code: 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__  (		lowercase				):
      lowerCAmelCase_:       Union[str, Any]         =  fname.split(os.path.sep				)[-1]
      return re.search(r"^(.*)_\d+\.jpg$"       ,							lowercase				).groups()[0]
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=None					,	lowerCamelCase__=None   ):
          lowerCAmelCase_:       List[str]         =  file_names
          lowerCAmelCase_:       Dict         =  image_transform
          lowerCAmelCase_:       Optional[Any]         =  label_to_id
  
  
  
  
      def __len__(						self   ):
          return len(self.file_names   )
  
  
  
  
      def __getitem__(						self					,	lowerCamelCase__   ):
          lowerCAmelCase_:       Optional[int]         =  self.file_names[idx]
          lowerCAmelCase_:       Union[str, Any]         =  PIL.Image.open(lowerCamelCase__   )
          lowerCAmelCase_:       Optional[int]         =  raw_image.convert("RGB"   )
          if self.image_transform is not None:
              lowerCAmelCase_:       Tuple         =  self.image_transform(lowerCamelCase__   )
          lowerCAmelCase_:       List[Any]         =  extract_label(lowerCamelCase__   )
          if self.label_to_id is not None:
              lowerCAmelCase_:       str         =  self.label_to_id[label]
          return {"image": image, "label": label}
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase				):
      # Initialize accelerator
      lowerCAmelCase_:       Dict         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)
  
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase_:       str         =  config["lr"]
      lowerCAmelCase_:       Union[str, Any]         =  int(config["num_epochs"]				)
      lowerCAmelCase_:       Any         =  int(config["seed"]				)
      lowerCAmelCase_:       Dict         =  int(config["batch_size"]				)
      lowerCAmelCase_:       Any         =  config["image_size"]
      if not isinstance(lowercase       ,							(list, tuple)				):
          lowerCAmelCase_:       Tuple         =  (image_size, image_size)
  
      # Grab all the image filenames
      lowerCAmelCase_:       Tuple         =  [os.path.join(args.data_dir       ,							lowercase				) for fname in os.listdir(args.data_dir				) if fname.endswith(".jpg"				)]
  
      # Build the label correspondences
      lowerCAmelCase_:       List[str]         =  [extract_label(lowercase				) for fname in file_names]
      lowerCAmelCase_:       List[Any]         =  list(set(lowercase				)				)
      id_to_label.sort()
      lowerCAmelCase_:       Tuple         =  {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
      lowerCAmelCase_:       Union[str, Any]         =  np.random.permutation(len(lowercase				)				)
      lowerCAmelCase_:       int         =  int(0.8 * len(lowercase				)				)
      lowerCAmelCase_:       Tuple         =  random_perm[:cut]
      lowerCAmelCase_:       Any         =  random_perm[cut:]
  
      # For training we use a simple RandomResizedCrop
      lowerCAmelCase_:       Union[str, Any]         =  Compose([RandomResizedCrop(lowercase       ,							scale=(0.5, 1.0)				), ToTensor()]				)
      lowerCAmelCase_:       Optional[int]         =  PetsDataset(
          [file_names[i] for i in train_split]       ,							image_transform=lowercase       ,							label_to_id=lowercase				)
  
      # For evaluation, we use a deterministic Resize
      lowerCAmelCase_:       Union[str, Any]         =  Compose([Resize(lowercase				), ToTensor()]				)
      lowerCAmelCase_:       Dict         =  PetsDataset([file_names[i] for i in eval_split]       ,							image_transform=lowercase       ,							label_to_id=lowercase				)
  
      # Instantiate dataloaders.
      lowerCAmelCase_:       List[Any]         =  DataLoader(lowercase       ,							shuffle=lowercase       ,							batch_size=lowercase       ,							num_workers=4				)
      lowerCAmelCase_:       Dict         =  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)
      lowerCAmelCase_:       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).
      lowerCAmelCase_:       Union[str, Any]         =  model.to(accelerator.device				)
  
      # Freezing the base model
      for param in model.parameters():
          lowerCAmelCase_:       Optional[Any]         =  False
      for param in model.get_classifier().parameters():
          lowerCAmelCase_:       Optional[Any]         =  True
  
      # We normalize the batches of images to be a bit faster.
      lowerCAmelCase_:       Dict         =  torch.tensor(model.default_cfg["mean"]				)[None, :, None, None].to(accelerator.device				)
      lowerCAmelCase_:       List[str]         =  torch.tensor(model.default_cfg["std"]				)[None, :, None, None].to(accelerator.device				)
  
      # Instantiate optimizer
      lowerCAmelCase_:       Any         =  torch.optim.Adam(params=model.parameters()       ,							lr=lr / 25				)
  
      # Instantiate learning rate scheduler
      lowerCAmelCase_:       List[Any]         =  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.
      lowerCAmelCase_:       Dict         =  accelerator.prepare(
          lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase				)
  
      # Now we train the model
      for epoch in range(lowercase				):
          model.train()
          for step, batch in enumerate(lowercase				):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              lowerCAmelCase_:       str         =  {k: v.to(accelerator.device				) for k, v in batch.items()}
              lowerCAmelCase_:       int         =  (batch["image"] - mean) / std
              lowerCAmelCase_:       str         =  model(lowercase				)
              lowerCAmelCase_:       int         =  torch.nn.functional.cross_entropy(lowercase       ,							batch["label"]				)
              accelerator.backward(lowercase				)
              optimizer.step()
              lr_scheduler.step()
              optimizer.zero_grad()
  
          model.eval()
          lowerCAmelCase_:       Tuple         =  0
          lowerCAmelCase_:       Optional[int]         =  0
          for _, batch in enumerate(lowercase				):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              lowerCAmelCase_:       List[str]         =  {k: v.to(accelerator.device				) for k, v in batch.items()}
              lowerCAmelCase_:       Any         =  (batch["image"] - mean) / std
              with torch.no_grad():
                  lowerCAmelCase_:       Optional[int]         =  model(lowercase				)
              lowerCAmelCase_:       List[str]         =  outputs.argmax(dim=-1				)
              lowerCAmelCase_:       str         =  accelerator.gather_for_metrics((predictions, batch["label"])				)
              lowerCAmelCase_:       List[str]         =  predictions == references
              num_elems += accurate_preds.shape[0]
              accurate += accurate_preds.long().sum()
  
          lowerCAmelCase_:       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 		snake_case__  (		):
      lowerCAmelCase_:       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(
          "--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(
          "--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("--cpu"       ,							action="store_true"       ,							help="If passed, will train on the CPU."				)
      lowerCAmelCase_:       Optional[int]         =  parser.parse_args()
      lowerCAmelCase_:       Union[str, Any]         =  {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(lowercase       ,							lowercase				)
  
  
  if __name__ == "__main__":
   main()
style_context: def 		snake_case__  (		lowercase = 998001				):
  
     # fetches the next number
     for number in range(n - 1       ,							9999       ,							-1				):
        lowerCAmelCase_:       str         =  str(lowercase				)
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           lowerCAmelCase_:       List[str]         =  999
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
                 return number
              divisor -= 1
     raise ValueError("That number is larger than our acceptable range."				)
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')