



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

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None ):
       UpperCamelCase__      =			file_names
       UpperCamelCase__      =			image_transform
       UpperCamelCase__      =			label_to_id


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







   def __getitem__(  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.file_names[idx]
       UpperCamelCase__      =			PIL.Image.open(__UpperCamelCase )
       UpperCamelCase__      =			raw_image.convert("RGB" )
       if self.image_transform is not None:
           UpperCamelCase__      =			self.image_transform(__UpperCamelCase )
       UpperCamelCase__      =			extract_label(__UpperCamelCase )
       if self.label_to_id is not None:
           UpperCamelCase__      =			self.label_to_id[label]
       return {"image": image, "label": label}





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    # Initialize accelerator
    UpperCamelCase__      =			Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision	)

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    UpperCamelCase__      =			config["lr"]
    UpperCamelCase__      =			int(config["num_epochs"]	)
    UpperCamelCase__      =			int(config["seed"]	)
    UpperCamelCase__      =			int(config["batch_size"]	)
    UpperCamelCase__      =			config["image_size"]
    if not isinstance(lowerCAmelCase_					,  (list, tuple)	):
        UpperCamelCase__      =			(image_size, image_size)

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

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

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

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

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

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

    # Instantiate dataloaders.
    UpperCamelCase__      =			DataLoader(lowerCAmelCase_					,  shuffle=lowerCAmelCase_					,  batch_size=lowerCAmelCase_					,  num_workers=4	)
    UpperCamelCase__      =			DataLoader(lowerCAmelCase_					,  shuffle=lowerCAmelCase_					,  batch_size=lowerCAmelCase_					,  num_workers=4	)

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

    # 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__      =			model.to(accelerator.device	)

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

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

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

    # Instantiate learning rate scheduler
    UpperCamelCase__      =			OneCycleLR(optimizer=lowerCAmelCase_					,  max_lr=lowerCAmelCase_					,  epochs=lowerCAmelCase_					,  steps_per_epoch=len(lowerCAmelCase_	)	)

    # 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__      =			accelerator.prepare(
        lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	)

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

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

        UpperCamelCase__      =			accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}: {1_00 * eval_metric:.2f}'	)





def 						snake_case__ ():
    UpperCamelCase__      =			argparse.ArgumentParser(description="Simple example of training script."	)
    parser.add_argument("--data_dir"					,  required=lowerCAmelCase_					,  help="The data folder on disk."	)
    parser.add_argument(
        "--mixed_precision"					,  type=lowerCAmelCase_					,  default=lowerCAmelCase_					,  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=lowerCAmelCase_					,  default=lowerCAmelCase_					,  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__      =			parser.parse_args()
    UpperCamelCase__      =			{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
    training_function(lowerCAmelCase_					,  lowerCAmelCase_	)


if __name__ == "__main__":
      main()









'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_	):
    for i, _ in enumerate(lowerCAmelCase_	):
        if (i % 2 == 1) == (nums[i - 1] > nums[i]):
            UpperCamelCase__    ,  UpperCamelCase__      =			nums[i], nums[i - 1]

    return nums


if __name__ == "__main__":
      print('Enter the array elements:')
      lowercase__     :  Optional[int]    					=			list(map(int, input().split()))
      print('The unsorted array is:')
      print(array)
      print('Array after Wiggle sort:')
      print(wiggle_sort(array))




