






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




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a=None			,     a=None   )			->       Dict:
								snake_case_           =				file_names
								snake_case_           =				image_transform
								snake_case_           =				label_to_id




	def __len__(			self   )			->       str:
								return len(self.file_names   )






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






def 		__UpperCAmelCase     (	a_				,	a_):
							# Initialize accelerator
							if args.with_tracking:
														snake_case_           =				Accelerator(
														    cpu=args.cpu				,	mixed_precision=args.mixed_precision				,	log_with='all'				,	project_dir=args.project_dir)
							else:
														snake_case_           =				Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision)

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							snake_case_           =				config['lr']
							snake_case_           =				int(config['num_epochs'])
							snake_case_           =				int(config['seed'])
							snake_case_           =				int(config['batch_size'])
							snake_case_           =				config['image_size']
							if not isinstance(a_				,	(list, tuple)):
														snake_case_           =				(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":
																					snake_case_           =				args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					snake_case_           =				int(args.checkpointing_steps)
														else:
																					raise ValueError(
																					    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''')
							else:
														snake_case_           =				None

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

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

							# Build the label correspondences
							snake_case_           =				[extract_label(a_) for fname in file_names]
							snake_case_           =				list(set(a_))
							id_to_label.sort()
							snake_case_           =				{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
							snake_case_           =				np.random.permutation(len(a_))
							snake_case_           =				int(0.8 * len(a_))
							snake_case_           =				random_perm[:cut]
							snake_case_           =				random_perm[cut:]

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

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

							# Instantiate dataloaders.
							snake_case_           =				DataLoader(a_				,	shuffle=a_				,	batch_size=a_				,	num_workers=4)
							snake_case_           =				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)
							snake_case_           =				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).
							snake_case_           =				model.to(accelerator.device)

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

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

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

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

														if "epoch" in training_difference:
																					snake_case_           =				int(training_difference.replace('epoch_'				,	'')) + 1
																					snake_case_           =				None
														else:
																					snake_case_           =				int(training_difference.replace('step_'				,	''))
																					snake_case_           =				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:
																					snake_case_           =				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
																					snake_case_           =				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
																					snake_case_           =				train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					snake_case_           =				{k: v.to(accelerator.device) for k, v in batch.items()}
																					snake_case_           =				(batch['image'] - mean) / std
																					snake_case_           =				model(a_)
																					snake_case_           =				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_):
																												snake_case_           =				f'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										snake_case_           =				os.path.join(args.output_dir				,	a_)
																																			accelerator.save_state(a_)
														model.eval()
														snake_case_           =				0
														snake_case_           =				0
														for step, batch in enumerate(a_):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					snake_case_           =				{k: v.to(accelerator.device) for k, v in batch.items()}
																					snake_case_           =				(batch['image'] - mean) / std
																					with torch.no_grad():
																												snake_case_           =				model(a_)
																					snake_case_           =				outputs.argmax(dim=-1)
																					snake_case_     ,						snake_case_           =				accelerator.gather_for_metrics((predictions, batch['label']))
																					snake_case_           =				predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														snake_case_           =				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":
																					snake_case_           =				f'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												snake_case_           =				os.path.join(args.output_dir				,	a_)
																					accelerator.save_state(a_)

							if args.with_tracking:
														accelerator.end_training()





def 		__UpperCAmelCase     (	):
							snake_case_           =				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'				,	)
							snake_case_           =				parser.parse_args()
							snake_case_           =				{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
							training_function(a_				,	a_)


if __name__ == "__main__":
	main()













class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





