




'''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 			_a			(		_lowercase      :			str						):





     '''simple docstring'''






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



class 			a       (	__UpperCAmelCase ):


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Tuple=None							,				snake_case  :	str=None    )		->							Optional[Any]:
          __UpperCAmelCase			:				Dict													=    file_names
          __UpperCAmelCase			:				str													=    image_transform
          __UpperCAmelCase			:				str													=    label_to_id

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






     def __getitem__(       self  :	Union[str, Any]							,				snake_case  :	Optional[Any]    )		->							Any:
          __UpperCAmelCase			:				Union[str, Any]													=    self.file_names[idx]
          __UpperCAmelCase			:				int													=    PIL.Image.open(_lowerCamelCase    )
          __UpperCAmelCase			:				Dict													=    raw_image.convert('''RGB'''    )
          if self.image_transform is not None:
               __UpperCAmelCase			:				Optional[Any]													=    self.image_transform(_lowerCamelCase    )
          __UpperCAmelCase			:				List[Any]													=    extract_label(_lowerCamelCase    )
          if self.label_to_id is not None:
               __UpperCAmelCase			:				int													=    self.label_to_id[label]
          return {"image": image, "label": label}




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			Tuple						):





     '''simple docstring'''






     if args.with_tracking:
          __UpperCAmelCase			:				Dict													=    Accelerator(
              cpu=args.cpu    ,      mixed_precision=args.mixed_precision    ,      log_with='''all'''    ,      project_dir=args.project_dir						)
     else:
          __UpperCAmelCase			:				Dict													=    Accelerator(cpu=args.cpu    ,      mixed_precision=args.mixed_precision						)

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

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

     # Grab all the image filenames
     __UpperCAmelCase			:				Dict													=    [os.path.join(args.data_dir    ,      _lowerCAmelCase						) for fname in os.listdir(args.data_dir						) if fname.endswith('''.jpg'''						)]

     # Build the label correspondences
     __UpperCAmelCase			:				Any													=    [extract_label(_lowerCAmelCase						) for fname in file_names]
     __UpperCAmelCase			:				str													=    list(set(_lowerCAmelCase						)						)
     id_to_label.sort()
     __UpperCAmelCase			:				Union[str, Any]													=    {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			:				Dict													=    np.random.permutation(len(_lowerCAmelCase						)						)
     __UpperCAmelCase			:				str													=    int(0.8 * len(_lowerCAmelCase						)						)
     __UpperCAmelCase			:				List[str]													=    random_perm[:cut]
     __UpperCAmelCase			:				Any													=    random_perm[cut:]

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

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

     # Instantiate dataloaders.
     __UpperCAmelCase			:				Tuple													=    DataLoader(_lowerCAmelCase    ,      shuffle=_lowerCAmelCase    ,      batch_size=_lowerCAmelCase    ,      num_workers=4						)
     __UpperCAmelCase			:				Any													=    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			:				Optional[int]													=    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			:				Dict													=    model.to(accelerator.device						)

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

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

     # Instantiate optimizer
     __UpperCAmelCase			:				Optional[Any]													=    torch.optim.Adam(params=model.parameters()    ,      lr=lr / 25						)

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

          if "epoch" in training_difference:
               __UpperCAmelCase			:				Tuple													=    int(training_difference.replace('''epoch_'''    ,      ''''''						)						) + 1
               __UpperCAmelCase			:				int													=    None
          else:
               __UpperCAmelCase			:				Tuple													=    int(training_difference.replace('''step_'''    ,      ''''''						)						)
               __UpperCAmelCase			:				List[str]													=    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:
               __UpperCAmelCase			:				str													=    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			:				Optional[int]													=    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
               __UpperCAmelCase			:				List[Any]													=    train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __UpperCAmelCase			:				int													=    {k: v.to(accelerator.device						) for k, v in batch.items()}
               __UpperCAmelCase			:				List[Any]													=    (batch['''image'''] - mean) / std
               __UpperCAmelCase			:				Optional[int]													=    model(_lowerCAmelCase						)
               __UpperCAmelCase			:				List[str]													=    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						):
                    __UpperCAmelCase			:				Optional[Any]													=    F'step_{overall_step}'
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __UpperCAmelCase			:				int													=    os.path.join(args.output_dir    ,      _lowerCAmelCase						)
                         accelerator.save_state(_lowerCAmelCase						)
          model.eval()
          __UpperCAmelCase			:				List[Any]													=    0
          __UpperCAmelCase			:				List[Any]													=    0
          for step, batch in enumerate(_lowerCAmelCase						):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __UpperCAmelCase			:				int													=    {k: v.to(accelerator.device						) for k, v in batch.items()}
               __UpperCAmelCase			:				List[Any]													=    (batch['''image'''] - mean) / std
               with torch.no_grad():
                    __UpperCAmelCase			:				Any													=    model(_lowerCAmelCase						)
               __UpperCAmelCase			:				List[str]													=    outputs.argmax(dim=-1						)
               __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    accelerator.gather_for_metrics((predictions, batch['''label'''])						)
               __UpperCAmelCase			:				Union[str, Any]													=    predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          __UpperCAmelCase			:				Dict													=    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(_lowerCAmelCase						),
                       '''epoch''': epoch,
                   }    ,      step=_lowerCAmelCase    ,      )
          if checkpointing_steps == "epoch":
               __UpperCAmelCase			:				str													=    F'epoch_{epoch}'
               if args.output_dir is not None:
                    __UpperCAmelCase			:				List[str]													=    os.path.join(args.output_dir    ,      _lowerCAmelCase						)
               accelerator.save_state(_lowerCAmelCase						)

     if args.with_tracking:
          accelerator.end_training()




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    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'''    ,      )
     __UpperCAmelCase			:				int													=    parser.parse_args()
     __UpperCAmelCase			:				Tuple													=    {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
     training_function(_lowerCAmelCase    ,      _lowerCAmelCase						)


if __name__ == "__main__":
      main()



'''simple docstring'''



from __future__ import annotations




def 			_a			(		_lowercase      :			list						):





     '''simple docstring'''






     if not nums:
          raise ValueError('''List is empty'''						)
     return sum(_lowercase						) / len(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()