






'''simple docstring'''







import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__lowerCAmelCase									=   logging.getLogger(__name__)





def 		_UpperCAmelCase (				__A				: Dict    ,		__A				: Union[str, Any]   ):
				# save results
				if os.path.exists(__A   ):
								if os.path.exists(os.path.join(__A    ,		'''config.json'''   )   ) and os.path.isfile(
								    os.path.join(__A    ,		'''config.json'''   )   ):
												os.remove(os.path.join(__A    ,		'''config.json'''   )   )
								if os.path.exists(os.path.join(__A    ,		'''pytorch_model.bin'''   )   ) and os.path.isfile(
								    os.path.join(__A    ,		'''pytorch_model.bin'''   )   ):
												os.remove(os.path.join(__A    ,		'''pytorch_model.bin'''   )   )
				else:
								os.makedirs(__A   )
				model.save_pretrained(__A   )





def 		_UpperCAmelCase (				__A				: Any    ,		__A				: List[Any]=False   ):
				a_		:	Union[str, Any]									=   2
				if unlogit:
								a_		:	Union[str, Any]									=   torch.pow(__A    ,		__A   )
				a_		:	Optional[Any]									=   p * torch.log(__A   )
				a_		:	int									=   0
				return -plogp.sum(dim=-1   )





def 		_UpperCAmelCase (				__A				: int   ):
				logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(__A   )   )   )   )
				for row in range(len(__A   )   ):
								if tensor.dtype != torch.long:
												logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:.5f}' for x in tensor[row].cpu().data   )   )
								else:
												logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:d}' for x in tensor[row].cpu().data   )   )





def 		_UpperCAmelCase (				__A				: List[Any]    ,		__A				: Union[str, Any]    ,		__A				: Optional[int]    ,		__A				: Tuple=True    ,		__A				: Tuple=True    ,		__A				: Tuple=None    ,		__A				: Optional[int]=False   ):
				a_					,      a_		:	Dict									=   model.config.num_hidden_layers, model.config.num_attention_heads
				a_		:	Tuple									=   torch.zeros(__A    ,		__A   ).to(args.device   )
				a_		:	Optional[Any]									=   torch.zeros(__A    ,		__A   ).to(args.device   )

				if head_mask is None:
								a_		:	Tuple									=   torch.ones(__A    ,		__A   ).to(args.device   )

				head_mask.requires_grad_(requires_grad=__A   )
				# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
				if actually_pruned:
								a_		:	Optional[int]									=   None

				a_		:	str									=   0.0
				a_		:	Dict									=   0.0
				for step, inputs in enumerate(tqdm(__A    ,		desc='''Iteration'''    ,		disable=args.local_rank not in [-1, 0]   )   ):
								a_		:	str									=   tuple(t.to(args.device   ) for t in inputs   )
								((a_)					,      )		:	Dict									=   inputs

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								a_		:	Tuple									=   model(__A    ,		labels=__A    ,		head_mask=__A   )
								#  (loss), lm_logits, presents, (all hidden_states), (attentions)
								a_					,      a_					,      a_		:	List[str]									=   (
								    outputs[0],
								    outputs[1],
								    outputs[-1],
								)  # Loss and logits are the first, attention the last
								loss.backward()  # Backpropagate to populate the gradients in the head mask
								total_loss += loss.detach().cpu().numpy()
								if compute_entropy:
												for layer, attn in enumerate(__A   ):
																a_		:	Tuple									=   entropy(attn.detach()    ,		__A   )
																attn_entropy[layer] += masked_entropy.sum(-1   ).sum(0   ).sum(0   ).detach()

								if compute_importance:
												head_importance += head_mask.grad.abs().detach()
								tot_tokens += torch.ones_like(__A   ).float().detach().sum().data

				# Normalize
				attn_entropy /= tot_tokens
				head_importance /= tot_tokens
				# Layerwise importance normalization
				if not args.dont_normalize_importance_by_layer:
								a_		:	Union[str, Any]									=   2
								a_		:	Union[str, Any]									=   torch.pow(torch.pow(__A    ,		__A   ).sum(-1   )    ,		1 / exponent   )
								head_importance /= norm_by_layer.unsqueeze(-1   ) + 1E-20

				if not args.dont_normalize_global_importance:
								a_		:	int									=   (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

				# Print matrices
				if compute_entropy:
								logger.info('''Attention entropies'''   )
								print_ad_tensor(__A   )
				if compute_importance:
								logger.info('''Head importance scores'''   )
								print_ad_tensor(__A   )
				logger.info('''Head ranked by importance scores'''   )
				a_		:	List[Any]									=   torch.zeros(head_importance.numel()    ,		dtype=torch.long    ,		device=args.device   )
				a_		:	Union[str, Any]									=   torch.arange(
				    head_importance.numel()    ,		device=args.device   )
				a_		:	Tuple									=   head_ranks.view_as(__A   )
				print_ad_tensor(__A   )
				return attn_entropy, head_importance, total_loss





def 		_UpperCAmelCase (				__A				: str    ,		__A				: List[Any]    ,		__A				: Optional[Any]   ):
				a_					,      a_					,      a_		:	Optional[int]									=   compute_heads_importance(__A    ,		__A    ,		__A    ,		compute_entropy=__A   )
				a_		:	Union[str, Any]									=   1 / loss  # instead of downsteam score use the LM loss
				logger.info('''Pruning: original score: %f, threshold: %f'''    ,		__A    ,		original_score * args.masking_threshold   )

				a_		:	Optional[int]									=   torch.ones_like(__A   )
				a_		:	Dict									=   max(1    ,		int(new_head_mask.numel() * args.masking_amount   )   )

				a_		:	Optional[Any]									=   original_score
				while current_score >= original_score * args.masking_threshold:
								a_		:	int									=   new_head_mask.clone().detach()  # save current head mask
								# heads from least important to most - keep only not-masked heads
								a_		:	Dict									=   float('''Inf'''   )
								a_		:	Tuple									=   head_importance.view(-1   ).sort()[1]

								if len(__A   ) <= num_to_mask:
												print('''BREAK BY num_to_mask'''   )
												break

								# mask heads
								a_		:	List[Any]									=   current_heads_to_mask[:num_to_mask]
								logger.info('''Heads to mask: %s'''    ,		str(current_heads_to_mask.tolist()   )   )
								a_		:	Dict									=   new_head_mask.view(-1   )
								a_		:	Optional[int]									=   0.0
								a_		:	int									=   new_head_mask.view_as(__A   )
								a_		:	Dict									=   new_head_mask.clone().detach()
								print_ad_tensor(__A   )

								# Compute metric and head importance again
								a_					,      a_					,      a_		:	int									=   compute_heads_importance(
								    __A    ,		__A    ,		__A    ,		compute_entropy=__A    ,		head_mask=__A   )
								a_		:	str									=   1 / loss
								logger.info(
								    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''    ,		__A    ,		new_head_mask.sum()    ,		new_head_mask.sum() / new_head_mask.numel() * 1_00    ,		)

				logger.info('''Final head mask'''   )
				print_ad_tensor(__A   )
				np.save(os.path.join(args.output_dir    ,		'''head_mask.npy'''   )    ,		head_mask.detach().cpu().numpy()   )

				return head_mask





def 		_UpperCAmelCase (				__A				: Tuple    ,		__A				: List[str]    ,		__A				: Union[str, Any]    ,		__A				: List[str]   ):
				a_		:	Tuple									=   datetime.now()
				a_					,      a_					,      a_		:	Union[str, Any]									=   compute_heads_importance(
				    __A    ,		__A    ,		__A    ,		compute_entropy=__A    ,		compute_importance=__A    ,		head_mask=__A   )
				a_		:	Union[str, Any]									=   1 / loss
				a_		:	Any									=   datetime.now() - before_time

				a_		:	List[str]									=   sum(p.numel() for p in model.parameters()   )
				a_		:	Any									=   {
				    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A   )   )
				}

				for k, v in heads_to_prune.items():
								if isinstance(__A    ,		__A   ):
												a_		:	List[Any]									=   [
												    v,
												]

				assert sum(len(__A   ) for h in heads_to_prune.values()   ) == (1 - head_mask.long()).sum().item()
				model.prune_heads(__A   )
				a_		:	Optional[int]									=   sum(p.numel() for p in model.parameters()   )

				a_		:	Optional[int]									=   datetime.now()
				a_					,      a_					,      a_		:	Optional[Any]									=   compute_heads_importance(
				    __A    ,		__A    ,		__A    ,		compute_entropy=__A    ,		compute_importance=__A    ,		head_mask=__A    ,		actually_pruned=__A    ,		)

				a_		:	Union[str, Any]									=   1 / loss
				a_		:	Tuple									=   datetime.now() - before_time

				logger.info(
				    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''    ,		__A    ,		__A    ,		pruned_num_params / original_num_params * 1_00    ,		)
				logger.info('''Pruning: score with masking: %f score with pruning: %f'''    ,		__A    ,		__A   )
				logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''    ,		original_time / new_time * 1_00   )
				save_model(__A    ,		args.output_dir   )





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--data_dir'''    ,		default=__A    ,		type=__A    ,		required=__A    ,		help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''    ,		)
				parser.add_argument(
				    '''--model_name_or_path'''    ,		default=__A    ,		type=__A    ,		required=__A    ,		help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,		)
				parser.add_argument(
				    '''--output_dir'''    ,		default=__A    ,		type=__A    ,		required=__A    ,		help='''The output directory where the model predictions and checkpoints will be written.'''    ,		)

				# Other parameters
				parser.add_argument(
				    '''--config_name'''    ,		default=''''''    ,		type=__A    ,		help='''Pretrained config name or path if not the same as model_name_or_path'''    ,		)
				parser.add_argument(
				    '''--tokenizer_name'''    ,		default=''''''    ,		type=__A    ,		help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''    ,		)
				parser.add_argument(
				    '''--cache_dir'''    ,		default=__A    ,		type=__A    ,		help='''Where do you want to store the pre-trained models downloaded from s3'''    ,		)
				parser.add_argument(
				    '''--data_subset'''    ,		type=__A    ,		default=-1    ,		help='''If > 0: limit the data to a subset of data_subset instances.'''   )
				parser.add_argument(
				    '''--overwrite_output_dir'''    ,		action='''store_true'''    ,		help='''Whether to overwrite data in output directory'''   )
				parser.add_argument(
				    '''--overwrite_cache'''    ,		action='''store_true'''    ,		help='''Overwrite the cached training and evaluation sets'''   )

				parser.add_argument(
				    '''--dont_normalize_importance_by_layer'''    ,		action='''store_true'''    ,		help='''Don\'t normalize importance score by layers'''   )
				parser.add_argument(
				    '''--dont_normalize_global_importance'''    ,		action='''store_true'''    ,		help='''Don\'t normalize all importance scores between 0 and 1'''    ,		)

				parser.add_argument(
				    '''--try_masking'''    ,		action='''store_true'''    ,		help='''Whether to try to mask head until a threshold of accuracy.'''   )
				parser.add_argument(
				    '''--masking_threshold'''    ,		default=0.9    ,		type=__A    ,		help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''    ,		)
				parser.add_argument(
				    '''--masking_amount'''    ,		default=0.1    ,		type=__A    ,		help='''Amount to heads to masking at each masking step.'''   )
				parser.add_argument('''--metric_name'''    ,		default='''acc'''    ,		type=__A    ,		help='''Metric to use for head masking.'''   )

				parser.add_argument(
				    '''--max_seq_length'''    ,		default=1_28    ,		type=__A    ,		help=(
				        '''The maximum total input sequence length after WordPiece tokenization. \n'''
				        '''Sequences longer than this will be truncated, sequences shorter padded.'''
				    )    ,		)
				parser.add_argument('''--batch_size'''    ,		default=1    ,		type=__A    ,		help='''Batch size.'''   )

				parser.add_argument('''--seed'''    ,		type=__A    ,		default=42   )
				parser.add_argument('''--local_rank'''    ,		type=__A    ,		default=-1    ,		help='''local_rank for distributed training on gpus'''   )
				parser.add_argument('''--no_cuda'''    ,		action='''store_true'''    ,		help='''Whether not to use CUDA when available'''   )
				parser.add_argument('''--server_ip'''    ,		type=__A    ,		default=''''''    ,		help='''Can be used for distant debugging.'''   )
				parser.add_argument('''--server_port'''    ,		type=__A    ,		default=''''''    ,		help='''Can be used for distant debugging.'''   )
				a_		:	List[str]									=   parser.parse_args()

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('''Waiting for debugger attach'''   )
								ptvsd.enable_attach(address=(args.server_ip, args.server_port)    ,		redirect_output=__A   )
								ptvsd.wait_for_attach()

				# Setup devices and distributed training
				if args.local_rank == -1 or args.no_cuda:
								a_		:	int									=   torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''   )
								a_		:	Union[str, Any]									=   0 if args.no_cuda else torch.cuda.device_count()
				else:
								torch.cuda.set_device(args.local_rank   )
								a_		:	Optional[Any]									=   torch.device('''cuda'''    ,		args.local_rank   )
								a_		:	Dict									=   1
								torch.distributed.init_process_group(backend='''nccl'''   )  # Initializes the distributed backend

				# Setup logging
				logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN   )
				logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device    ,		args.n_gpu    ,		bool(args.local_rank != -1   )   )   )

				a_		:	str									=   GPTaLMHeadModel.from_pretrained(args.model_name_or_path   )

				# Distributed and parallel training
				model.to(args.device   )
				if args.local_rank != -1:
								a_		:	List[Any]									=   nn.parallel.DistributedDataParallel(
								    __A    ,		device_ids=[args.local_rank]    ,		output_device=args.local_rank    ,		find_unused_parameters=__A   )
				elif args.n_gpu > 1:
								a_		:	Optional[int]									=   nn.DataParallel(__A   )

				# Print/save training arguments
				os.makedirs(args.output_dir    ,		exist_ok=__A   )
				torch.save(__A    ,		os.path.join(args.output_dir    ,		'''run_args.bin'''   )   )
				logger.info('''Training/evaluation parameters %s'''    ,		__A   )

				# Prepare dataset
				a_		:	Any									=   np.concatenate(
				    [
				        np.loadtxt(args.data_dir    ,		dtype=np.intaa   ),
				    ]   )
				a_		:	Dict									=   (torch.from_numpy(__A   ),)
				a_		:	Dict									=   TensorDataset(*__A   )
				a_		:	Optional[int]									=   RandomSampler(__A   )
				a_		:	str									=   DataLoader(__A    ,		sampler=__A    ,		batch_size=args.batch_size   )

				# Compute head entropy and importance score
				compute_heads_importance(__A    ,		__A    ,		__A   )

				# Try head masking (set heads to zero until the score goes under a threshole)
				# and head pruning (remove masked heads and see the effect on the network)
				if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
								a_		:	Dict									=   mask_heads(__A    ,		__A    ,		__A   )
								prune_heads(__A    ,		__A    ,		__A    ,		__A   )


if __name__ == "__main__":
		main()












'''simple docstring'''







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 		_UpperCAmelCase (				__A				: Any   ):
				a_		:	List[str]									=   fname.split(os.path.sep   )[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''    ,		__A   ).groups()[0]





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=None	,    __SCREAMING_SNAKE_CASE   :      Any=None       )      ->      Optional[Any]:
							a_		:	List[str]									=   file_names
							a_		:	str									=   image_transform
							a_		:	Optional[Any]									=   label_to_id






			def __len__(			self   :      List[str]       )      ->      Dict:
							return len(self.file_names       )







			def __getitem__(			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[Any]:
							a_		:	Union[str, Any]									=   self.file_names[idx]
							a_		:	Union[str, Any]									=   PIL.Image.open(__SCREAMING_SNAKE_CASE       )
							a_		:	int									=   raw_image.convert('''RGB'''       )
							if self.image_transform is not None:
											a_		:	str									=   self.image_transform(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   extract_label(__SCREAMING_SNAKE_CASE       )
							if self.label_to_id is not None:
											a_		:	Any									=   self.label_to_id[label]
							return {"image": image, "label": label}





def 		_UpperCAmelCase (				__A				: List[Any]    ,		__A				: Any   ):
				# Initialize accelerator
				if args.with_tracking:
								a_		:	Any									=   Accelerator(
								    cpu=args.cpu    ,		mixed_precision=args.mixed_precision    ,		log_with='''all'''    ,		project_dir=args.project_dir   )
				else:
								a_		:	Optional[Any]									=   Accelerator(cpu=args.cpu    ,		mixed_precision=args.mixed_precision   )

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				a_		:	Dict									=   config['''lr''']
				a_		:	str									=   int(config['''num_epochs''']   )
				a_		:	Any									=   int(config['''seed''']   )
				a_		:	Any									=   int(config['''batch_size''']   )
				a_		:	Optional[Any]									=   config['''image_size''']
				if not isinstance(__A    ,		(list, tuple)   ):
								a_		:	Union[str, Any]									=   (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":
												a_		:	List[str]									=   args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												a_		:	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:
								a_		:	str									=   None

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

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

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

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

				# Split our filenames between train and validation
				a_		:	Optional[Any]									=   np.random.permutation(len(__A   )   )
				a_		:	Dict									=   int(0.8 * len(__A   )   )
				a_		:	Optional[Any]									=   random_perm[:cut]
				a_		:	Optional[int]									=   random_perm[cut:]

				# For training we use a simple RandomResizedCrop
				a_		:	Optional[int]									=   Compose([RandomResizedCrop(__A    ,		scale=(0.5, 1.0)   ), ToTensor()]   )
				a_		:	str									=   PetsDataset(
				    [file_names[i] for i in train_split]    ,		image_transform=__A    ,		label_to_id=__A   )

				# For evaluation, we use a deterministic Resize
				a_		:	Any									=   Compose([Resize(__A   ), ToTensor()]   )
				a_		:	Tuple									=   PetsDataset([file_names[i] for i in eval_split]    ,		image_transform=__A    ,		label_to_id=__A   )

				# Instantiate dataloaders.
				a_		:	Dict									=   DataLoader(__A    ,		shuffle=__A    ,		batch_size=__A    ,		num_workers=4   )
				a_		:	str									=   DataLoader(__A    ,		shuffle=__A    ,		batch_size=__A    ,		num_workers=4   )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				a_		:	Union[str, Any]									=   create_model('''resnet50d'''    ,		pretrained=__A    ,		num_classes=len(__A   )   )

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

				# Freezing the base model
				for param in model.parameters():
								a_		:	int									=   False
				for param in model.get_classifier().parameters():
								a_		:	Tuple									=   True

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

				# Instantiate optimizer
				a_		:	str									=   torch.optim.Adam(params=model.parameters()    ,		lr=lr / 25   )

				# Instantiate learning rate scheduler
				a_		:	Tuple									=   OneCycleLR(optimizer=__A    ,		max_lr=__A    ,		epochs=__A    ,		steps_per_epoch=len(__A   )   )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				a_					,      a_					,      a_					,      a_					,      a_		:	Tuple									=   accelerator.prepare(
				    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
				# We need to keep track of how many total steps we have iterated over
				a_		:	Dict									=   0
				# We also need to keep track of the starting epoch so files are named properly
				a_		:	Dict									=   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_		:	Optional[Any]									=   os.path.basename(args.resume_from_checkpoint   )
								else:
												# Get the most recent checkpoint
												a_		:	List[str]									=   [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
												dirs.sort(key=os.path.getctime   )
												a_		:	Tuple									=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								a_		:	List[str]									=   os.path.splitext(__A   )[0]

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

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

								a_		:	Any									=   accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'epoch {epoch}: {1_00 * eval_metric:.2f}'   )
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': 1_00 * eval_metric,
												        '''train_loss''': total_loss.item() / len(__A   ),
												        '''epoch''': epoch,
												    }    ,		step=__A    ,		)
								if checkpointing_steps == "epoch":
												a_		:	Dict									=   f'epoch_{epoch}'
												if args.output_dir is not None:
																a_		:	int									=   os.path.join(args.output_dir    ,		__A   )
												accelerator.save_state(__A   )

				if args.with_tracking:
								accelerator.end_training()





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   argparse.ArgumentParser(description='''Simple example of training script.'''   )
				parser.add_argument('''--data_dir'''    ,		required=__A    ,		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=__A    ,		default=__A    ,		choices=['''no''', '''fp16''', '''bf16''', '''fp8''']    ,		help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''    ,		)
				parser.add_argument('''--cpu'''    ,		action='''store_true'''    ,		help='''If passed, will train on the CPU.'''   )
				parser.add_argument(
				    '''--checkpointing_steps'''    ,		type=__A    ,		default=__A    ,		help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''    ,		)
				parser.add_argument(
				    '''--output_dir'''    ,		type=__A    ,		default='''.'''    ,		help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,		)
				parser.add_argument(
				    '''--resume_from_checkpoint'''    ,		type=__A    ,		default=__A    ,		help='''If the training should continue from a checkpoint folder.'''    ,		)
				parser.add_argument(
				    '''--with_tracking'''    ,		action='''store_true'''    ,		help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''    ,		)
				parser.add_argument(
				    '''--project_dir'''    ,		type=__A    ,		default='''logs'''    ,		help='''Location on where to store experiment tracking logs` and relevent project information'''    ,		)
				a_		:	Union[str, Any]									=   parser.parse_args()
				a_		:	List[Any]									=   {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
				training_function(__A    ,		__A   )


if __name__ == "__main__":
		main()




