

import math
def      _lowerCAmelCase(	__A			):
  return math.sqrt(__A			) * math.sqrt(__A			) == num
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  n
  while left <= right:
    UpperCAmelCase					      =  (left + right) // 2
    if mid**2 == n:
      return True
    elif mid**2 > n:
      UpperCAmelCase					      =  mid - 1
    else:
      UpperCAmelCase					      =  mid + 1
  return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()






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






class 						__magic_name__    (       _snake_case    ):


 def __init__(						self		: Any			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Union[str, Any]=None			,						lowerCAmelCase__		: int=None    )    ->      Optional[Any]:
   UpperCAmelCase					      =  file_names
   UpperCAmelCase					      =  image_transform
   UpperCAmelCase					      =  label_to_id


 def __len__(						self		: Tuple    )    ->      List[str]:
   return len(self.file_names    )


 def __getitem__(						self		: Optional[int]			,						lowerCAmelCase__		: Tuple    )    ->      Dict:
   UpperCAmelCase					      =  self.file_names[idx]
   UpperCAmelCase					      =  PIL.Image.open(lowerCAmelCase__    )
   UpperCAmelCase					      =  raw_image.convert("RGB"    )
   if self.image_transform is not None:
     UpperCAmelCase					      =  self.image_transform(lowerCAmelCase__    )
   UpperCAmelCase					      =  extract_label(lowerCAmelCase__    )
   if self.label_to_id is not None:
     UpperCAmelCase					      =  self.label_to_id[label]
   return {"image": image, "label": label}

def      _lowerCAmelCase(	__A   ,       __A			):
  # Initialize accelerator
  if args.with_tracking:
    UpperCAmelCase					      =  Accelerator(
        cpu=args.cpu   ,       mixed_precision=args.mixed_precision   ,       log_with="all"   ,       project_dir=args.project_dir			)
  else:
    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(__A   ,       (list, tuple)			):
    UpperCAmelCase					      =  (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":
      UpperCAmelCase					      =  args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      UpperCAmelCase					      =  int(args.checkpointing_steps			)
    else:
      raise ValueError(
          F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."			)
  else:
    UpperCAmelCase					      =  None

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

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

  # Build the label correspondences
  UpperCAmelCase					      =  [extract_label(__A			) for fname in file_names]
  UpperCAmelCase					      =  list(set(__A			)			)
  id_to_label.sort()
  UpperCAmelCase					      =  {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
  UpperCAmelCase					      =  np.random.permutation(len(__A			)			)
  UpperCAmelCase					      =  int(0.8 * len(__A			)			)
  UpperCAmelCase					      =  random_perm[:cut]
  UpperCAmelCase					      =  random_perm[cut:]

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

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

  # Instantiate dataloaders.
  UpperCAmelCase					      =  DataLoader(__A   ,       shuffle=__A   ,       batch_size=__A   ,       num_workers=4			)
  UpperCAmelCase					      =  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)
  UpperCAmelCase					      =  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).
  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=__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.
  UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.prepare(
      __A   ,       __A   ,       __A   ,       __A   ,       __A			)
  # We need to keep track of how many total steps we have iterated over
  UpperCAmelCase					      =  0
  # We also need to keep track of the starting epoch so files are named properly
  UpperCAmelCase					      =  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			)
      UpperCAmelCase					      =  os.path.basename(args.resume_from_checkpoint			)
    else:
      # Get the most recent checkpoint
      UpperCAmelCase					      =  [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
      dirs.sort(key=os.path.getctime			)
      UpperCAmelCase					      =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    UpperCAmelCase					      =  os.path.splitext(__A			)[0]

    if "epoch" in training_difference:
      UpperCAmelCase					      =  int(training_difference.replace("epoch_"   ,       ""			)			) + 1
      UpperCAmelCase					      =  None
    else:
      UpperCAmelCase					      =  int(training_difference.replace("step_"   ,       ""			)			)
      UpperCAmelCase					      =  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:
      UpperCAmelCase					      =  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
      UpperCAmelCase					      =  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
      UpperCAmelCase					      =  train_dataloader
    for batch in active_dataloader:
      # 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(__A			)
      UpperCAmelCase					      =  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			):
        UpperCAmelCase					      =  F"step_{overall_step}"
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            UpperCAmelCase					      =  os.path.join(args.output_dir   ,       __A			)
          accelerator.save_state(__A			)
    model.eval()
    UpperCAmelCase					      =  0
    UpperCAmelCase					      =  0
    for step, batch in enumerate(__A			):
      # 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(__A			)
      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}: {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":
      UpperCAmelCase					      =  F"epoch_{epoch}"
      if args.output_dir is not None:
        UpperCAmelCase					      =  os.path.join(args.output_dir   ,       __A			)
      accelerator.save_state(__A			)

  if args.with_tracking:
    accelerator.end_training()
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  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"   ,       )
  UpperCAmelCase					      =  parser.parse_args()
  UpperCAmelCase					      =  {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(__A   ,       __A			)


if __name__ == "__main__":
      main()



