import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def  A      (       _lowercase=32						,    _lowercase=10						,    _lowercase=100						,    _lowercase=1_026						,    _lowercase=True						,    _lowercase="data/tokenized_stories_train_wikitext103.jbl"						,    _lowercase="igf_context_pairs.jbl"						,    ):
						set_seed(3		)
						# generate train_data and objective_set
						SCREAMING_SNAKE_CASE				:       List[Any]     							=				generate_datasets(
						    _lowercase						,    _lowercase						,    number=_lowercase						,    min_len=1_026						,    trim=_lowercase		)
						# keeps model same across runs
						set_seed(4		)
						# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
						# can we train on GPU?
						SCREAMING_SNAKE_CASE				:       Optional[int]     							=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)

						# load pretrained model
						SCREAMING_SNAKE_CASE				:       Dict     							=				load_gpta('''gpt2'''		).to(_lowercase		)
						print('''computing perplexity on objective set'''		)
						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				compute_perplexity(_lowercase						,    _lowercase						,    _lowercase		).item()
						print('''perplexity on objective set:'''						,    _lowercase		)

						# collect igf pairs and save to file demo.jbl
						collect_objective_set(_lowercase						,    _lowercase						,    _lowercase						,    _lowercase						,    _lowercase						,    _lowercase						,    _lowercase						,    _lowercase		)

						# clean up, delete model and data we don't need anymore
						del model, train_data, objective_set
						torch.cuda.empty_cache()
def  A      (       _lowercase						,    _lowercase=15						,    _lowercase=128						,    _lowercase=100						,    _lowercase="igf_model.pt"						,    ):
						set_seed(42		)

						# Load pre-trained model
						SCREAMING_SNAKE_CASE				:       Any     							=				GPTaLMHeadModel.from_pretrained('''gpt2'''		)

						# Initialize secondary learner to use embedding weights of model
						SCREAMING_SNAKE_CASE				:       Tuple     							=				SecondaryLearner(_lowercase		)

						# Train secondary learner
						SCREAMING_SNAKE_CASE				:       Tuple     							=				train_secondary_learner(
						    _lowercase						,    _lowercase						,    max_epochs=_lowercase						,    batch_size=_lowercase						,    eval_freq=100						,    igf_model_path=_lowercase						,    )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner
def  A      (       _lowercase						,    _lowercase						,    _lowercase						,    _lowercase=32						,    _lowercase=1_000						,    _lowercase=16						,    _lowercase=1.0						,    _lowercase=recopy_gpta						,    _lowercase=None						,    _lowercase=10						,    _lowercase="gpt2_finetuned.pt"						,    ):
						SCREAMING_SNAKE_CASE				:       List[Any]     							=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)
						SCREAMING_SNAKE_CASE				:       Dict     							=				RandomSampler(_lowercase		)
						SCREAMING_SNAKE_CASE				:       Any     							=				DataLoader(_lowercase						,    sampler=_lowercase		)

						SCREAMING_SNAKE_CASE				:       Tuple     							=				max_steps // (len(_lowercase		)) + 1
						SCREAMING_SNAKE_CASE				:       str     							=				0
						SCREAMING_SNAKE_CASE				:       int     							=				torch.zeros((1, context_len)						,    dtype=torch.long						,    device=_lowercase		)
						SCREAMING_SNAKE_CASE				:       Tuple     							=				recopy_model(_lowercase						,    _lowercase						,    _lowercase		)

						model.train()
						if secondary_learner is not None:
												secondary_learner.to(_lowercase		)
												secondary_learner.eval()
						SCREAMING_SNAKE_CASE				:       List[str]     							=				[]
						SCREAMING_SNAKE_CASE				:       List[Any]     							=				0

						SCREAMING_SNAKE_CASE				:       int     							=				[]
						SCREAMING_SNAKE_CASE				:       List[Any]     							=				[]

						# Compute the performance of the transformer model at the beginning
						SCREAMING_SNAKE_CASE				:       Tuple     							=				compute_perplexity(_lowercase						,    _lowercase						,    _lowercase		)
						test_perps.append(_lowercase		)
						print('''Test perplexity, step'''						,    _lowercase						,    ''':'''						,    _lowercase		)
						for epoch in range(int(_lowercase		)		):
												for step, example in enumerate(_lowercase		):
																		torch.cuda.empty_cache()
																		SCREAMING_SNAKE_CASE				:       Dict     							=				random.randint(0						,    example.size(2		) - context_len - 1		)
																		SCREAMING_SNAKE_CASE				:       int     							=				example[0, 0, start : start + context_len]
																		lm_optimizer.zero_grad()
																		SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				model(_lowercase						,    labels=_lowercase		)
																		SCREAMING_SNAKE_CASE				:       List[Any]     							=				True

																		if secondary_learner is not None:
																								SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				secondary_learner.forward(
																								    torch.tensor(_lowercase						,    dtype=torch.long						,    device=_lowercase		).unsqueeze(0		)		)[0].item()
																								observed_qs.append(float(_lowercase		)		)

																								# Here we implement the simple non-constant threshold for the predicted IG(X) value
																								# We will decay the selectivity of our secondary learner filter from
																								# 1 standard deviation above average to 1 below average after 10 batches.

																								if global_step == 10:
																														SCREAMING_SNAKE_CASE				:       Dict     							=				-1
																								if predicted_q < threshold:
																														SCREAMING_SNAKE_CASE				:       List[Any]     							=				False

																														       # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()		)		)
																								SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				outputs[0]
																								lm_loss.backward()
																								examples += 1

																		del outputs

																		# Once the batch is filled with enough contexts, backprop on the batch.
																		if examples == batch_size:
																								torch.cuda.empty_cache()
																								SCREAMING_SNAKE_CASE				:       int     							=				0
																								# Do LM backprop
																								torch.nn.utils.clip_grad_norm_(model.parameters()						,    3.0		)
																								lm_optimizer.step()
																								lm_scheduler.step()  # Update learning rate schedule
																								global_step += 1
																								# Compute the performance of the transformer model at this batch
																								if global_step % eval_interval == 0:
																														SCREAMING_SNAKE_CASE				:       int     							=				compute_perplexity(_lowercase						,    _lowercase						,    _lowercase		)
																														test_perps.append(_lowercase		)

																														print('''Test perplexity, step'''						,    _lowercase						,    ''':'''						,    _lowercase		)
																														       # Break out of the loop after 60 batches
																		if max_steps > 0 and global_step > 60:
																								break
												if max_steps > 0 and global_step > 60:
																		break

																		 # save finetuned transformer model
						torch.save(model.state_dict()						,    _lowercase		)
						torch.cuda.empty_cache()
						# Do some cleaning up so we can reinitialize for the next run of this function
						del lm_optimizer
						del lm_scheduler
						return model
def  A      (       ):
						SCREAMING_SNAKE_CASE				:       Tuple     							=				argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''		)

						# Required parameters
						parser.add_argument(
						    '''--data_dir'''						,    default=_lowercase						,    type=_lowercase						,    required=_lowercase						,    help='''The input data dir. Should contain data files for WikiText.'''						,    )
						parser.add_argument(
						    '''--model_name_or_path'''						,    default=_lowercase						,    type=_lowercase						,    required=_lowercase						,    help='''Path to pretrained model or model identifier from huggingface.co/models'''						,    )
						parser.add_argument(
						    '''--data_file'''						,    type=_lowercase						,    default=_lowercase						,    help=(
						        '''A jbl file containing tokenized data which can be split as objective dataset, '''
						        '''train_dataset and test_dataset.'''
						    )						,    )

						parser.add_argument(
						    '''--igf_data_file'''						,    type=_lowercase						,    default=_lowercase						,    help='''A jbl file containing the context and information gain pairs to train secondary learner.'''						,    )

						parser.add_argument(
						    '''--output_dir'''						,    default=_lowercase						,    type=_lowercase						,    required=_lowercase						,    help='''The output directory where the final fine-tuned model is stored.'''						,    )

						parser.add_argument(
						    '''--tokenizer_name'''						,    default=_lowercase						,    type=_lowercase						,    help='''Pretrained tokenizer name or path if not the same as model_name'''						,    )
						parser.add_argument('''--seed'''						,    type=_lowercase						,    default=_lowercase						,    help='''A seed for reproducible training.'''		)

						parser.add_argument(
						    '''--context_len'''						,    default=32						,    type=_lowercase						,    help=(
						        '''The maximum total input sequence length after tokenization. Sequences longer '''
						        '''than this will be truncated, sequences shorter will be padded.'''
						    )						,    )

						parser.add_argument(
						    '''--size_objective_set'''						,    default=100						,    type=_lowercase						,    help='''number of articles that are long enough to be used as our objective set'''						,    )
						parser.add_argument(
						    '''--eval_freq'''						,    default=100						,    type=_lowercase						,    help='''secondary model evaluation is triggered at eval_freq'''		)

						parser.add_argument('''--max_steps'''						,    default=1_000						,    type=_lowercase						,    help='''To calculate training epochs'''		)

						parser.add_argument(
						    '''--secondary_learner_batch_size'''						,    default=128						,    type=_lowercase						,    help='''batch size of training data for secondary learner'''						,    )

						parser.add_argument(
						    '''--batch_size'''						,    default=16						,    type=_lowercase						,    help='''batch size of training data of language model(gpt2) '''		)

						parser.add_argument(
						    '''--eval_interval'''						,    default=10						,    type=_lowercase						,    help=(
						        '''decay the selectivity of our secondary learner filter from'''
						        '''1 standard deviation above average to 1 below average after 10 batches'''
						    )						,    )

						parser.add_argument(
						    '''--number'''						,    default=100						,    type=_lowercase						,    help='''The number of examples split to be used as objective_set/test_data'''		)

						parser.add_argument(
						    '''--min_len'''						,    default=1_026						,    type=_lowercase						,    help='''The minimum length of the article to be used as objective set'''		)

						parser.add_argument(
						    '''--secondary_learner_max_epochs'''						,    default=15						,    type=_lowercase						,    help='''number of epochs to train secondary learner'''		)

						parser.add_argument('''--trim'''						,    default=_lowercase						,    type=_lowercase						,    help='''truncate the example if it exceeds context length'''		)

						parser.add_argument(
						    '''--threshold'''						,    default=1.0						,    type=_lowercase						,    help=(
						        '''The threshold value used by secondary learner to filter the train_data and allow only'''
						        ''' informative data as input to the model'''
						    )						,    )

						parser.add_argument('''--finetuned_model_name'''						,    default='''gpt2_finetuned.pt'''						,    type=_lowercase						,    help='''finetuned_model_name'''		)

						parser.add_argument(
						    '''--recopy_model'''						,    default=_lowercase						,    type=_lowercase						,    help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''						,    )

						# function calls
						# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
						generate_n_pairs(
						    context_len=32						,    max_steps=10						,    size_objective_set=100						,    min_len=1_026						,    trim=_lowercase						,    data_file='''data/tokenized_stories_train_wikitext103.jbl'''						,    igf_data_file='''igf_context_pairs.jbl'''						,    )

						# Load train data for secondary learner
						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				joblib.load('''data/IGF_values.jbl'''		)

						# Train secondary learner
						SCREAMING_SNAKE_CASE				:       Dict     							=				training_secondary_learner(
						    _lowercase						,    secondary_learner_max_epochs=15						,    secondary_learner_batch_size=128						,    eval_freq=100						,    igf_model_path='''igf_model.pt'''						,    )

						# load pretrained gpt2 model
						SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				GPTaLMHeadModel.from_pretrained('''gpt2'''		)
						set_seed(42		)

						# Generate train and test data to train and evaluate gpt2 model
						SCREAMING_SNAKE_CASE				:       int     							=				generate_datasets(
						    context_len=32						,    file='''data/tokenized_stories_train_wikitext103.jbl'''						,    number=100						,    min_len=1_026						,    trim=_lowercase		)

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    _lowercase						,    _lowercase						,    _lowercase						,    context_len=32						,    max_steps=1_000						,    batch_size=16						,    threshold=1.0						,    recopy_model=_lowercase						,    secondary_learner=_lowercase						,    eval_interval=10						,    finetuned_model_name='''gpt2_finetuned.pt'''						,    )


if __name__ == "__main__":
					main()






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			fname.split(os.path.sep		)[-1]
 return re.search(R'''^(.*)_\d+\.jpg$'''	,       lowercase		).groups()[0]




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	Any		,	_lowercase       :	Any=None		,	_lowercase       :	List[str]=None				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			file_names
    _UpperCamelCase:  List[Any]    						=			image_transform
    _UpperCamelCase:  Tuple    						=			label_to_id



   def __len__(						self       :	List[str]				):



    """simple docstring"""



    return len(self.file_names				)



   def __getitem__(						self       :	List[str]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.file_names[idx]
    _UpperCamelCase:  Optional[int]    						=			PIL.Image.open(_lowercase				)
    _UpperCamelCase:  List[str]    						=			raw_image.convert('''RGB'''				)
    if self.image_transform is not None:
     _UpperCamelCase:  Optional[Any]    						=			self.image_transform(_lowercase				)
    _UpperCamelCase:  Tuple    						=			extract_label(_lowercase				)
    if self.label_to_id is not None:
     _UpperCamelCase:  Any    						=			self.label_to_id[label]
    return {"image": image, "label": label}




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Any		)     ->					str:







 '''simple docstring'''
 # Initialize accelerator
 if args.with_tracking:
  _UpperCamelCase:  Tuple    						=			Accelerator(
      cpu=args.cpu	,       mixed_precision=args.mixed_precision	,       log_with='''all'''	,       project_dir=args.project_dir		)
 else:
  _UpperCamelCase:  Dict    						=			Accelerator(cpu=args.cpu	,       mixed_precision=args.mixed_precision		)

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:  List[str]    						=			config['''lr''']
 _UpperCamelCase:  int    						=			int(config['''num_epochs''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''seed''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''batch_size''']		)
 _UpperCamelCase:  List[str]    						=			config['''image_size''']
 if not isinstance(lowercase	,       (list, tuple)		):
  _UpperCamelCase:  Optional[int]    						=			(image_size, image_size)

 # Parse out whether we are saving every epoch or after a certain number of batches
 if hasattr(args.checkpointing_steps	,       '''isdigit'''		):
  if args.checkpointing_steps == "epoch":
   _UpperCamelCase:  Any    						=			args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _UpperCamelCase:  List[str]    						=			int(args.checkpointing_steps		)
  else:
   raise ValueError(
       F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
 else:
  _UpperCamelCase:  Any    						=			None

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  _UpperCamelCase:  Union[str, Any]    						=			os.path.split(lowercase		)[-1].split('''.'''		)[0]
  accelerator.init_trackers(lowercase	,       lowercase		)

 # Grab all the image filenames
 _UpperCamelCase:  List[str]    						=			[os.path.join(args.data_dir	,       lowercase		) for fname in os.listdir(args.data_dir		) if fname.endswith('''.jpg'''		)]

 # Build the label correspondences
 _UpperCamelCase:  Optional[Any]    						=			[extract_label(lowercase		) for fname in file_names]
 _UpperCamelCase:  int    						=			list(set(lowercase		)		)
 id_to_label.sort()
 _UpperCamelCase:  Tuple    						=			{lbl: i for i, lbl in enumerate(lowercase		)}

 # Set the seed before splitting the data.
 np.random.seed(lowercase		)
 torch.manual_seed(lowercase		)
 torch.cuda.manual_seed_all(lowercase		)

 # Split our filenames between train and validation
 _UpperCamelCase:  List[str]    						=			np.random.permutation(len(lowercase		)		)
 _UpperCamelCase:  Dict    						=			int(0.8 * len(lowercase		)		)
 _UpperCamelCase:  Optional[int]    						=			random_perm[:cut]
 _UpperCamelCase:  Optional[int]    						=			random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 _UpperCamelCase:  List[Any]    						=			Compose([RandomResizedCrop(lowercase	,       scale=(0.5, 1.0)		), ToTensor()]		)
 _UpperCamelCase:  int    						=			PetsDataset(
     [file_names[i] for i in train_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # For evaluation, we use a deterministic Resize
 _UpperCamelCase:  Dict    						=			Compose([Resize(lowercase		), ToTensor()]		)
 _UpperCamelCase:  str    						=			PetsDataset([file_names[i] for i in eval_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # Instantiate dataloaders.
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  str    						=			create_model('''resnet50d'''	,       pretrained=lowercase	,       num_classes=len(lowercase		)		)

 # 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).
 _UpperCamelCase:  Optional[int]    						=			model.to(accelerator.device		)

 # Freezing the base model
 for param in model.parameters():
  _UpperCamelCase:  Optional[Any]    						=			False
 for param in model.get_classifier().parameters():
  _UpperCamelCase:  Optional[int]    						=			True

 # We normalize the batches of images to be a bit faster.
 _UpperCamelCase:  Union[str, Any]    						=			torch.tensor(model.default_cfg['''mean''']		)[None, :, None, None].to(accelerator.device		)
 _UpperCamelCase:  List[Any]    						=			torch.tensor(model.default_cfg['''std''']		)[None, :, None, None].to(accelerator.device		)

 # Instantiate optimizer
 _UpperCamelCase:  List[str]    						=			torch.optim.Adam(params=model.parameters()	,       lr=lr / 25		)

 # Instantiate learning rate scheduler
 _UpperCamelCase:  Any    						=			OneCycleLR(optimizer=lowercase	,       max_lr=lowercase	,       epochs=lowercase	,       steps_per_epoch=len(lowercase		)		)

 # 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.
 _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  List[str]    						=			accelerator.prepare(
     lowercase	,       lowercase	,       lowercase	,       lowercase	,       lowercase		)
 # We need to keep track of how many total steps we have iterated over
 _UpperCamelCase:  Tuple    						=			0
 # We also need to keep track of the starting epoch so files are named properly
 _UpperCamelCase:  List[str]    						=			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		)
   _UpperCamelCase:  Union[str, Any]    						=			os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   _UpperCamelCase:  Union[str, Any]    						=			[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   _UpperCamelCase:  Union[str, Any]    						=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _UpperCamelCase:  List[Any]    						=			os.path.splitext(lowercase		)[0]

  if "epoch" in training_difference:
   _UpperCamelCase:  Any    						=			int(training_difference.replace('''epoch_'''	,       ''''''		)		) + 1
   _UpperCamelCase:  List[str]    						=			None
  else:
   _UpperCamelCase:  int    						=			int(training_difference.replace('''step_'''	,       ''''''		)		)
   _UpperCamelCase:  List[Any]    						=			resume_step // len(lowercase		)
   resume_step -= starting_epoch * len(lowercase		)

    # Now we train the model
 for epoch in range(lowercase	,       lowercase		):
  model.train()
  if args.with_tracking:
   _UpperCamelCase:  Optional[int]    						=			0
  if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
   # We need to skip steps until we reach the resumed step
   _UpperCamelCase:  Union[str, Any]    						=			accelerator.skip_first_batches(lowercase	,       lowercase		)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _UpperCamelCase:  Optional[int]    						=			train_dataloader
  for batch in active_dataloader:
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  int    						=			(batch['''image'''] - mean) / std
   _UpperCamelCase:  List[str]    						=			model(lowercase		)
   _UpperCamelCase:  Dict    						=			torch.nn.functional.cross_entropy(lowercase	,       batch['''label''']		)
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(lowercase		)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(lowercase	,       lowercase		):
    _UpperCamelCase:  Any    						=			F"""step_{overall_step}"""
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _UpperCamelCase:  List[Any]    						=			os.path.join(args.output_dir	,       lowercase		)
     accelerator.save_state(lowercase		)
  model.eval()
  _UpperCamelCase:  List[str]    						=			0
  _UpperCamelCase:  Union[str, Any]    						=			0
  for step, batch in enumerate(lowercase		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  Union[str, Any]    						=			(batch['''image'''] - mean) / std
   with torch.no_grad():
    _UpperCamelCase:  List[Any]    						=			model(lowercase		)
   _UpperCamelCase:  Optional[Any]    						=			outputs.argmax(dim=-1		)
   _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			accelerator.gather_for_metrics((predictions, batch['''label'''])		)
   _UpperCamelCase:  List[str]    						=			predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  _UpperCamelCase:  Optional[int]    						=			accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""		)
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': 100 * eval_metric,
           '''train_loss''': total_loss.item() / len(lowercase		),
           '''epoch''': epoch,
       }	,       step=lowercase	,       )
  if checkpointing_steps == "epoch":
   _UpperCamelCase:  Tuple    						=			F"""epoch_{epoch}"""
   if args.output_dir is not None:
    _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       lowercase		)
   accelerator.save_state(lowercase		)

 if args.with_tracking:
  accelerator.end_training()




def    lowerCAmelCase_  (		)     ->					Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument('''--data_dir'''	,       required=lowercase	,       help='''The data folder on disk.'''		)
 parser.add_argument('''--fp16'''	,       action='''store_true'''	,       help='''If passed, will use FP16 training.'''		)
 parser.add_argument(
     '''--mixed_precision'''	,       type=lowercase	,       default=lowercase	,       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=lowercase	,       default=lowercase	,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,       )
 parser.add_argument(
     '''--output_dir'''	,       type=lowercase	,       default='''.'''	,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,       )
 parser.add_argument(
     '''--resume_from_checkpoint'''	,       type=lowercase	,       default=lowercase	,       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(
     '''--project_dir'''	,       type=lowercase	,       default='''logs'''	,       help='''Location on where to store experiment tracking logs` and relevent project information'''	,       )
 _UpperCamelCase:  Any    						=			parser.parse_args()
 _UpperCamelCase:  Optional[int]    						=			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()