


'''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       a				(      __a       )  ->     Optional[Any]:



					'''simple docstring'''






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

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			file_names
							UpperCamelCase__       :str							 =			image_transform
							UpperCamelCase__       :Union[str, Any]							 =			label_to_id




		def __len__(		self							):

							'''simple docstring'''


							return len(self.file_names							)




		def __getitem__(		self   ,       UpperCamelCase_							):

							'''simple docstring'''


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



def       a				(      __a			, __a       )  ->     int:



					'''simple docstring'''






					if args.with_tracking:
										UpperCamelCase__       :int							 =			Accelerator(
										    cpu=args.cpu			, mixed_precision=args.mixed_precision			, log_with='''all'''			, project_dir=args.project_dir       )
					else:
										UpperCamelCase__       :Dict							 =			Accelerator(cpu=args.cpu			, mixed_precision=args.mixed_precision       )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase__       :Tuple							 =			config['lr']
					UpperCamelCase__       :int							 =			int(config['''num_epochs''']       )
					UpperCamelCase__       :Union[str, Any]							 =			int(config['''seed''']       )
					UpperCamelCase__       :Optional[int]							 =			int(config['''batch_size''']       )
					UpperCamelCase__       :Dict							 =			config['image_size']
					if not isinstance(_UpperCAmelCase			, (list, tuple)       ):
										UpperCamelCase__       :Optional[int]							 =			(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__       :Optional[Any]							 =			args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															UpperCamelCase__       :List[Any]							 =			int(args.checkpointing_steps       )
										else:
															raise ValueError(
															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
					else:
										UpperCamelCase__       :Union[str, Any]							 =			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__       :str							 =			[os.path.join(args.data_dir			, _UpperCAmelCase       ) for fname in os.listdir(args.data_dir       ) if fname.endswith('''.jpg'''       )]

					# Build the label correspondences
					UpperCamelCase__       :Optional[int]							 =			[extract_label(_UpperCAmelCase       ) for fname in file_names]
					UpperCamelCase__       :Dict							 =			list(set(_UpperCAmelCase       )       )
					id_to_label.sort()
					UpperCamelCase__       :Tuple							 =			{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__       :int							 =			int(0.8 * len(_UpperCAmelCase       )       )
					UpperCamelCase__       :str							 =			random_perm[:cut]
					UpperCamelCase__       :Optional[int]							 =			random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					UpperCamelCase__       :List[Any]							 =			Compose([RandomResizedCrop(_UpperCAmelCase			, scale=(0.5, 1.0)       ), ToTensor()]       )
					UpperCamelCase__       :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__       :Any							 =			Compose([Resize(_UpperCAmelCase       ), ToTensor()]       )
					UpperCamelCase__       :Union[str, Any]							 =			PetsDataset([file_names[i] for i in eval_split]			, image_transform=_UpperCAmelCase			, label_to_id=_UpperCAmelCase       )

					# Instantiate dataloaders.
					UpperCamelCase__       :List[Any]							 =			DataLoader(_UpperCAmelCase			, shuffle=_UpperCAmelCase			, batch_size=_UpperCAmelCase			, num_workers=4       )
					UpperCamelCase__       :Optional[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__       :Any							 =			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__       :Dict							 =			model.to(accelerator.device       )

					# Freezing the base model
					for param in model.parameters():
										UpperCamelCase__       :Dict							 =			False
					for param in model.get_classifier().parameters():
										UpperCamelCase__       :Union[str, Any]							 =			True

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

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

					# Instantiate learning rate scheduler
					UpperCamelCase__       :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__       :Optional[int]							 =			accelerator.prepare(
					    _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase       )
					# We need to keep track of how many total steps we have iterated over
					UpperCamelCase__       :Optional[Any]							 =			0
					# We also need to keep track of the starting epoch so files are named properly
					UpperCamelCase__       :int							 =			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__       :Optional[int]							 =			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__       :Dict							 =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										UpperCamelCase__       :Optional[Any]							 =			os.path.splitext(_UpperCAmelCase       )[0]

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

										UpperCamelCase__       :Optional[int]							 =			accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''       )
										if args.with_tracking:
															accelerator.log(
															    {
															        '''accuracy''': 100 * eval_metric,
															        '''train_loss''': total_loss.item() / len(_UpperCAmelCase       ),
															        '''epoch''': epoch,
															    }			, step=_UpperCAmelCase			, )
										if checkpointing_steps == "epoch":
															UpperCamelCase__       :int							 =			f'''epoch_{epoch}'''
															if args.output_dir is not None:
																				UpperCamelCase__       :Dict							 =			os.path.join(args.output_dir			, _UpperCAmelCase       )
															accelerator.save_state(_UpperCAmelCase       )

					if args.with_tracking:
										accelerator.end_training()


def       a				(      )  ->     Dict:



					'''simple docstring'''






					UpperCamelCase__       :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__       :Optional[Any]							 =			parser.parse_args()
					UpperCamelCase__       :Tuple							 =			{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
					training_function(_UpperCAmelCase			, _UpperCAmelCase       )


if __name__ == "__main__":
		main()









'''simple docstring'''
from __future__ import annotations

__snake_case											=					[True] * 1000001
__snake_case											=					2
while i * i <= 1000000:
		if seive[i]:
				for j in range(i * i, 1000001, i):
						__snake_case											=					False
		i += 1


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					return seive[n]


def       a				(      __a       )  ->     bool:



					'''simple docstring'''






					return any(digit in '''02468''' for digit in str(__a       )       )


def       a				(      __a = 1000000       )  ->     list[int]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			[2]  # result already includes the number 2.
					for num in range(3			, limit + 1			, 2       ):
										if is_prime(__a       ) and not contains_an_even_digit(__a       ):
															UpperCamelCase__       :str							 =			str(__a       )
															UpperCamelCase__       :List[str]							 =			[int(str_num[j:] + str_num[:j]       ) for j in range(len(__a       )       )]
															if all(is_prime(__a       ) for i in list_nums       ):
																				result.append(__a       )
					return result


def       a				(      )  ->     int:



					'''simple docstring'''






					return len(find_circular_primes()       )


if __name__ == "__main__":
		print(F"""{len(find_circular_primes()) = }""")