


"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  list			)     ->						list:


    """simple docstring"""





    if len(lowercase			) <= 1:
        return lst

    snake_case   :				List[str]				 =   1

    while i < len(lowercase			):
        if lst[i - 1] <= lst[i]:
            i += 1
        else:
            snake_case ,snake_case   :				Union[str, Any]				 =   lst[i], lst[i - 1]
            i -= 1
            if i == 0:
                snake_case   :				Optional[int]				 =   1

    return lst


if __name__ == "__main__":
  __snake_case             =   input("""Enter numbers separated by a comma:\n""").strip()
  __snake_case             =   [int(item) for item in user_input.split(""",""")]
  print(gnome_sort(unsorted))









"""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						(						lowercase  :  int			)     ->						Tuple:


    """simple docstring"""





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







class _lowerCAmelCase    (				snake_case_     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=None						,     UpperCamelCase__=None						)     ->		Union[str, Any]:

       '''simple docstring'''




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







   def __len__(     self						)     ->		Tuple:

       '''simple docstring'''




       return len(self.file_names						)







   def __getitem__(     self						,     UpperCamelCase__						)     ->		int:

       '''simple docstring'''




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







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  List[Any]			)     ->						List[str]:


    """simple docstring"""





    if args.with_tracking:
        snake_case   :				List[str]				 =   Accelerator(
            cpu=args.cpu						,							mixed_precision=args.mixed_precision						,							log_with="all"						,							project_dir=args.project_dir			)
    else:
        snake_case   :				Optional[int]				 =   Accelerator(cpu=args.cpu						,							mixed_precision=args.mixed_precision			)

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

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

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

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

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

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

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

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

    # Instantiate dataloaders.
    snake_case   :				Optional[Any]				 =   DataLoader(lowercase						,							shuffle=lowercase						,							batch_size=lowercase						,							num_workers=4			)
    snake_case   :				Tuple				 =   DataLoader(lowercase						,							shuffle=lowercase						,							batch_size=lowercase						,							num_workers=4			)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case   :				Optional[int]				 =   create_model("resnet50d"						,							pretrained=lowercase						,							num_classes=len(lowercase			)			)

    # 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).
    snake_case   :				Any				 =   model.to(accelerator.device			)

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

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

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

    # Instantiate learning rate scheduler
    snake_case   :				Dict				 =   OneCycleLR(optimizer=lowercase						,							max_lr=lowercase						,							epochs=lowercase						,							steps_per_epoch=len(lowercase			)			)

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

        if "epoch" in training_difference:
            snake_case   :				Any				 =   int(training_difference.replace("epoch_"						,							""			)			) + 1
            snake_case   :				int				 =   None
        else:
            snake_case   :				Any				 =   int(training_difference.replace("step_"						,							""			)			)
            snake_case   :				Optional[int]				 =   resume_step // len(lowercase			)
            resume_step -= starting_epoch * len(lowercase			)

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

        snake_case   :				List[Any]				 =   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(lowercase			),
                    "epoch": epoch,
                }						,							step=lowercase						,							)
        if checkpointing_steps == "epoch":
            snake_case   :				Optional[Any]				 =   F'epoch_{epoch}'
            if args.output_dir is not None:
                snake_case   :				Union[str, Any]				 =   os.path.join(args.output_dir						,							lowercase			)
            accelerator.save_state(lowercase			)

    if args.with_tracking:
        accelerator.end_training()







def   __lowerCAmelCase						(						)     ->						str:


    """simple docstring"""





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


if __name__ == "__main__":
  main()





