




from typing import Any




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Optional[int]      ,   __lowerCamelCase       :  Any							)      ->  Optional[int]:
							a					=    data
							a					=    None




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  str							)      ->  Optional[int]:
							a					=    None





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							a					=    self.head
							while temp is not None:
										print(temp.data      ,   end=" "							)
										a					=    temp.next
							print()





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  Any							)      ->  Any:
							a					=    Node(__lowerCamelCase							)
							a					=    self.head
							a					=    new_node





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[int]							)      ->  Optional[Any]:
							if node_data_a == node_data_a:
										return
							else:
										a					=    self.head
										while node_a is not None and node_a.data != node_data_a:
													a					=    node_a.next

										a					=    self.head
										while node_a is not None and node_a.data != node_data_a:
													a					=    node_a.next

										if node_a is None or node_a is None:
													return

										a					,	a					=    node_a.data, node_a.data


if __name__ == "__main__":
		__lowerCAmelCase	:       Dict     =	LinkedList()
		for i in range(5, 0, -1):
				ll.push(i)

		ll.print_list()

		ll.swap_nodes(1, 4)
		print('After swapping')
		ll.print_list()












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     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    fname.split(os.path.sep     )[-1]
			return re.search(R"^(.*)_\d+\.jpg$",						A     ).groups()[0]




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  str      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  Union[str, Any]=None							)      ->  Tuple:
							a					=    file_names
							a					=    image_transform
							a					=    label_to_id





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





				def __getitem__(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  int:
							a					=    self.file_names[idx]
							a					=    PIL.Image.open(__lowerCamelCase							)
							a					=    raw_image.convert("RGB"							)
							if self.image_transform is not None:
										a					=    self.image_transform(__lowerCamelCase							)
							a					=    extract_label(__lowerCamelCase							)
							if self.label_to_id is not None:
										a					=    self.label_to_id[label]
							return {"image": image, "label": label}




def     __magic_name__       (				A	:      str,						A	:      int     ):






			'''simple docstring'''







			if args.with_tracking:
						a					=    Accelerator(
						    cpu=args.cpu,						mixed_precision=args.mixed_precision,						log_with="all",						project_dir=args.project_dir     )
			else:
						a					=    Accelerator(cpu=args.cpu,						mixed_precision=args.mixed_precision     )

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a					=    config["lr"]
			a					=    int(config["num_epochs"]     )
			a					=    int(config["seed"]     )
			a					=    int(config["batch_size"]     )
			a					=    config["image_size"]
			if not isinstance(A,						(list, tuple)     ):
						a					=    (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":
									a					=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									a					=    int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						a					=    None

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

			# Grab all the image filenames
			a					=    [os.path.join(args.data_dir,						A     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(".jpg"     )]

			# Build the label correspondences
			a					=    [extract_label(A     ) for fname in file_names]
			a					=    list(set(A     )     )
			id_to_label.sort()
			a					=    {lbl: i for i, lbl in enumerate(A     )}

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

			# Split our filenames between train and validation
			a					=    np.random.permutation(len(A     )     )
			a					=    int(0.8 * len(A     )     )
			a					=    random_perm[:cut]
			a					=    random_perm[cut:]

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

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

			# Instantiate dataloaders.
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a					=    create_model("resnet50d",						pretrained=A,						num_classes=len(A     )     )

			# 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).
			a					=    model.to(accelerator.device     )

			# Freezing the base model
			for param in model.parameters():
						a					=    False
			for param in model.get_classifier().parameters():
						a					=    True

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

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

			# Instantiate learning rate scheduler
			a					=    OneCycleLR(optimizer=A,						max_lr=A,						epochs=A,						steps_per_epoch=len(A     )     )

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

						if "epoch" in training_difference:
									a					=    int(training_difference.replace("epoch_",						""     )     ) + 1
									a					=    None
						else:
									a					=    int(training_difference.replace("step_",						""     )     )
									a					=    resume_step // len(A     )
									resume_step -= starting_epoch * len(A     )

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

						a					=    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(A     ),
									        "epoch": epoch,
									    },						step=A,						)
						if checkpointing_steps == "epoch":
									a					=    F"""epoch_{epoch}"""
									if args.output_dir is not None:
												a					=    os.path.join(args.output_dir,						A     )
									accelerator.save_state(A     )

			if args.with_tracking:
						accelerator.end_training()




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    argparse.ArgumentParser(description="Simple example of training script."     )
			parser.add_argument("--data_dir",						required=A,						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=A,						default=A,						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=A,						default=A,						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=A,						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=A,						default=A,						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=A,						default="logs",						help="Location on where to store experiment tracking logs` and relevent project information",						)
			a					=    parser.parse_args()
			a					=    {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
			training_function(A,						A     )


if __name__ == "__main__":
		main()






