code: 
  
  
  import logging
  import pathlib
  import re
  import sys
  from dataclasses import dataclass, field
  from typing import Any, Callable, Dict, List, Optional, Set, Union
  
  import datasets
  import librosa
  import numpy as np
  import torch
  from lang_trans import arabic
  from packaging import version
  from torch import nn
  
  from transformers import (
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaForCTC,
      WavaVecaProcessor,
      is_apex_available,
      trainer_utils,
  )
  
  
  if is_apex_available():
  					from apex import amp
  
  if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
  					UpperCAmelCase__      =							True
  					from torch.cuda.amp import autocast
  
  
  UpperCAmelCase__      =							logging.getLogger(__name__)
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							str							= field(
  			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
  			A	:							Optional[str]							= field(
  			    default=a_			,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,     )
  			A	:							Optional[bool]							= field(
  			    default=a_			,     metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}      )
  			A	:							Optional[bool]							= field(
  			    default=a_			,     metadata={'''help''': '''Whether to log verbose messages or not.'''}			,     )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   ModelArguments,_SCREAMING_SNAKE_CASE				:   TrainingArguments   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			logging.basicConfig(
  			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',datefmt='%m/%d/%Y %H:%M:%S',handlers=[logging.StreamHandler(sys.stdout   )],)
  			__A=  logging.WARNING
  			if model_args.verbose_logging:
  						__A=  logging.DEBUG
  			elif trainer_utils.is_main_process(training_args.local_rank   ):
  						__A=  logging.INFO
  			logger.setLevel(_SCREAMING_SNAKE_CASE   )
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							str							= field(
  			    default=a_			,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}      )
  			A	:							Optional[str]							= field(
  			    default=a_			,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
  			A	:							Optional[str]							= field(
  			    default='''train'''			,     metadata={
  			        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
  			    }			,     )
  			A	:							Optional[str]							= field(
  			    default='''validation'''			,     metadata={
  			        '''help''': (
  			            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
  			        )
  			    }			,     )
  			A	:							Optional[str]							= field(
  			    default='''text'''			,     metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}			,     )
  			A	:							Optional[str]							= field(
  			    default='''file'''			,     metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}			,     )
  			A	:							Optional[bool]							= field(
  			    default=a_			,     metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}			,     )
  			A	:							Optional[float]							= field(
  			    default=a_			,     metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}			,     )
  			A	:							Optional[str]							= field(
  			    default='''librispeech'''			,     metadata={
  			        '''help''': (
  			            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
  			            ''' \'buckwalter\'.'''
  			        )
  			    }			,     )
  			A	:							bool							= field(
  			    default=a_			,     metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}      )
  			A	:							Optional[int]							= field(
  			    default=a_			,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''}			,     )
  
  
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							bool							= False
  			A	:							Optional[str]							= None
  			A	:							Optional[str]							= "|"
  			A	:							Optional[Dict[str, str]]							= field(default_factory=a_      )
  			A	:							Optional[Set[str]]							= field(default_factory=a_      )
  			A	:							Optional[Callable[[str], str]]							= None
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : str ,						lowerCAmelCase_       : str  )			->		List[Any]:
  						if name == "librispeech":
  									return cls()
  						if name == "timit":
  									return cls(
  									    do_lower_case=lowerCAmelCase_ ,						translation_table=str.maketrans({'-': ' '}  ) ,						)
  						if name == "buckwalter":
  									__A=  {
  									    '-': ' ',  # sometimes used to represent pauses
  									    '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
  									}
  									return cls(
  									    vocab_file=pathlib.Path(lowerCAmelCase_  ).parent.joinpath('vocab/buckwalter.json'  ) ,						word_delimiter_token='/' ,						translation_table=str.maketrans(lowerCAmelCase_  ) ,						words_to_remove={'sil'} ,						untransliterator=arabic.buckwalter.untransliterate ,						)
  						raise ValueError(F"""Unsupported orthography: '{name}'."""  )
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : str  )			->		str:
  						# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
  						if len(self.translation_table  ) > 0:
  									__A=  text.translate(self.translation_table  )
  						if len(self.words_to_remove  ) == 0:
  									__A=  ' '.join(text.split()  )  # clean up whitespaces
  						else:
  									__A=  ' '.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
  						return text
  
  			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : ModelArguments  )			->		WavaVecaProcessor:
  						__A=  WavaVecaFeatureExtractor.from_pretrained(
  						    model_args.model_name_or_path ,						cache_dir=model_args.cache_dir  )
  						if self.vocab_file:
  									__A=  WavaVecaCTCTokenizer(
  									    self.vocab_file ,						cache_dir=model_args.cache_dir ,						do_lower_case=self.do_lower_case ,						word_delimiter_token=self.word_delimiter_token ,						)
  						else:
  									__A=  WavaVecaCTCTokenizer.from_pretrained(
  									    model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						do_lower_case=self.do_lower_case ,						word_delimiter_token=self.word_delimiter_token ,						)
  						return WavaVecaProcessor(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							WavaVecaProcessor
  			A	:							Union[bool, str]							= True
  			A	:							Optional[int]							= None
  			A	:							Optional[int]							= None
  			A	:							Optional[int]							= None
  			A	:							Optional[int]							= None
  			def __call__(     self       : str ,						lowerCAmelCase_       : List[Dict[str, Union[List[int], torch.Tensor]]]  )			->		Dict[str, torch.Tensor]:
  						# split inputs and labels since they have to be of different lenghts and need
  						# different padding methods
  						__A=  [{'input_values': feature['input_values']} for feature in features]
  						__A=  [{'input_ids': feature['labels']} for feature in features]
  
  						__A=  self.processor.pad(
  						    lowerCAmelCase_ ,						padding=self.padding ,						max_length=self.max_length ,						pad_to_multiple_of=self.pad_to_multiple_of ,						return_tensors='pt' ,						)
  						__A=  self.processor.pad(
  						    labels=lowerCAmelCase_ ,						padding=self.padding ,						max_length=self.max_length_labels ,						pad_to_multiple_of=self.pad_to_multiple_of_labels ,						return_tensors='pt' ,						)
  
  						# replace padding with -100 to ignore loss correctly
  						__A=  labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1  ) ,						-100  )
  
  						__A=  labels
  
  						return batch
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : nn.Module ,						lowerCAmelCase_       : Dict[str, Union[torch.Tensor, Any]]  )			->		torch.Tensor:
  						model.train()
  						__A=  self._prepare_inputs(lowerCAmelCase_  )
  
  						if self.use_amp:
  									with autocast():
  												__A=  self.compute_loss(lowerCAmelCase_ ,						lowerCAmelCase_  )
  						else:
  									__A=  self.compute_loss(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						if self.args.n_gpu > 1:
  									if model.module.config.ctc_loss_reduction == "mean":
  												__A=  loss.mean()
  									elif model.module.config.ctc_loss_reduction == "sum":
  												__A=  loss.sum() / (inputs['labels'] >= 0).sum()
  									else:
  												raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""  )
  
  						if self.args.gradient_accumulation_steps > 1:
  									__A=  loss / self.args.gradient_accumulation_steps
  
  						if self.use_amp:
  									self.scaler.scale(lowerCAmelCase_  ).backward()
  						elif self.use_apex:
  									with amp.scale_loss(lowerCAmelCase_ ,						self.optimizer  ) as scaled_loss:
  												scaled_loss.backward()
  						elif self.deepspeed:
  									self.deepspeed.backward(lowerCAmelCase_  )
  						else:
  									loss.backward()
  
  						return loss.detach()
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
  
  			__A,  __A,  __A=  parser.parse_args_into_dataclasses()
  			configure_logger(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  			__A=  Orthography.from_name(data_args.orthography.lower()   )
  			__A=  orthography.create_processor(_SCREAMING_SNAKE_CASE   )
  			__A=  WavaVecaForCTC.from_pretrained(
  			    model_args.model_name_or_path,cache_dir=model_args.cache_dir,gradient_checkpointing=training_args.gradient_checkpointing,vocab_size=len(processor.tokenizer   ),)
  
  			__A=  datasets.load_dataset(
  			    data_args.dataset_name,data_args.dataset_config_name,split=data_args.train_split_name   )
  			__A=  datasets.load_dataset(
  			    data_args.dataset_name,data_args.dataset_config_name,split=data_args.validation_split_name   )
  
  			__A=  datasets.load_metric('wer'   )
  			__A=  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
  			__A=  ''.join(t for t in processor.tokenizer.get_vocab().keys() if len(_SCREAMING_SNAKE_CASE   ) == 1   )
  			__A=  re.compile(  # remove characters not in vocabulary
  			    rf"""[^\s{re.escape(_SCREAMING_SNAKE_CASE   )}]""",flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0,)
  			__A=  []
  
  			def prepare_example(_SCREAMING_SNAKE_CASE				:   Optional[int]   ):  # TODO(elgeish) make use of multiprocessing?
  						__A,  __A=  librosa.load(example[data_args.speech_file_column],sr=_SCREAMING_SNAKE_CASE   )
  						if data_args.max_duration_in_seconds is not None:
  									__A=  len(example['speech']   ) / example['sampling_rate']
  						# Normalize and clean up text; order matters!
  						__A=  orthography.preprocess_for_training(example[data_args.target_text_column]   )
  						__A=  vocabulary_text_cleaner.sub('',_SCREAMING_SNAKE_CASE   )
  						if updated_text != example[data_args.target_text_column]:
  									text_updates.append((example[data_args.target_text_column], updated_text)   )
  									__A=  updated_text
  						return example
  
  			__A=  train_dataset.map(_SCREAMING_SNAKE_CASE,remove_columns=[data_args.speech_file_column]   )
  			__A=  val_dataset.map(_SCREAMING_SNAKE_CASE,remove_columns=[data_args.speech_file_column]   )
  
  			if data_args.max_duration_in_seconds is not None:
  
  						def filter_by_max_duration(_SCREAMING_SNAKE_CASE				:   Union[str, Any]   ):
  									return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
  						__A=  len(_SCREAMING_SNAKE_CASE   )
  						__A=  len(_SCREAMING_SNAKE_CASE   )
  						__A=  train_dataset.filter(_SCREAMING_SNAKE_CASE,remove_columns=['duration_in_seconds']   )
  						__A=  val_dataset.filter(_SCREAMING_SNAKE_CASE,remove_columns=['duration_in_seconds']   )
  						if len(_SCREAMING_SNAKE_CASE   ) > old_train_size:
  									logger.warning(
  									    f"""Filtered out {len(_SCREAMING_SNAKE_CASE   ) - old_train_size} train example(s) longer than"""
  									    f""" {data_args.max_duration_in_seconds} second(s)."""   )
  						if len(_SCREAMING_SNAKE_CASE   ) > old_val_size:
  									logger.warning(
  									    f"""Filtered out {len(_SCREAMING_SNAKE_CASE   ) - old_val_size} validation example(s) longer than"""
  									    f""" {data_args.max_duration_in_seconds} second(s)."""   )
  			logger.info(f"""Split sizes: {len(_SCREAMING_SNAKE_CASE   )} train and {len(_SCREAMING_SNAKE_CASE   )} validation."""   )
  
  			logger.warning(f"""Updated {len(_SCREAMING_SNAKE_CASE   )} transcript(s) using '{data_args.orthography}' orthography rules."""   )
  			if logger.isEnabledFor(logging.DEBUG   ):
  						for original_text, updated_text in text_updates:
  									logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\""""   )
  			__A=  None
  
  			def prepare_dataset(_SCREAMING_SNAKE_CASE				:   str   ):
  						# check that all files have the correct sampling rate
  						assert (
  						    len(set(batch['sampling_rate']   )   ) == 1
  						), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
  
  						__A=  processor(
  						    audio=batch['speech'],text=batch[data_args.target_text_column],sampling_rate=batch['sampling_rate'][0]   )
  						batch.update(_SCREAMING_SNAKE_CASE   )
  						return batch
  
  			__A=  train_dataset.map(
  			    _SCREAMING_SNAKE_CASE,batch_size=training_args.per_device_train_batch_size,batched=_SCREAMING_SNAKE_CASE,num_proc=data_args.preprocessing_num_workers,)
  			__A=  val_dataset.map(
  			    _SCREAMING_SNAKE_CASE,batch_size=training_args.per_device_train_batch_size,batched=_SCREAMING_SNAKE_CASE,num_proc=data_args.preprocessing_num_workers,)
  
  			__A=  DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE,padding=_SCREAMING_SNAKE_CASE   )
  
  			def compute_metrics(_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  						__A=  pred.predictions
  						__A=  np.argmax(_SCREAMING_SNAKE_CASE,axis=-1   )
  
  						__A=  processor.tokenizer.pad_token_id
  
  						__A=  processor.batch_decode(_SCREAMING_SNAKE_CASE   )
  						# we do not want to group tokens when computing the metrics
  						__A=  processor.batch_decode(pred.label_ids,group_tokens=_SCREAMING_SNAKE_CASE   )
  						if logger.isEnabledFor(logging.DEBUG   ):
  									for reference, predicted in zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ):
  												logger.debug(f"""reference: \"{reference}\""""   )
  												logger.debug(f"""predicted: \"{predicted}\""""   )
  												if orthography.untransliterator is not None:
  															logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(_SCREAMING_SNAKE_CASE   )}\""""   )
  															logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(_SCREAMING_SNAKE_CASE   )}\""""   )
  
  						__A=  wer_metric.compute(predictions=_SCREAMING_SNAKE_CASE,references=_SCREAMING_SNAKE_CASE   )
  
  						return {"wer": wer}
  
  			if model_args.freeze_feature_extractor:
  						model.freeze_feature_extractor()
  
  			__A=  CTCTrainer(
  			    model=_SCREAMING_SNAKE_CASE,data_collator=_SCREAMING_SNAKE_CASE,args=_SCREAMING_SNAKE_CASE,compute_metrics=_SCREAMING_SNAKE_CASE,train_dataset=_SCREAMING_SNAKE_CASE,eval_dataset=_SCREAMING_SNAKE_CASE,tokenizer=processor.feature_extractor,)
  
  			trainer.train()
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  
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()
  
  
  
  