code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import List, Optional
  
  import faiss
  import torch
  from datasets import Features, Sequence, Value, load_dataset
  
  from transformers import (
      DPRContextEncoder,
      DPRContextEncoderTokenizerFast,
      HfArgumentParser,
      RagRetriever,
      RagSequenceForGeneration,
      RagTokenizer,
  )
  
  
  UpperCAmelCase__      =							logging.getLogger(__name__)
  torch.set_grad_enabled(False)
  UpperCAmelCase__      =							'''cuda''' if torch.cuda.is_available() else '''cpu'''
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   List[str]=100,_SCREAMING_SNAKE_CASE				:   int=" "   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  text.split(_SCREAMING_SNAKE_CASE   )
  			return [character.join(text[i : i + n]   ).strip() for i in range(0,len(_SCREAMING_SNAKE_CASE   ),_SCREAMING_SNAKE_CASE   )]
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   dict   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A,  __A=  [], []
  			for title, text in zip(documents['title'],documents['text']   ):
  						if text is not None:
  									for passage in split_text(_SCREAMING_SNAKE_CASE   ):
  												titles.append(title if title is not None else ''   )
  												texts.append(_SCREAMING_SNAKE_CASE   )
  			return {"title": titles, "text": texts}
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   dict,_SCREAMING_SNAKE_CASE				:   DPRContextEncoder,_SCREAMING_SNAKE_CASE				:   DPRContextEncoderTokenizerFast   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  ctx_tokenizer(
  			    documents['title'],documents['text'],truncation=_SCREAMING_SNAKE_CASE,padding='longest',return_tensors='pt'   )['input_ids']
  			__A=  ctx_encoder(input_ids.to(device=_SCREAMING_SNAKE_CASE   ),return_dict=_SCREAMING_SNAKE_CASE   ).pooler_output
  			return {"embeddings": embeddings.detach().cpu().numpy()}
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   "RagExampleArguments",_SCREAMING_SNAKE_CASE				:   "ProcessingArguments",_SCREAMING_SNAKE_CASE				:   "IndexHnswArguments",):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			logger.info('Step 1 - Create the dataset'   )
  			######################################
  
  			# The dataset needed for RAG must have three columns:
  			# - title (string): title of the document
  			# - text (string): text of a passage of the document
  			# - embeddings (array of dimension d): DPR representation of the passage
  
  			# Let's say you have documents in tab-separated csv files with columns "title" and "text"
  			assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"
  
  			# You can load a Dataset object this way
  			__A=  load_dataset(
  			    'csv',data_files=[rag_example_args.csv_path],split='train',delimiter='\t',column_names=['title', 'text']   )
  
  			# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
  
  			# Then split the documents into passages of 100 words
  			__A=  dataset.map(_SCREAMING_SNAKE_CASE,batched=_SCREAMING_SNAKE_CASE,num_proc=processing_args.num_proc   )
  
  			# And compute the embeddings
  			__A=  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=_SCREAMING_SNAKE_CASE   )
  			__A=  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
  			__A=  Features(
  			    {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space
  			__A=  dataset.map(
  			    partial(_SCREAMING_SNAKE_CASE,ctx_encoder=_SCREAMING_SNAKE_CASE,ctx_tokenizer=_SCREAMING_SNAKE_CASE   ),batched=_SCREAMING_SNAKE_CASE,batch_size=processing_args.batch_size,features=_SCREAMING_SNAKE_CASE,)
  
  			# And finally save your dataset
  			__A=  os.path.join(rag_example_args.output_dir,'my_knowledge_dataset'   )
  			dataset.save_to_disk(_SCREAMING_SNAKE_CASE   )
  			# from datasets import load_from_disk
  			# dataset = load_from_disk(passages_path)  # to reload the dataset
  
  			######################################
  			logger.info('Step 2 - Index the dataset'   )
  			######################################
  
  			# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
  			__A=  faiss.IndexHNSWFlat(index_hnsw_args.d,index_hnsw_args.m,faiss.METRIC_INNER_PRODUCT   )
  			dataset.add_faiss_index('embeddings',custom_index=_SCREAMING_SNAKE_CASE   )
  
  			# And save the index
  			__A=  os.path.join(rag_example_args.output_dir,'my_knowledge_dataset_hnsw_index.faiss'   )
  			dataset.get_index('embeddings'   ).save(_SCREAMING_SNAKE_CASE   )
  			# dataset.load_faiss_index("embeddings", index_path)  # to reload the index
  
  			######################################
  			logger.info('Step 3 - Load RAG'   )
  			######################################
  
  			# Easy way to load the model
  			__A=  RagRetriever.from_pretrained(
  			    rag_example_args.rag_model_name,index_name='custom',indexed_dataset=_SCREAMING_SNAKE_CASE   )
  			__A=  RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name,retriever=_SCREAMING_SNAKE_CASE   )
  			__A=  RagTokenizer.from_pretrained(rag_example_args.rag_model_name   )
  
  			# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
  			# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)
  
  			######################################
  			logger.info('Step 4 - Have fun'   )
  			######################################
  
  			__A=  rag_example_args.question or 'What does Moses\' rod turn into ?'
  			__A=  tokenizer.question_encoder(_SCREAMING_SNAKE_CASE,return_tensors='pt'   )['input_ids']
  			__A=  model.generate(_SCREAMING_SNAKE_CASE   )
  			__A=  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE,skip_special_tokens=_SCREAMING_SNAKE_CASE   )[0]
  			logger.info('Q: ' + question   )
  			logger.info('A: ' + generated_string   )
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							str							= field(
  			    default=str(Path(a_      ).parent / '''test_data''' / '''my_knowledge_dataset.csv'''      )			,     metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}			,     )
  			A	:							Optional[str]							= field(
  			    default=a_			,     metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}			,     )
  			A	:							str							= field(
  			    default='''facebook/rag-sequence-nq'''			,     metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}			,     )
  			A	:							str							= field(
  			    default='''facebook/dpr-ctx_encoder-multiset-base'''			,     metadata={
  			        '''help''': (
  			            '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
  			            ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
  			        )
  			    }			,     )
  			A	:							Optional[str]							= field(
  			    default=a_			,     metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}			,     )
  
  
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Optional[int]							= field(
  			    default=a_			,     metadata={
  			        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
  			    }			,     )
  			A	:							int							= field(
  			    default=16			,     metadata={
  			        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
  			    }			,     )
  
  
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= field(
  			    default=768			,     metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}			,     )
  			A	:							int							= field(
  			    default=128			,     metadata={
  			        '''help''': (
  			            '''The number of bi-directional links created for every new element during the HNSW index construction.'''
  			        )
  			    }			,     )
  
  
  if __name__ == "__main__":
  					logging.basicConfig(level=logging.WARNING)
  					logger.setLevel(logging.INFO)
  
  					UpperCAmelCase__      =							HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  					UpperCAmelCase__ ,     UpperCAmelCase__ ,     UpperCAmelCase__      =							parser.parse_args_into_dataclasses()
  					with TemporaryDirectory() as tmp_dir:
  										UpperCAmelCase__      =							rag_example_args.output_dir or tmp_dir
  										main(rag_example_args, processing_args, index_hnsw_args)
  
  
  
  
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()
  
  
  
  