





import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase						:       Dict        =					16
UpperCAmelCase						:       int        =					32







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Any:
     return int(x / 2**20  )





class 						_A:


    """simple docstring"""


    def __enter__(      self					):
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         __A     :						Optional[int]          =    torch.cuda.memory_allocated()
         __A     :						int          =    psutil.Process()

         __A     :						Tuple          =    self.cpu_mem_used()
         __A     :						Any          =    True
         __A     :						Union[str, Any]          =    threading.Thread(target=self.peak_monitor_func					)
         __A     :						Any          =    True
         peak_monitor_thread.start()
         return self



    def  UpperCAmelCase_		(      self					):
         return self.process.memory_info().rss



    def  UpperCAmelCase_		(      self					):
         __A     :						Any          =    -1

         while True:
              __A     :						Optional[Any]          =    max(self.cpu_mem_used()					, self.cpu_peak					)

              # can't sleep or will not catch the peak right (this comment is here on purpose)
              # time.sleep(0.001) # 1msec

              if not self.peak_monitoring:
                   break





    def __exit__(      self					, *_A					):
         __A     :						Dict          =    False

         gc.collect()
         torch.cuda.empty_cache()
         __A     :						Union[str, Any]          =    torch.cuda.memory_allocated()
         __A     :						Any          =    torch.cuda.max_memory_allocated()
         __A     :						str          =    bamb(self.end - self.begin					)
         __A     :						Tuple          =    bamb(self.peak - self.begin					)

         __A     :						List[Any]          =    self.cpu_mem_used()
         __A     :						int          =    bamb(self.cpu_end - self.cpu_begin					)
         __A     :						Any          =    bamb(self.cpu_peak - self.cpu_begin					)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    UpperCAmelCase						:       Union[str, Any]        =					mocked_dataloaders  # noqa: F811







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Any:

     if os.environ.get('TESTING_MOCKED_DATALOADERS' ,						a  ) == "1":
          __A     :						Dict          =    2

     # New Code #
     # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
     __A     :						Union[str, Any]          =    FullyShardedDataParallelPlugin(
         state_dict_config=FullStateDictConfig(offload_to_cpu=a ,						ranka_only=a  ) ,						optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=a ,						ranka_only=a  ) ,						)

     # Initialize accelerator
     if args.with_tracking:
          __A     :						Optional[Any]          =    Accelerator(
              cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with='wandb' ,						project_dir=args.logging_dir ,						fsdp_plugin=a ,						)
     else:
          __A     :						List[Any]          =    Accelerator(fsdp_plugin=a  )
     accelerator.print(accelerator.distributed_type  )

     if hasattr(args.checkpointing_steps ,						'isdigit'  ):
          if args.checkpointing_steps == "epoch":
               __A     :						List[str]          =    args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __A     :						Optional[Any]          =    int(args.checkpointing_steps  )
          else:
               raise ValueError(
                   F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""  )
     else:
          __A     :						Union[str, Any]          =    None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A     :						Dict          =    config['lr']
     __A     :						Optional[Any]          =    int(config['num_epochs']  )
     __A     :						str          =    int(config['seed']  )
     __A     :						Dict          =    int(config['batch_size']  )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          __A     :						int          =    vars(a  )
          accelerator.init_trackers('fsdp_glue_no_trainer' ,						a  )

     __A     :						Tuple          =    AutoTokenizer.from_pretrained(args.model_name_or_path  )
     __A     :						Any          =    load_dataset('glue' ,						'mrpc'  )
     __A     :						Union[str, Any]          =    evaluate.load('glue' ,						'mrpc'  )

     def tokenize_function(a  ):
          # max_length=None => use the model max length (it's actually the default)
          __A     :						Union[str, Any]          =    tokenizer(examples['sentence1'] ,						examples['sentence2'] ,						truncation=a ,						max_length=a  )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          __A     :						Any          =    datasets.map(
              a ,						batched=a ,						remove_columns=['idx', 'sentence1', 'sentence2'] ,						)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __A     :						List[str]          =    tokenized_datasets.rename_column('label' ,						'labels'  )

     # If the batch size is too big we use gradient accumulation
     __A     :						Any          =    1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __A     :						Tuple          =    batch_size // MAX_GPU_BATCH_SIZE
          __A     :						Dict          =    MAX_GPU_BATCH_SIZE

     def collate_fn(a  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __A     :						Tuple          =    1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __A     :						List[Any]          =    16
          elif accelerator.mixed_precision != "no":
               __A     :						Tuple          =    8
          else:
               __A     :						Any          =    None

          return tokenizer.pad(
              a ,						padding='longest' ,						max_length=a ,						pad_to_multiple_of=a ,						return_tensors='pt' ,						)

     # Instantiate dataloaders.
     __A     :						List[Any]          =    DataLoader(
         tokenized_datasets['train'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )
     __A     :						Dict          =    DataLoader(
         tokenized_datasets['validation'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )

     set_seed(a  )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A     :						List[str]          =    AutoModelForSequenceClassification.from_pretrained(
         args.model_name_or_path ,						return_dict=a ,						low_cpu_mem_usage=a  )

     # New Code #
     # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
     __A     :						List[str]          =    accelerator.prepare(a  )
     accelerator.print(a  )

     # Instantiate optimizer
     # New Code #
     # For FSDP feature, at present it doesn't support multiple parameter groups,
     # so we need to create a single parameter group for the whole model
     __A     :						Tuple          =    torch.optim.AdamW(params=model.parameters() ,						lr=a ,						weight_decay=2e-4  )

     # Instantiate scheduler
     __A     :						Any          =    get_linear_schedule_with_warmup(
         optimizer=a ,						num_warmup_steps=10 ,						num_training_steps=(len(a  ) * num_epochs) // gradient_accumulation_steps ,						)

     # New Code #
     # For FSDP feature, prepare everything except the model as we have already prepared the model
     # before creating the optimizer
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     __A   ,    __A   ,    __A   ,    __A     :						Optional[int]          =    accelerator.prepare(
         a ,						a ,						a ,						a  )

     __A     :						Any          =    0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""  )
               accelerator.load_state(args.resume_from_checkpoint  )
               __A     :						str          =    os.path.basename(args.resume_from_checkpoint  )
          else:
               # Get the most recent checkpoint
               __A     :						int          =    [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
               dirs.sort(key=os.path.getctime  )
               __A     :						str          =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __A     :						Union[str, Any]          =    os.path.splitext(a  )[0]

          if "epoch" in training_difference:
               num_epochs -= int(training_difference.replace('epoch_' ,						''  )  )
               __A     :						int          =    None
          else:
               __A     :						Any          =    int(training_difference.replace('step_' ,						''  )  )
               num_epochs -= resume_step // len(a  )
               # If resuming by step, we also need to know exactly how far into the DataLoader we went
               __A     :						List[str]          =    (num_epochs * len(a  )) - resume_step

    # Now we train the model
     for epoch in range(a  ):
          # New Code #
          # context manager to track the peak memory usage during the training epoch
          with TorchTracemalloc() as tracemalloc:
               model.train()
               if args.with_tracking:
                    __A     :						Dict          =    0
               for step, batch in enumerate(a  ):
                    # We need to skip steps until we reach the resumed step
                    if args.resume_from_checkpoint and epoch == 0:
                         if resume_step is not None and step < resume_step:
                              pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device  )
                    __A     :						Dict          =    model(**a  )
                    __A     :						Union[str, Any]          =    outputs.loss
                    __A     :						List[str]          =    loss / gradient_accumulation_steps
                    # We keep track of the loss at each epoch
                    if args.with_tracking:
                         total_loss += loss.detach().float()
                    accelerator.backward(a  )
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # accelerator.print(lr_scheduler.get_lr())

                    overall_step += 1

                    if isinstance(a ,						a  ):
                         __A     :						Any          =    F"""step_{overall_step}"""
                         if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                   __A     :						Dict          =    os.path.join(args.output_dir ,						a  )
                              accelerator.save_state(a  )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin  )  )  )
          accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used  )  )
          accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked  )  )
          accelerator.print(
              'Total Peak Memory consumed during the train (max): {}'.format(
                  tracemalloc.peaked + bamb(tracemalloc.begin  )  )  )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin  ),
                   } ,						step=a ,						)

          # New Code #
          # context manager to track the peak memory usage during the evaluation
          with TorchTracemalloc() as tracemalloc:
               model.eval()
               for step, batch in enumerate(a  ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device  )
                    with torch.no_grad():
                         __A     :						List[Any]          =    model(**a  )
                    __A     :						Tuple          =    outputs.logits.argmax(dim=-1  )
                    __A   ,    __A     :						Any          =    accelerator.gather_for_metrics((predictions, batch['labels'])  )
                    metric.add_batch(
                        predictions=a ,						references=a ,						)

               __A     :						Dict          =    metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F"""epoch {epoch}:""" ,						a  )
               if args.with_tracking:
                    accelerator.log(
                        {
                            'accuracy': eval_metric['accuracy'],
                            'f1': eval_metric['f1'],
                            'train_loss': total_loss.item() / len(a  ),
                        } ,						step=a ,						)

               if checkpointing_steps == "epoch":
                    __A     :						str          =    F"""epoch_{epoch}"""
                    if args.output_dir is not None:
                         __A     :						List[Any]          =    os.path.join(args.output_dir ,						a  )
                    accelerator.save_state(a  )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin  )  )  )
          accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used  )  )
          accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked  )  )
          accelerator.print(
              'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin  )  )  )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin  ),
                   } ,						step=a ,						)

     if args.with_tracking:
          accelerator.end_training()







def 			_SCREAMING_SNAKE_CASE    (       )				->				str:
     __A     :						Any          =    argparse.ArgumentParser(description='Simple example of training script.'  )
     parser.add_argument(
         '--mixed_precision' ,						type=a ,						default=a ,						choices=['no', 'fp16', 'bf16', 'fp8'] ,						help='Whether to use mixed precision. Choose'
         'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
         'and an Nvidia Ampere GPU.' ,						)
     parser.add_argument('--cpu' ,						action='store_true' ,						help='If passed, will train on the CPU.'  )
     parser.add_argument(
         '--checkpointing_steps' ,						type=a ,						default=a ,						help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' ,						)
     parser.add_argument(
         '--resume_from_checkpoint' ,						type=a ,						default=a ,						help='If the training should continue from a checkpoint folder.' ,						)
     parser.add_argument(
         '--with_tracking' ,						action='store_true' ,						help='Whether to load in all available experiment trackers from the environment and use them for logging.' ,						)
     parser.add_argument(
         '--output_dir' ,						type=a ,						default='.' ,						help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,						)
     parser.add_argument(
         '--logging_dir' ,						type=a ,						default='logs' ,						help='Location on where to store experiment tracking logs`' ,						)
     parser.add_argument(
         '--model_name_or_path' ,						type=a ,						help='Path to pretrained model or model identifier from huggingface.co/models.' ,						required=a ,						)
     __A     :						Optional[int]          =    parser.parse_args()
     __A     :						str          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(a ,						a  )


if __name__ == "__main__":
    main()











import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase						:       List[Any]        =					32







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = 16  )				->				List[Any]:
					__A     :						List[str]          =    AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A     :						int          =    load_dataset('glue' ,						'mrpc'  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A     :						str          =    tokenizer(examples['sentence1'] ,						examples['sentence2'] ,						truncation=a ,						max_length=a  )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										__A     :						Optional[int]          =    datasets.map(
										    a ,						batched=a ,						remove_columns=['idx', 'sentence1', 'sentence2'] ,						)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__A     :						Tuple          =    tokenized_datasets.rename_column('label' ,						'labels'  )

					def collate_fn(a  ):
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__A     :						Union[str, Any]          =    16
										elif accelerator.mixed_precision != "no":
															__A     :						Any          =    8
										else:
															__A     :						Union[str, Any]          =    None

										return tokenizer.pad(
										    a ,						padding='longest' ,						pad_to_multiple_of=a ,						return_tensors='pt' ,						)

					# Instantiate dataloaders.
					__A     :						Any          =    DataLoader(
					    tokenized_datasets['train'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )
					__A     :						List[Any]          =    DataLoader(
					    tokenized_datasets['validation'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				UpperCAmelCase						:       Tuple        =					mocked_dataloaders  # noqa: F811







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Optional[Any]:
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS' ,						a  ) == "1":
										__A     :						Dict          =    2
					# Initialize accelerator
					__A     :						int          =    Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A     :						Dict          =    config['lr']
					__A     :						Optional[Any]          =    int(config['num_epochs']  )
					__A     :						int          =    int(config['seed']  )
					__A     :						int          =    int(config['batch_size']  )

					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(a  )  )
					def inner_training_loop(a  ):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										__A     :						Optional[Any]          =    observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										__A     :						List[str]          =    num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(a  )

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										__A     :						List[Any]          =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,						return_dict=a  )

										# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
										# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
										# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
										__A     :						Dict          =    model.to(accelerator.device  )

										# Instantiate optimizer
										__A     :						Optional[Any]          =    AdamW(params=model.parameters() ,						lr=a  )
										__A   ,    __A     :						Dict          =    get_dataloaders(a ,						a  )

										# Instantiate scheduler
										__A     :						List[Any]          =    get_linear_schedule_with_warmup(
										    optimizer=a ,						num_warmup_steps=1_00 ,						num_training_steps=(len(a  ) * num_epochs) ,						)

										# Prepare everything
										# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
										# prepare method.
										__A   ,    __A   ,    __A   ,    __A   ,    __A     :						Tuple          =    accelerator.prepare(
										    a ,						a ,						a ,						a ,						a  )

										# Now we train the model
										for epoch in range(a  ):
															model.train()
															for step, batch in enumerate(a  ):
																				# And perform gradient accumulation
																				with accelerator.accumulate(a  ):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device  )
																									__A     :						List[str]          =    model(**a  )
																									__A     :						List[str]          =    outputs.loss
																									accelerator.backward(a  )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															for step, batch in enumerate(a  ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device  )
																				with torch.no_grad():
																									__A     :						List[Any]          =    model(**a  )
																				__A     :						List[Any]          =    outputs.logits.argmax(dim=-1  )
																				__A   ,    __A     :						Tuple          =    accelerator.gather_for_metrics((predictions, batch['labels'])  )
																				metric.add_batch(
																				    predictions=a ,						references=a ,						)

															__A     :						Optional[int]          =    metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:""" ,						a  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()







def 			_SCREAMING_SNAKE_CASE    (       )				->				Union[str, Any]:
					__A     :						List[Any]          =    argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision' ,						type=a ,						default=a ,						choices=['no', 'fp16', 'bf16', 'fp8'] ,						help='Whether to use mixed precision. Choose'
					    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
					    'and an Nvidia Ampere GPU.' ,						)
					parser.add_argument('--cpu' ,						action='store_true' ,						help='If passed, will train on the CPU.'  )
					__A     :						int          =    parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					__A     :						Optional[int]          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()




