

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_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    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 :		Union[str, Any]								=    file_names
			_UpperCamelCase :		Optional[Any]								=    image_transform
			_UpperCamelCase :		Union[str, Any]								=    label_to_id


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






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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str						)    ->					Tuple:




	'''simple docstring'''




	_UpperCamelCase :		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
	_UpperCamelCase :		str								=    config['lr']
	_UpperCamelCase :		str								=    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 :		str								=    (image_size, image_size)

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

	# Build the label correspondences
	_UpperCamelCase :		str								=    [extract_label(UpperCAmelCase_						) for fname in file_names]
	_UpperCamelCase :		Any								=    list(set(UpperCAmelCase_						)						)
	id_to_label.sort()
	_UpperCamelCase :		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 :		Union[str, Any]								=    np.random.permutation(len(UpperCAmelCase_						)						)
	_UpperCamelCase :		Any								=    int(0.8 * len(UpperCAmelCase_						)						)
	_UpperCamelCase :		Optional[int]								=    random_perm[:cut]
	_UpperCamelCase :		Dict								=    random_perm[cut:]

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

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

	# Instantiate dataloaders.
	_UpperCamelCase :		str								=    DataLoader(UpperCAmelCase_				,							shuffle=UpperCAmelCase_				,							batch_size=UpperCAmelCase_				,							num_workers=4						)
	_UpperCamelCase :		Optional[Any]								=    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 :		int								=    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 :		Any								=    False
	for param in model.get_classifier().parameters():
		_UpperCamelCase :		List[Any]								=    True

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

	# Instantiate optimizer
	_UpperCamelCase :		Any								=    torch.optim.Adam(params=model.parameters()				,							lr=lr / 2_5						)

	# Instantiate learning rate scheduler
	_UpperCamelCase :		List[str]								=    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 :		Any								=    accelerator.prepare(
	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)

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

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

		_UpperCamelCase :		Tuple								=    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}'''						)






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




	'''simple docstring'''




	_UpperCamelCase :		Optional[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(
	    '--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(
	    '--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('--cpu'				,							action='store_true'				,							help='If passed, will train on the CPU.'						)
	_UpperCamelCase :		int								=    parser.parse_args()
	_UpperCamelCase :		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()







from __future__ import annotations






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




	'''simple docstring'''




	_UpperCamelCase :		Any								=    [0] * max_one

	for i in range(0				,							UpperCAmelCase_						):
		_UpperCamelCase :		List[Any]								=    i

	for i in range(2				,							UpperCAmelCase_						):
		if totients[i] == i:
			for j in range(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						):
				totients[j] -= totients[j] // i

	return totients






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					bool:




	'''simple docstring'''




	return sorted(str(UpperCAmelCase_						)						) == sorted(str(UpperCAmelCase_						)						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int = 1_0_0_0_0_0_0_0						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		List[Any]								=    1  # i
	_UpperCamelCase :		List[str]								=    0  # φ(i)
	_UpperCamelCase :		str								=    get_totients(max_n + 1						)

	for i in range(2				,							max_n + 1						):
		_UpperCamelCase :		List[str]								=    totients[i]

		if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase_				,							UpperCAmelCase_						):
			_UpperCamelCase :		Optional[int]								=    i
			_UpperCamelCase :		Tuple								=    t

	return min_numerator


if __name__ == "__main__":
							print(f'{solution() = }')




