

"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE_     :			List[Any]	  =			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')


@dataclass
class 		_A       :
      __a								=	field(
          default='cifar10'				,       metadata={'help': 'Name of a dataset from the datasets package'}					)
      __a								=	field(
          default=_lowercase				,       metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}					)
      __a								=	field(
          default=_lowercase				,       metadata={'help': 'The column name of the images in the files.'}					)
      __a								=	field(default=_lowercase				,       metadata={'help': 'A folder containing the training data.'}					)
      __a								=	field(default=_lowercase				,       metadata={'help': 'A folder containing the validation data.'}					)
      __a								=	field(
          default=0.15				,       metadata={'help': 'Percent to split off of train for validation.'}					)
      __a								=	field(
          default=_lowercase				,       metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of training examples to this '
                  'value if set.'
              )
          }				,       )
      __a								=	field(
          default=_lowercase				,       metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                  'value if set.'
              )
          }				,       )





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			{}
         if self.train_dir is not None:
            lowerCamelCase__						=			self.train_dir
         if self.validation_dir is not None:
            lowerCamelCase__						=			self.validation_dir
         lowerCamelCase__						=			data_files if data_files else None






@dataclass
class 		_A       :
      __a								=	field(
          default=_lowercase				,       metadata={
              'help': (
                  'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
              )
          }				,       )
      __a								=	field(
          default=_lowercase				,       metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}					)
      __a								=	field(
          default=_lowercase				,       metadata={
              'help': (
                  'Override some existing default config settings when a model is trained from scratch. Example: '
                  'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
              )
          }				,       )
      __a								=	field(
          default=_lowercase				,       metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}					)
      __a								=	field(
          default='main'				,       metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}				,       )
      __a								=	field(default=_lowercase				,       metadata={'help': 'Name or path of preprocessor config.'}					)
      __a								=	field(
          default=_lowercase				,       metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }				,       )
      __a								=	field(
          default=0.75				,       metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}					)
      __a								=	field(
          default=_lowercase				,       metadata={'help': 'Whether or not to train with normalized pixel values as target.'}					)






@dataclass
class 		_A       (			_lowercase					):
      __a								=	field(
          default=1E-3				,       metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}					)






def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			torch.stack([example["pixel_values"] for example in examples]   )
   return {"pixel_values": pixel_values}



def 	UpperCAmelCase__   (				)      ->      List[Any]:




   """simple docstring"""
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowerCamelCase__						=			HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
   if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry("run_mae"	,    snake_case__	,    snake_case__   )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,    datefmt="%m/%d/%Y %H:%M:%S"	,    handlers=[logging.StreamHandler(sys.stdout   )]	,    )

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   lowerCamelCase__						=			training_args.get_process_log_level()
   logger.setLevel(snake_case__   )
   transformers.utils.logging.set_verbosity(snake_case__   )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
       + f'distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'   )
   logger.info(f'Training/evaluation parameters {training_args}'   )

   # Detecting last checkpoint.
   lowerCamelCase__						=			None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      lowerCamelCase__						=			get_last_checkpoint(training_args.output_dir   )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
         raise ValueError(
             f'Output directory ({training_args.output_dir}) already exists and is not empty. '
             "Use --overwrite_output_dir to overcome."   )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
             "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."   )

    # Initialize our dataset.
   lowerCamelCase__						=			load_dataset(
       data_args.dataset_name	,    data_args.dataset_config_name	,    data_files=data_args.data_files	,    cache_dir=model_args.cache_dir	,    use_auth_token=True if model_args.use_auth_token else None	,    )

   # If we don't have a validation split, split off a percentage of train as validation.
   lowerCamelCase__						=			None if "validation" in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split	,    snake_case__   ) and data_args.train_val_split > 0.0:
      lowerCamelCase__						=			ds["train"].train_test_split(data_args.train_val_split   )
      lowerCamelCase__						=			split["train"]
      lowerCamelCase__						=			split["test"]

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   lowerCamelCase__						=			{
       "cache_dir": model_args.cache_dir,
       "revision": model_args.model_revision,
       "use_auth_token": True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      lowerCamelCase__						=			ViTMAEConfig.from_pretrained(model_args.config_name	,    **snake_case__   )
   elif model_args.model_name_or_path:
      lowerCamelCase__						=			ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,    **snake_case__   )
   else:
      lowerCamelCase__						=			ViTMAEConfig()
      logger.warning("You are instantiating a new config instance from scratch."   )
      if model_args.config_overrides is not None:
         logger.info(f'Overriding config: {model_args.config_overrides}'   )
         config.update_from_string(model_args.config_overrides   )
         logger.info(f'New config: {config}'   )

    # adapt config
   config.update(
       {
           "mask_ratio": model_args.mask_ratio,
           "norm_pix_loss": model_args.norm_pix_loss,
       }   )

   # create image processor
   if model_args.image_processor_name:
      lowerCamelCase__						=			ViTImageProcessor.from_pretrained(model_args.image_processor_name	,    **snake_case__   )
   elif model_args.model_name_or_path:
      lowerCamelCase__						=			ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,    **snake_case__   )
   else:
      lowerCamelCase__						=			ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      lowerCamelCase__						=			ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path	,    from_tf=bool(".ckpt" in model_args.model_name_or_path   )	,    config=snake_case__	,    cache_dir=model_args.cache_dir	,    revision=model_args.model_revision	,    use_auth_token=True if model_args.use_auth_token else None	,    )
   else:
      logger.info("Training new model from scratch"   )
      lowerCamelCase__						=			ViTMAEForPreTraining(snake_case__   )

   if training_args.do_train:
      lowerCamelCase__						=			ds["train"].column_names
   else:
      lowerCamelCase__						=			ds["validation"].column_names

   if data_args.image_column_name is not None:
      lowerCamelCase__						=			data_args.image_column_name
   elif "image" in column_names:
      lowerCamelCase__						=			"image"
   elif "img" in column_names:
      lowerCamelCase__						=			"img"
   else:
      lowerCamelCase__						=			column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      lowerCamelCase__						=			image_processor.size["shortest_edge"]
   else:
      lowerCamelCase__						=			(image_processor.size["height"], image_processor.size["width"])
   lowerCamelCase__						=			Compose(
       [
           Lambda(lambda A__   : img.convert("RGB"   ) if img.mode != "RGB" else img   ),
           RandomResizedCrop(snake_case__	,    scale=(0.2, 1.0)	,    interpolation=InterpolationMode.BICUBIC   ),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean	,    std=image_processor.image_std   ),
       ]   )

   def preprocess_images(A__   ):
      lowerCamelCase__						=			[transforms(snake_case__   ) for image in examples[image_column_name]]
      return examples

   if training_args.do_train:
      if "train" not in ds:
         raise ValueError("--do_train requires a train dataset"   )
      if data_args.max_train_samples is not None:
         lowerCamelCase__						=			ds["train"].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
      # Set the training transforms
      ds["train"].set_transform(snake_case__   )

   if training_args.do_eval:
      if "validation" not in ds:
         raise ValueError("--do_eval requires a validation dataset"   )
      if data_args.max_eval_samples is not None:
         lowerCamelCase__						=			(
             ds["validation"].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
         )
      # Set the validation transforms
      ds["validation"].set_transform(snake_case__   )

   # Compute absolute learning rate
   lowerCamelCase__						=			(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      lowerCamelCase__						=			training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   lowerCamelCase__						=			Trainer(
       model=snake_case__	,    args=snake_case__	,    train_dataset=ds["train"] if training_args.do_train else None	,    eval_dataset=ds["validation"] if training_args.do_eval else None	,    tokenizer=snake_case__	,    data_collator=snake_case__	,    )

   # Training
   if training_args.do_train:
      lowerCamelCase__						=			None
      if training_args.resume_from_checkpoint is not None:
         lowerCamelCase__						=			training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         lowerCamelCase__						=			last_checkpoint
      lowerCamelCase__						=			trainer.train(resume_from_checkpoint=snake_case__   )
      trainer.save_model()
      trainer.log_metrics("train"	,    train_result.metrics   )
      trainer.save_metrics("train"	,    train_result.metrics   )
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      lowerCamelCase__						=			trainer.evaluate()
      trainer.log_metrics("eval"	,    snake_case__   )
      trainer.save_metrics("eval"	,    snake_case__   )

   # Write model card and (optionally) push to hub
   lowerCamelCase__						=			{
       "tasks": "masked-auto-encoding",
       "dataset": data_args.dataset_name,
       "tags": ["masked-auto-encoding"],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**snake_case__   )
   else:
      trainer.create_model_card(**snake_case__   )



def 	UpperCAmelCase__   (				A__   )      ->      List[str]:




   """simple docstring"""
   # For xla_spawn (TPUs)
   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 	UpperCAmelCase__   (				)      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(2	,    4   )
   lowerCamelCase__						=			torch.optim.AdamW(model.parameters()	,    lr=1.0   )
   lowerCamelCase__						=			torch.optim.lr_scheduler.OneCycleLR(A__	,    max_lr=0.01	,    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 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
   model.load_state_dict(A__   )


class 		_A       (			__a					):





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         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 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()

         (
             (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       
         )						=			accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         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 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         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 					_lowerCamelCase  (  self		)   ->					Dict:
         PartialState._reset_state()

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

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





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

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

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

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





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

         # saving hook
         def save_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			{"class_name": models[0].__class__.__name__}

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

        # loading hook
         def load_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"data.json"		)    ,						"r"		) as f:
               lowerCamelCase__						=			json.load(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			config["class_name"]

         lowerCamelCase__						=			accelerator.register_save_state_pre_hook(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.register_load_state_pre_hook(SCREAMING_SNAKE_CASE__		)

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

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

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

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

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

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

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

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





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			None

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





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			[1, 2, 3]

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





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					str:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map={"": 0}    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         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(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			"cpu"

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						device_map=SCREAMING_SNAKE_CASE__    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						llm_inta_enable_fpaa_cpu_offload=SCREAMING_SNAKE_CASE__		)

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





      @slow
      @require_bnb
      @require_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         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(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

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

         PartialState._reset_state()





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

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			torch.nn.Linear(10    ,						10		)
         lowerCamelCase__						=			torch.optim.SGD(model.parameters()    ,						lr=0.01		)
         lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)




