


'''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 lowerCamelCase    (    lowerCAmelCase       :			int	):





	"""simple docstring"""







	__magic_name__ :					Any  			=  fname.split(os.path.sep	)[-1]
	return re.search(R'^(.*)_\d+\.jpg$'	,	lowerCAmelCase	).groups()[0]
class _lowerCamelCase (		lowercase__							):



		'''simple docstring'''







		def __init__(      self	:		Tuple						,  _A	:		List[Any]						,  _A	:		Tuple=None						,  _A	:		Tuple=None    )						->				int:
			__magic_name__ :					Union[str, Any]  			=  file_names
			__magic_name__ :					Dict  			=  image_transform
			__magic_name__ :					int  			=  label_to_id







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







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







def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[Any]	):





	"""simple docstring"""







	__magic_name__ :					int  			=  Accelerator(cpu=args.cpu	,	mixed_precision=args.mixed_precision	)

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

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

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

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

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

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

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

	# Instantiate dataloaders.
	__magic_name__ :					Dict  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	,	num_workers=4	)
	__magic_name__ :					Optional[Any]  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	,	num_workers=4	)

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

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

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

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

	# Instantiate optimizer
	__magic_name__ :					Optional[Any]  			=  torch.optim.Adam(params=model.parameters()	,	lr=lr / 25	)

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

	# 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.
	__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					Tuple  			=  accelerator.prepare(
	    lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)

	# Now we train the model
	for epoch in range(lowerCAmelCase	):
		model.train()
		for step, batch in enumerate(lowerCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			__magic_name__ :					Optional[int]  			=  {k: v.to(accelerator.device	) for k, v in batch.items()}
			__magic_name__ :					Union[str, Any]  			=  (batch['image'] - mean) / std
			__magic_name__ :					Tuple  			=  model(lowerCAmelCase	)
			__magic_name__ :					List[str]  			=  torch.nn.functional.cross_entropy(lowerCAmelCase	,	batch['label']	)
			accelerator.backward(lowerCAmelCase	)
			optimizer.step()
			lr_scheduler.step()
			optimizer.zero_grad()

		model.eval()
		__magic_name__ :					Dict  			=  0
		__magic_name__ :					Optional[Any]  			=  0
		for _, batch in enumerate(lowerCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			__magic_name__ :					Union[str, Any]  			=  {k: v.to(accelerator.device	) for k, v in batch.items()}
			__magic_name__ :					Union[str, Any]  			=  (batch['image'] - mean) / std
			with torch.no_grad():
				__magic_name__ :					Optional[Any]  			=  model(lowerCAmelCase	)
			__magic_name__ :					List[str]  			=  outputs.argmax(dim=-1	)
			__magic_name__			,       __magic_name__ :					Union[str, Any]  			=  accelerator.gather_for_metrics((predictions, batch['label'])	)
			__magic_name__ :					Union[str, Any]  			=  predictions == references
			num_elems += accurate_preds.shape[0]
			accurate += accurate_preds.long().sum()

		__magic_name__ :					Tuple  			=  accurate.item() / num_elems
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}'	)





def lowerCamelCase    (    ):





	"""simple docstring"""







	__magic_name__ :					Tuple  			=  argparse.ArgumentParser(description='Simple example of training script.'	)
	parser.add_argument('--data_dir'	,	required=lowerCAmelCase	,	help='The data folder on disk.'	)
	parser.add_argument(
	    '--mixed_precision'	,	type=lowerCAmelCase	,	default=lowerCAmelCase	,	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(
	    '--checkpointing_steps'	,	type=lowerCAmelCase	,	default=lowerCAmelCase	,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	,	)
	parser.add_argument('--cpu'	,	action='store_true'	,	help='If passed, will train on the CPU.'	)
	__magic_name__ :					Optional[Any]  			=  parser.parse_args()
	__magic_name__ :					Optional[int]  			=  {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
	training_function(lowerCAmelCase	,	lowerCAmelCase	)


if __name__ == "__main__":
							main()



'''simple docstring'''

import math





def lowerCamelCase    (    lowerCAmelCase       :			int	):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase	,	lowerCAmelCase	):
		__magic_name__ :					str  			=  f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase	)
	if number < 1:
		__magic_name__ :					Tuple  			=  f'Input value of [number={number}] must be a positive integer'
		raise ValueError(lowerCAmelCase	)
	__magic_name__ :					Tuple  			=  [number]
	while number != 1:
		if number % 2 == 0:
			__magic_name__ :					Optional[int]  			=  math.floor(math.sqrt(lowerCAmelCase	)	)
		else:
			__magic_name__ :					Union[str, Any]  			=  math.floor(
			    math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	)	)
		sequence.append(lowerCAmelCase	)
	return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()