





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


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      List[str]		,	UpperCAmelCase_       :      List[str]		,	UpperCAmelCase_       :      int=None		,	UpperCAmelCase_       :      List[Any]=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     file_names
							__UpperCAmelCase	:							str     =     image_transform
							__UpperCAmelCase	:							Union[str, Any]     =     label_to_id
					def __len__(   self       :      List[str]		):







							"""simple docstring"""
							return len(self.file_names		)






					def __getitem__(   self       :      Optional[int]		,	UpperCAmelCase_       :      Any		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     self.file_names[idx]
							__UpperCAmelCase	:							Tuple     =     PIL.Image.open(UpperCAmelCase_		)
							__UpperCAmelCase	:							str     =     raw_image.convert("RGB"		)
							if self.image_transform is not None:
									__UpperCAmelCase	:							str     =     self.image_transform(UpperCAmelCase_		)
							__UpperCAmelCase	:							Dict     =     extract_label(UpperCAmelCase_		)
							if self.label_to_id is not None:
									__UpperCAmelCase	:							Tuple     =     self.label_to_id[label]
							return {"image": image, "label": label}






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		# Initialize accelerator
		__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	:							Tuple     =     config["lr"]
		__UpperCAmelCase	:							Dict     =     int(config["num_epochs"]  )
		__UpperCAmelCase	:							str     =     int(config["seed"]  )
		__UpperCAmelCase	:							Any     =     int(config["batch_size"]  )
		__UpperCAmelCase	:							Optional[Any]     =     config["image_size"]
		if not isinstance(_UpperCAmelCase,       (list, tuple)  ):
				__UpperCAmelCase	:							int     =     (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	:							Optional[Any]     =     [extract_label(_UpperCAmelCase  ) for fname in file_names]
		__UpperCAmelCase	:							Any     =     list(set(_UpperCAmelCase  )  )
		id_to_label.sort()
		__UpperCAmelCase	:							int     =     {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	:							List[Any]     =     np.random.permutation(len(_UpperCAmelCase  )  )
		__UpperCAmelCase	:							Optional[int]     =     int(0.8 * len(_UpperCAmelCase  )  )
		__UpperCAmelCase	:							List[Any]     =     random_perm[:cut]
		__UpperCAmelCase	:							List[str]     =     random_perm[cut:]

		# For training we use a simple RandomResizedCrop
		__UpperCAmelCase	:							List[Any]     =     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	:							List[Any]     =     Compose([Resize(_UpperCAmelCase  ), ToTensor()]  )
		__UpperCAmelCase	:							List[Any]     =     PetsDataset([file_names[i] for i in eval_split],       image_transform=_UpperCAmelCase,       label_to_id=_UpperCAmelCase  )

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

		# Freezing the base model
		for param in model.parameters():
				__UpperCAmelCase	:							Dict     =     False
		for param in model.get_classifier().parameters():
				__UpperCAmelCase	:							List[Any]     =     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 / 25  )

		# Instantiate learning rate scheduler
		__UpperCAmelCase	:							Union[str, 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	:							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	:							Dict     =     (batch["image"] - mean) / std
						__UpperCAmelCase	:							List[str]     =     model(_UpperCAmelCase  )
						__UpperCAmelCase	:							Optional[int]     =     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	:							Dict     =     0
				for _, batch in enumerate(_UpperCAmelCase  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						__UpperCAmelCase	:							Optional[Any]     =     {k: v.to(accelerator.device  ) for k, v in batch.items()}
						__UpperCAmelCase	:							Tuple     =     (batch["image"] - mean) / std
						with torch.no_grad():
								__UpperCAmelCase	:							Union[str, Any]     =     model(_UpperCAmelCase  )
						__UpperCAmelCase	:							Dict     =     outputs.argmax(dim=-1  )
						__UpperCAmelCase  ,			__UpperCAmelCase	:							Dict     =     accelerator.gather_for_metrics((predictions, batch["label"])  )
						__UpperCAmelCase	:							List[Any]     =     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}"  )


def 						__UpperCamelCase						(			):
		__UpperCAmelCase	:							Optional[int]     =     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	:							str     =     parser.parse_args()
		__UpperCAmelCase	:							List[str]     =     {"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

lowerCAmelCase__      :   Tuple      	=			{1: 1}


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		__UpperCAmelCase	:							int     =     n // 2 if n % 2 == 0 else 3 * n + 1
		__UpperCAmelCase	:							str     =     collatz_sequence_length(_UpperCAmelCase  ) + 1
		__UpperCAmelCase	:							Union[str, Any]     =     sequence_length
		return sequence_length


def 						__UpperCamelCase						(			_UpperCAmelCase = 1000000  ):
		__UpperCAmelCase	:							Union[str, Any]     =     max((collatz_sequence_length(_UpperCAmelCase  ), i) for i in range(1,       _UpperCAmelCase  )  )
		return result[1]


if __name__ == "__main__":
			print(solution(int(input().strip())))




