





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


class     __magic_name__     (							__SCREAMING_SNAKE_CASE							):





     """simple docstring"""



     def __init__(							self				:Dict	,					snake_case				:str	,					snake_case				:List[str]=None	,					snake_case				:List[Any]=None     ):





        '''simple docstring'''




        A_    : List[Any]								=	file_names
        A_    : Union[str, Any]								=	image_transform
        A_    : Optional[Any]								=	label_to_id



     def __len__(							self				:Dict     ):





        '''simple docstring'''




        return len(self.file_names     )





     def __getitem__(							self				:Any	,					snake_case				:Tuple     ):





        '''simple docstring'''




        A_    : Optional[Any]								=	self.file_names[idx]
        A_    : Optional[int]								=	PIL.Image.open(_a     )
        A_    : int								=	raw_image.convert("RGB"     )
        if self.image_transform is not None:
           A_    : Optional[Any]								=	self.image_transform(_a     )
        A_    : Any								=	extract_label(_a     )
        if self.label_to_id is not None:
           A_    : Tuple								=	self.label_to_id[label]
        return {"image": image, "label": label}




def    __snake_case  (    _lowerCAmelCase       :       Dict , _lowerCAmelCase       :       List[str]		)       ->  Tuple:
   # Initialize accelerator
   if args.with_tracking:
      A_    : Union[str, Any]								=	Accelerator(
          cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir		)
   else:
      A_    : Union[str, Any]								=	Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision		)

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

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

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

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

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

   # For evaluation, we use a deterministic Resize
   A_    : int								=	Compose([Resize(lowerCAmelCase__		), ToTensor()]		)
   A_    : Union[str, Any]								=	PetsDataset([file_names[i] for i in eval_split] , image_transform=lowerCAmelCase__ , label_to_id=lowerCAmelCase__		)

   # Instantiate dataloaders.
   A_    : Optional[Any]								=	DataLoader(lowerCAmelCase__ , shuffle=lowerCAmelCase__ , batch_size=lowerCAmelCase__ , num_workers=4		)
   A_    : List[str]								=	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_    : Tuple								=	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_    : Dict								=	model.to(accelerator.device		)

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

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

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

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

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

      A_    : 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":
         A_    : Optional[Any]								=	f"epoch_{epoch}"
         if args.output_dir is not None:
            A_    : int								=	os.path.join(args.output_dir , lowerCAmelCase__		)
         accelerator.save_state(lowerCAmelCase__		)

   if args.with_tracking:
      accelerator.end_training()

def    __snake_case  (    )       ->  int:
   A_    : str								=	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_    : str								=	parser.parse_args()
   A_    : str								=	{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
   training_function(lowerCAmelCase__ , lowerCAmelCase__		)


if __name__ == "__main__":
 main()




"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     list[str]			) ->      str:
		__a           =			''''''
		for word_or_phrase in separated:
				if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
						raise Exception('''join() accepts only strings to be joined'''			)
				joined += word_or_phrase + separator
		return joined.strip(lowerCAmelCase__			)


if __name__ == "__main__":
				from doctest import testmod

				testmod()






