

'''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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = fname.split(os.path.sep			)[-1]
       return re.search(r"""^(.*)_\d+\.jpg$""",    __lowerCAmelCase			).groups()[0]


class    A__ ( lowerCamelCase__     ):



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__=None						,			UpperCamelCase__=None    )  -> int:





           '''simple docstring'''
           A_		   = file_names
           A_		   = image_transform
           A_		   = label_to_id



    def __len__(   self    )  -> Optional[int]:





           '''simple docstring'''
           return len(self.file_names    )







    def __getitem__(   self						,			UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.file_names[idx]
           A_		   = PIL.Image.open(snake_case__    )
           A_		   = raw_image.convert("""RGB"""    )
           if self.image_transform is not None:
                  A_		   = self.image_transform(snake_case__    )
           A_		   = extract_label(snake_case__    )
           if self.label_to_id is not None:
                  A_		   = self.label_to_id[label]
           return {"image": image, "label": label}

def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[Any]:





       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(__lowerCAmelCase,    (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(__lowerCAmelCase			)[-1].split("""."""			)[0]
              accelerator.init_trackers(__lowerCAmelCase,    __lowerCAmelCase			)

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

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

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

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

       # Instantiate dataloaders.
       A_		   = DataLoader(__lowerCAmelCase,    shuffle=__lowerCAmelCase,    batch_size=__lowerCAmelCase,    num_workers=4			)
       A_		   = 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)
       A_		   = 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).
       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=__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.
       A_		   = accelerator.prepare(
           __lowerCAmelCase,    __lowerCAmelCase,    __lowerCAmelCase,    __lowerCAmelCase,    __lowerCAmelCase			)
       # 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(__lowerCAmelCase			)[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(__lowerCAmelCase			)
                     resume_step -= starting_epoch * len(__lowerCAmelCase			)

    # Now we train the model
       for epoch in range(__lowerCAmelCase,    __lowerCAmelCase			):
              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(__lowerCAmelCase,    __lowerCAmelCase			)
                     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(__lowerCAmelCase			)
                     A_		   = torch.nn.functional.cross_entropy(__lowerCAmelCase,    batch["""label"""]			)
                     # We keep track of the loss at each epoch
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     accelerator.backward(__lowerCAmelCase			)
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()
                     overall_step += 1
                     if isinstance(__lowerCAmelCase,    __lowerCAmelCase			):
                            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,    __lowerCAmelCase			)
                                   accelerator.save_state(__lowerCAmelCase			)
              model.eval()
              A_		   = 0
              A_		   = 0
              for step, batch in enumerate(__lowerCAmelCase			):
                     # 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(__lowerCAmelCase			)
                     A_		   = outputs.argmax(dim=-1			)
                     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}: {1_00 * eval_metric:.2f}'''			)
              if args.with_tracking:
                     accelerator.log(
                         {
                             """accuracy""": 1_00 * eval_metric,
                             """train_loss""": total_loss.item() / len(__lowerCAmelCase			),
                             """epoch""": epoch,
                         },    step=__lowerCAmelCase,    )
              if checkpointing_steps == "epoch":
                     A_		   = F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            A_		   = os.path.join(args.output_dir,    __lowerCAmelCase			)
                     accelerator.save_state(__lowerCAmelCase			)

       if args.with_tracking:
              accelerator.end_training()
def  UpperCAmelCase__      ( )     ->     Dict:
       A_		   = argparse.ArgumentParser(description="""Simple example of training script."""			)
       parser.add_argument("""--data_dir""",    required=__lowerCAmelCase,    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=__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("""--cpu""",    action="""store_true""",    help="""If passed, will train on the CPU."""			)
       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(
           """--output_dir""",    type=__lowerCAmelCase,    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=__lowerCAmelCase,    default=__lowerCAmelCase,    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=__lowerCAmelCase,    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''': 2_24}
       training_function(__lowerCAmelCase,    __lowerCAmelCase			)


if __name__ == "__main__":
      main()








'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[False] * (required_sum + 1) for _ in range(arr_len + 1			)]

       # for each arr value, a sum of zero(0) can be formed by not taking any element
       # hence True/1
       for i in range(arr_len + 1			):
              A_		   = True

       # sum is not zero and set is empty then false
       for i in range(1,    required_sum + 1			):
              A_		   = False

       for i in range(1,    arr_len + 1			):
              for j in range(1,    required_sum + 1			):
                     if arr[i - 1] > j:
                            A_		   = subset[i - 1][j]
                     if arr[i - 1] <= j:
                            A_		   = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

       return subset[arr_len][required_sum]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
