code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  UpperCAmelCase__      =							logging.getLogger(__name__)
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : List[Any]  )			->		List[str]:
  						__A=  False
  			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : str  )			->		Dict:
  						if not self.initialized:
  									__A=  RagRetriever(
  									    lowerCAmelCase_ ,						question_encoder_tokenizer=lowerCAmelCase_ ,						generator_tokenizer=lowerCAmelCase_ ,						index=lowerCAmelCase_ ,						init_retrieval=lowerCAmelCase_ ,						)
  									__A=  True
  			def 				lowerCAmelCase    (     self       : Any  )			->		str:
  						self.retriever.index.init_index()
  			def 				lowerCAmelCase    (     self       : str  )			->		Dict:
  						# delete the old self.retriever object before assigning the new index
  						del self.retriever
  						__A=  False
  
  			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Optional[Any]  )			->		Tuple:
  						__A,  __A=  self.retriever._main_retrieve(lowerCAmelCase_ ,						lowerCAmelCase_  )
  						__A=  self.retriever.index.get_doc_dicts(lowerCAmelCase_  )
  						return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Union[str, Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : str=None  )			->		Tuple:
  						if index is not None and index.is_initialized() and len(lowerCAmelCase_  ) > 0:
  									raise ValueError(
  									    'When using Ray for distributed fine-tuning, '
  									    'you\'ll need to provide the paths instead, '
  									    'as the dataset and the index are loaded '
  									    'separately. More info in examples/rag/use_own_knowledge_dataset.py '  )
  
  						super().__init__(
  						    lowerCAmelCase_ ,						question_encoder_tokenizer=lowerCAmelCase_ ,						generator_tokenizer=lowerCAmelCase_ ,						index=lowerCAmelCase_ ,						init_retrieval=lowerCAmelCase_ ,						)
  
  						__A=  retrieval_workers
  						__A=  question_encoder_tokenizer
  						__A=  generator_tokenizer
  						if len(self.retrieval_workers  ) > 0:
  									ray.get(
  									    [
  									        worker.create_rag_retriever.remote(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  									        for worker in self.retrieval_workers
  									    ]  )
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Union[str, Any]:
  						logger.info('initializing retrieval'  )
  
  						if len(self.retrieval_workers  ) > 0:
  									ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]  )
  						else:
  									# Non-distributed training. Load index into this same process.
  									self.index.init_index()
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		str:
  						if len(self.retrieval_workers  ) > 0:
  									# Select a random retrieval actor.
  									__A=  self.retrieval_workers[random.randint(0 ,						len(self.retrieval_workers  ) - 1  )]
  									__A,  __A,  __A=  ray.get(
  									    random_worker.retrieve.remote(lowerCAmelCase_ ,						lowerCAmelCase_  )  )
  						else:
  									__A,  __A=  self._main_retrieve(lowerCAmelCase_ ,						lowerCAmelCase_  )
  									__A=  self.index.get_doc_dicts(lowerCAmelCase_  )
  						return retrieved_doc_embeds, doc_ids, doc_dicts
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Dict ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : List[Any]=None ,						**lowerCAmelCase_       : Dict  )			->		Dict:
  						return super(lowerCAmelCase_ ,						cls  ).get_tokenizers(lowerCAmelCase_ ,						lowerCAmelCase_ ,						**lowerCAmelCase_  )
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Tuple=None ,						**lowerCAmelCase_       : Optional[Any]  )			->		str:
  						__A=  kwargs.pop('config' ,						lowerCAmelCase_  ) or RagConfig.from_pretrained(lowerCAmelCase_ ,						**lowerCAmelCase_  )
  						__A=  RagTokenizer.from_pretrained(lowerCAmelCase_ ,						config=lowerCAmelCase_  )
  						__A=  rag_tokenizer.question_encoder
  						__A=  rag_tokenizer.generator
  
  						if indexed_dataset is not None:
  									__A=  'custom'
  									__A=  CustomHFIndex(config.retrieval_vector_size ,						lowerCAmelCase_  )
  						else:
  									__A=  cls._build_index(lowerCAmelCase_  )
  
  						return cls(
  						    lowerCAmelCase_ ,						question_encoder_tokenizer=lowerCAmelCase_ ,						generator_tokenizer=lowerCAmelCase_ ,						retrieval_workers=lowerCAmelCase_ ,						index=lowerCAmelCase_ ,						)
  
  			def 				lowerCAmelCase    (     self       : Any  )			->		Tuple:
  						logger.info('re-loading the new dataset with embeddings'  )
  						# access from the training loop
  
  						ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]  )
  
  						# build the index object again
  						__A=  self._build_index(self.config  )
  
  						ray.get(
  						    [
  						        worker.create_rag_retriever.remote(
  						            self.config ,						self.question_encoder_tokenizer ,						self.generator_tokenizer ,						lowerCAmelCase_  )
  						        for worker in self.retrieval_workers
  						    ]  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  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__      =							1_6
  UpperCAmelCase__      =							3_2
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			return int(x / 2**20   )
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __enter__(     self       : str  )			->		Optional[Any]:
  						gc.collect()
  						torch.cuda.empty_cache()
  						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  						__A=  torch.cuda.memory_allocated()
  						__A=  psutil.Process()
  
  						__A=  self.cpu_mem_used()
  						__A=  True
  						__A=  threading.Thread(target=self.peak_monitor_func  )
  						__A=  True
  						peak_monitor_thread.start()
  						return self
  			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
  						return self.process.memory_info().rss
  			def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
  						__A=  -1
  
  						while True:
  									__A=  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       : List[Any] ,						*lowerCAmelCase_       : Dict  )			->		List[Any]:
  						__A=  False
  
  						gc.collect()
  						torch.cuda.empty_cache()
  						__A=  torch.cuda.memory_allocated()
  						__A=  torch.cuda.max_memory_allocated()
  						__A=  bamb(self.end - self.begin  )
  						__A=  bamb(self.peak - self.begin  )
  
  						__A=  self.cpu_mem_used()
  						__A=  bamb(self.cpu_end - self.cpu_begin  )
  						__A=  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__      =							mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if os.environ.get('TESTING_MOCKED_DATALOADERS',_SCREAMING_SNAKE_CASE   ) == "1":
  						__A=  2
  
  			# New Code #
  			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  			__A=  FullyShardedDataParallelPlugin(
  			    state_dict_config=FullStateDictConfig(offload_to_cpu=_SCREAMING_SNAKE_CASE,ranka_only=_SCREAMING_SNAKE_CASE   ),optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_SCREAMING_SNAKE_CASE,ranka_only=_SCREAMING_SNAKE_CASE   ),)
  
  			# Initialize accelerator
  			if args.with_tracking:
  						__A=  Accelerator(
  						    cpu=args.cpu,mixed_precision=args.mixed_precision,log_with='wandb',project_dir=args.logging_dir,fsdp_plugin=_SCREAMING_SNAKE_CASE,)
  			else:
  						__A=  Accelerator(fsdp_plugin=_SCREAMING_SNAKE_CASE   )
  			accelerator.print(accelerator.distributed_type   )
  
  			if hasattr(args.checkpointing_steps,'isdigit'   ):
  						if args.checkpointing_steps == "epoch":
  									__A=  args.checkpointing_steps
  						elif args.checkpointing_steps.isdigit():
  									__A=  int(args.checkpointing_steps   )
  						else:
  									raise ValueError(
  									    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
  			else:
  						__A=  None
  			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  			__A=  config['lr']
  			__A=  int(config['num_epochs']   )
  			__A=  int(config['seed']   )
  			__A=  int(config['batch_size']   )
  
  			# We need to initialize the trackers we use, and also store our configuration
  			if args.with_tracking:
  						__A=  vars(_SCREAMING_SNAKE_CASE   )
  						accelerator.init_trackers('fsdp_glue_no_trainer',_SCREAMING_SNAKE_CASE   )
  
  			__A=  AutoTokenizer.from_pretrained(args.model_name_or_path   )
  			__A=  load_dataset('glue','mrpc'   )
  			__A=  evaluate.load('glue','mrpc'   )
  
  			def tokenize_function(_SCREAMING_SNAKE_CASE				:   str   ):
  						# max_length=None => use the model max length (it's actually the default)
  						__A=  tokenizer(examples['sentence1'],examples['sentence2'],truncation=_SCREAMING_SNAKE_CASE,max_length=_SCREAMING_SNAKE_CASE   )
  						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=  datasets.map(
  						    _SCREAMING_SNAKE_CASE,batched=_SCREAMING_SNAKE_CASE,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=  tokenized_datasets.rename_column('label','labels'   )
  
  			# If the batch size is too big we use gradient accumulation
  			__A=  1
  			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  						__A=  batch_size // MAX_GPU_BATCH_SIZE
  						__A=  MAX_GPU_BATCH_SIZE
  
  			def collate_fn(_SCREAMING_SNAKE_CASE				:   int   ):
  						# On TPU it's best to pad everything to the same length or training will be very slow.
  						__A=  128 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=  16
  						elif accelerator.mixed_precision != "no":
  									__A=  8
  						else:
  									__A=  None
  
  						return tokenizer.pad(
  						    _SCREAMING_SNAKE_CASE,padding='longest',max_length=_SCREAMING_SNAKE_CASE,pad_to_multiple_of=_SCREAMING_SNAKE_CASE,return_tensors='pt',)
  
  			# Instantiate dataloaders.
  			__A=  DataLoader(
  			    tokenized_datasets['train'],shuffle=_SCREAMING_SNAKE_CASE,collate_fn=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
  			__A=  DataLoader(
  			    tokenized_datasets['validation'],shuffle=_SCREAMING_SNAKE_CASE,collate_fn=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
  
  			set_seed(_SCREAMING_SNAKE_CASE   )
  
  			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  			__A=  AutoModelForSequenceClassification.from_pretrained(
  			    args.model_name_or_path,return_dict=_SCREAMING_SNAKE_CASE,low_cpu_mem_usage=_SCREAMING_SNAKE_CASE   )
  
  			# New Code #
  			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  			__A=  accelerator.prepare(_SCREAMING_SNAKE_CASE   )
  			accelerator.print(_SCREAMING_SNAKE_CASE   )
  
  			# 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=  torch.optim.AdamW(params=model.parameters(),lr=_SCREAMING_SNAKE_CASE,weight_decay=2e-4   )
  
  			# Instantiate scheduler
  			__A=  get_linear_schedule_with_warmup(
  			    optimizer=_SCREAMING_SNAKE_CASE,num_warmup_steps=10,num_training_steps=(len(_SCREAMING_SNAKE_CASE   ) * 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=  accelerator.prepare(
  			    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  			__A=  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=  os.path.basename(args.resume_from_checkpoint   )
  						else:
  									# Get the most recent checkpoint
  									__A=  [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
  									dirs.sort(key=os.path.getctime   )
  									__A=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  						# Extract `epoch_{i}` or `step_{i}`
  						__A=  os.path.splitext(_SCREAMING_SNAKE_CASE   )[0]
  
  						if "epoch" in training_difference:
  									num_epochs -= int(training_difference.replace('epoch_',''   )   )
  									__A=  None
  						else:
  									__A=  int(training_difference.replace('step_',''   )   )
  									num_epochs -= resume_step // len(_SCREAMING_SNAKE_CASE   )
  									# If resuming by step, we also need to know exactly how far into the DataLoader we went
  									__A=  (num_epochs * len(_SCREAMING_SNAKE_CASE   )) - resume_step
  
      # Now we train the model
  			for epoch in range(_SCREAMING_SNAKE_CASE   ):
  						# 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=  0
  									for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
  												# 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=  model(**_SCREAMING_SNAKE_CASE   )
  												__A=  outputs.loss
  												__A=  loss / gradient_accumulation_steps
  												# We keep track of the loss at each epoch
  												if args.with_tracking:
  															total_loss += loss.detach().float()
  												accelerator.backward(_SCREAMING_SNAKE_CASE   )
  												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(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ):
  															__A=  f"""step_{overall_step}"""
  															if overall_step % checkpointing_steps == 0:
  																		if args.output_dir is not None:
  																					__A=  os.path.join(args.output_dir,_SCREAMING_SNAKE_CASE   )
  																		accelerator.save_state(_SCREAMING_SNAKE_CASE   )
          # 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=_SCREAMING_SNAKE_CASE,)
  
  						# New Code #
  						# context manager to track the peak memory usage during the evaluation
  						with TorchTracemalloc() as tracemalloc:
  									model.eval()
  									for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
  												# We could avoid this line since we set the accelerator with `device_placement=True`.
  												batch.to(accelerator.device   )
  												with torch.no_grad():
  															__A=  model(**_SCREAMING_SNAKE_CASE   )
  												__A=  outputs.logits.argmax(dim=-1   )
  												__A,  __A=  accelerator.gather_for_metrics((predictions, batch['labels'])   )
  												metric.add_batch(
  												    predictions=_SCREAMING_SNAKE_CASE,references=_SCREAMING_SNAKE_CASE,)
  
  									__A=  metric.compute()
  									# Use accelerator.print to print only on the main process.
  									accelerator.print(f"""epoch {epoch}:""",_SCREAMING_SNAKE_CASE   )
  									if args.with_tracking:
  												accelerator.log(
  												    {
  												        'accuracy': eval_metric['accuracy'],
  												        'f1': eval_metric['f1'],
  												        'train_loss': total_loss.item() / len(_SCREAMING_SNAKE_CASE   ),
  												    },step=_SCREAMING_SNAKE_CASE,)
  
  									if checkpointing_steps == "epoch":
  												__A=  f"""epoch_{epoch}"""
  												if args.output_dir is not None:
  															__A=  os.path.join(args.output_dir,_SCREAMING_SNAKE_CASE   )
  												accelerator.save_state(_SCREAMING_SNAKE_CASE   )
          # 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=_SCREAMING_SNAKE_CASE,)
  
  			if args.with_tracking:
  						accelerator.end_training()
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  argparse.ArgumentParser(description='Simple example of training script.'   )
  			parser.add_argument(
  			    '--mixed_precision',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,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=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,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=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,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=_SCREAMING_SNAKE_CASE,default='.',help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',)
  			parser.add_argument(
  			    '--logging_dir',type=_SCREAMING_SNAKE_CASE,default='logs',help='Location on where to store experiment tracking logs`',)
  			parser.add_argument(
  			    '--model_name_or_path',type=_SCREAMING_SNAKE_CASE,help='Path to pretrained model or model identifier from huggingface.co/models.',required=_SCREAMING_SNAKE_CASE,)
  			__A=  parser.parse_args()
  			__A=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  			training_function(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  