	)
		    if training_args.do_eval
		    else None
		)
		if config.model_type == "xlnet":
				a						:		Any											=      DataCollatorForPermutationLanguageModeling(
				    tokenizer=_lowercase  ,       plm_probability=data_args.plm_probability  ,       max_span_length=data_args.max_span_length  ,       )
		else:
				if data_args.mlm and data_args.whole_word_mask:
						a						:		Any											=      DataCollatorForWholeWordMask(
						    tokenizer=_lowercase  ,       mlm_probability=data_args.mlm_probability	)
				else:
						a						:		Tuple											=      DataCollatorForLanguageModeling(
						    tokenizer=_lowercase  ,       mlm=data_args.mlm  ,       mlm_probability=data_args.mlm_probability	)

    # Initialize our Trainer
		a						:		Optional[Any]											=      Trainer(
		    model=_lowercase  ,       args=_lowercase  ,       data_collator=_lowercase  ,       train_dataset=_lowercase  ,       eval_dataset=_lowercase  ,       prediction_loss_only=_lowercase  ,       )

		# Training
		if training_args.do_train:
				a						:		Dict											=      (
				    model_args.model_name_or_path
				    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path	)
				    else None
				)
				trainer.train(model_path=_lowercase	)
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
		a						:		List[str]											=      {}
		if training_args.do_eval:
				logger.info("*** Evaluate ***"	)

				a						:		int											=      trainer.evaluate()

				a						:		List[Any]											=      math.exp(eval_output["eval_loss"]	)
				a						:		Tuple											=      {"perplexity": perplexity}

				a						:		int											=      os.path.join(training_args.output_dir  ,       "eval_results_lm.txt"	)
				if trainer.is_world_master():
						with open(_lowercase  ,       "w"	) as writer:
								logger.info("***** Eval results *****"	)
								for key in sorted(result.keys()	):
										logger.info("  %s = %s"  ,       _lowercase  ,       str(result[key]	)	)
										writer.write("%s = %s\n" % (key, str(result[key]	))	)

				results.update(_lowercase	)

		return results


def 					lowercase      (    A_	)->				List[Any]:
		'''simple docstring'''







		main()


if __name__ == "__main__":
	main()










"""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 					lowercase      (    )->				Union[str, Any]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      torch.nn.Linear(2  ,       4	)
  a						:		Tuple											=      torch.optim.AdamW(model.parameters()  ,       lr=1.0	)
  a						:		Union[str, Any]											=      torch.optim.lr_scheduler.OneCycleLR(A_  ,       max_lr=0.0_1  ,       steps_per_epoch=2  ,       epochs=1	)
  a						:		List[str]											=      DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
  a						:		int											=      DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

  return model, optimizer, scheduler, train_dl, valid_dl


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  return (model.weight.abs().sum() + model.bias.abs().sum()).item()


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Optional[int]											=      torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
  model.load_state_dict(A_	)







class 						_A  (  _a  ):







    """simple docstring"""




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






    def      __snake_case							( self    :   List[Any]):
      a						:		str											=      Accelerator()
      a						:		Optional[Any]											=      GradientState()
      assert state.num_steps == 1
      a						:		Dict											=      4
      assert state.num_steps == 4

      assert state.sync_gradients is True
      a						:		Optional[int]											=      False
      assert state.sync_gradients is False
      GradientState._reset_state()






    def      __snake_case							( self    :   str):
      a						:		int											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Tuple											=      create_components()

      (
          (
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		
      )						:		Tuple											=      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      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      __snake_case							( self    :   Dict):
      a						:		Dict											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Any											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      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      __snake_case							( self    :   int):
      PartialState._reset_state()

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

      with patch("torch.cuda.set_device"    ,			__UpperCAmelCase), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"):
        a						:		int											=      Accelerator()
        self.assertEqual(str(accelerator.state.device)    ,			"cuda:64")






    def      __snake_case							( self    :   List[str]):
      a						:		Tuple											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Optional[Any]											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      get_signature(__UpperCAmelCase)

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

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

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






    def      __snake_case							( self    :   Optional[int]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Dict											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Union[str, Any]											=      get_signature(__UpperCAmelCase)

      # saving hook
      def save_config(__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Union[str, Any]):
        a						:		Tuple											=      {"class_name": models[0].__class__.__name__}

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

        # loading hook
      def load_config(__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]):
        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"r") as f:
          a						:		Optional[Any]											=      json.load(__UpperCAmelCase)

        a						:		Tuple											=      config["class_name"]

      a						:		Optional[int]											=      accelerator.register_save_state_pre_hook(__UpperCAmelCase)
      a						:		Tuple											=      accelerator.register_load_state_pre_hook(__UpperCAmelCase)

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

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

        # random class name to verify correct one is loaded
        a						:		int											=      "random"

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

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

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

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

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






    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[str]											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		int											=      create_components()
      a						:		Tuple											=      None

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		Any											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertTrue(dummy_obj is None)






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

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






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map={"": 0}    ,			)
      a						:		Tuple											=      Accelerator()

      # This should work
      a						:		List[Any]											=      accelerator.prepare(__UpperCAmelCase)






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Dict											=      Accelerator()

      with init_empty_weights():
        a						:		Any											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Union[str, Any]											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      "cpu"

      a						:		int											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			device_map=__UpperCAmelCase    ,			load_in_abit=__UpperCAmelCase    ,			llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase)

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






    @slow
    @require_bnb
    @require_multi_gpu
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Union[str, Any]											=      {"distributed_type": DistributedType.MULTI_GPU}

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Any											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		Dict											=      1

      a						:		Any											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		int											=      Accelerator()

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

      PartialState._reset_state()






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

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        a						:		Tuple											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      1

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		str											=      Accelerator()

      # This should work
      a						:		Any											=      accelerator.prepare(__UpperCAmelCase)






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






