

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_     (							UpperCAmelCase_							:     Any	)						->				Any:




	'''simple docstring'''




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







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case=None	,       __snake_case=None):
			_UpperCamelCase   :		List[str]			=						file_names
			_UpperCamelCase   :		Union[str, Any]			=						image_transform
			_UpperCamelCase   :		Tuple			=						label_to_id


		def __len__(				self):
			return len(self.file_names)






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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Optional[int]	)						->				Dict:




	'''simple docstring'''




	if args.with_tracking:
		_UpperCamelCase   :		str			=						Accelerator(
		    cpu=args.cpu							,       mixed_precision=args.mixed_precision							,       log_with='all'							,       project_dir=args.project_dir	)
	else:
		_UpperCamelCase   :		Union[str, Any]			=						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   :		List[Any]			=						int(config['num_epochs']	)
	_UpperCamelCase   :		Dict			=						int(config['seed']	)
	_UpperCamelCase   :		Union[str, Any]			=						int(config['batch_size']	)
	_UpperCamelCase   :		Optional[Any]			=						config['image_size']
	if not isinstance(UpperCAmelCase_							,       (list, tuple)	):
		_UpperCamelCase   :		List[str]			=						(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   :		Dict			=						args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			_UpperCamelCase   :		Dict			=						int(args.checkpointing_steps	)
		else:
			raise ValueError(
			    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
	else:
		_UpperCamelCase   :		Dict			=						None

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

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

	# Build the label correspondences
	_UpperCamelCase   :		Union[str, Any]			=						[extract_label(UpperCAmelCase_	) for fname in file_names]
	_UpperCamelCase   :		Union[str, Any]			=						list(set(UpperCAmelCase_	)	)
	id_to_label.sort()
	_UpperCamelCase   :		List[str]			=						{lbl: i for i, lbl in enumerate(UpperCAmelCase_	)}

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

	# Split our filenames between train and validation
	_UpperCamelCase   :		Optional[Any]			=						np.random.permutation(len(UpperCAmelCase_	)	)
	_UpperCamelCase   :		Union[str, Any]			=						int(0.8 * len(UpperCAmelCase_	)	)
	_UpperCamelCase   :		Optional[int]			=						random_perm[:cut]
	_UpperCamelCase   :		Tuple			=						random_perm[cut:]

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

	# For evaluation, we use a deterministic Resize
	_UpperCamelCase   :		Optional[int]			=						Compose([Resize(UpperCAmelCase_	), ToTensor()]	)
	_UpperCamelCase   :		Tuple			=						PetsDataset([file_names[i] for i in eval_split]							,       image_transform=UpperCAmelCase_							,       label_to_id=UpperCAmelCase_	)

	# Instantiate dataloaders.
	_UpperCamelCase   :		Optional[Any]			=						DataLoader(UpperCAmelCase_							,       shuffle=UpperCAmelCase_							,       batch_size=UpperCAmelCase_							,       num_workers=4	)
	_UpperCamelCase   :		int			=						DataLoader(UpperCAmelCase_							,       shuffle=UpperCAmelCase_							,       batch_size=UpperCAmelCase_							,       num_workers=4	)

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

	# 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   :		Any			=						model.to(accelerator.device	)

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

	# We normalize the batches of images to be a bit faster.
	_UpperCamelCase   :		int			=						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   :		Optional[int]			=						torch.optim.Adam(params=model.parameters()							,       lr=lr / 2_5	)

	# Instantiate learning rate scheduler
	_UpperCamelCase   :		List[Any]			=						OneCycleLR(optimizer=UpperCAmelCase_							,       max_lr=UpperCAmelCase_							,       epochs=UpperCAmelCase_							,       steps_per_epoch=len(UpperCAmelCase_	)	)

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

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

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

		_UpperCamelCase   :		Dict			=						accurate.item() / num_elems
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}: {1_0_0 * eval_metric:.2f}'''	)
		if args.with_tracking:
			accelerator.log(
			    {
			        'accuracy': 1_0_0 * eval_metric,
			        'train_loss': total_loss.item() / len(UpperCAmelCase_	),
			        'epoch': epoch,
			    }							,       step=UpperCAmelCase_							,       )
		if checkpointing_steps == "epoch":
			_UpperCamelCase   :		Any			=						F'''epoch_{epoch}'''
			if args.output_dir is not None:
				_UpperCamelCase   :		str			=						os.path.join(args.output_dir							,       UpperCAmelCase_	)
			accelerator.save_state(UpperCAmelCase_	)

	if args.with_tracking:
		accelerator.end_training()






def 					lowerCamelCase_     (							)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						argparse.ArgumentParser(description='Simple example of training script.'	)
	parser.add_argument('--data_dir'							,       required=UpperCAmelCase_							,       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=UpperCAmelCase_							,       default=UpperCAmelCase_							,       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=UpperCAmelCase_							,       default=UpperCAmelCase_							,       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=UpperCAmelCase_							,       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=UpperCAmelCase_							,       default=UpperCAmelCase_							,       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=UpperCAmelCase_							,       default='logs'							,       help='Location on where to store experiment tracking logs` and relevent project information'							,       )
	_UpperCamelCase   :		str			=						parser.parse_args()
	_UpperCamelCase   :		Optional[int]			=						{'lr': 3e-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
	training_function(UpperCAmelCase_							,       UpperCAmelCase_	)


if __name__ == "__main__":
							main()







import functools






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[int]							,       UpperCAmelCase_							:     list[int]	)						->				int:




	'''simple docstring'''




	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for day in days	):
		raise ValueError('The parameter days should be a list of integers'	)

	if len(UpperCAmelCase_	) != 3 or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for cost in costs	):
		raise ValueError('The parameter costs should be a list of three integers'	)

	if len(UpperCAmelCase_	) == 0:
		return 0

	if min(UpperCAmelCase_	) <= 0:
		raise ValueError('All days elements should be greater than 0'	)

	if max(UpperCAmelCase_	) >= 3_6_6:
		raise ValueError('All days elements should be less than 366'	)

	_UpperCamelCase   :		Union[str, Any]			=						set(UpperCAmelCase_	)

	@functools.cache
	def dynamic_programming(UpperCAmelCase_							:     int	) -> int:
		if index > 3_6_5:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1	)

		return min(
		    costs[0] + dynamic_programming(index + 1	)							,       costs[1] + dynamic_programming(index + 7	)							,       costs[2] + dynamic_programming(index + 3_0	)							,       )

	return dynamic_programming(1	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




