






class 							_UpperCamelCase			:


      def __init__(	self					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					n
           __lowerCAmelCase								=					[None] * self.n
           __lowerCAmelCase								=					0  # index of the first element
           __lowerCAmelCase								=					0
           __lowerCAmelCase								=					0


      def __len__(	self	)->	int:
           return self.size


      def  __UpperCAmelCase		(	self	)->	bool:
           return self.size == 0


      def  __UpperCAmelCase		(	self	)->	int:
           return False if self.is_empty() else self.array[self.front]


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Union[str, Any]:
           if self.size >= self.n:
                raise Exception("QUEUE IS FULL"	)

           __lowerCAmelCase								=					data
           __lowerCAmelCase								=					(self.rear + 1) % self.n
           self.size += 1
           return self


      def  __UpperCAmelCase		(	self	)->	List[str]:
           if self.size == 0:
                raise Exception("UNDERFLOW"	)

           __lowerCAmelCase								=					self.array[self.front]
           __lowerCAmelCase								=					None
           __lowerCAmelCase								=					(self.front + 1) % self.n
           self.size -= 1
           return temp














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



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase=None					,						__UpperCamelCase=None	)->	Dict:
           __lowerCAmelCase								=					file_names
           __lowerCAmelCase								=					image_transform
           __lowerCAmelCase								=					label_to_id


      def __len__(	self	)->	Optional[int]:
           return len(self.file_names	)


      def __getitem__(	self					,						__UpperCamelCase	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.file_names[idx]
           __lowerCAmelCase								=					PIL.Image.open(__UpperCamelCase	)
           __lowerCAmelCase								=					raw_image.convert("RGB"	)
           if self.image_transform is not None:
                __lowerCAmelCase								=					self.image_transform(__UpperCamelCase	)
           __lowerCAmelCase								=					extract_label(__UpperCamelCase	)
           if self.label_to_id is not None:
                __lowerCAmelCase								=					self.label_to_id[label]
           return {"image": image, "label": label}





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     # Initialize accelerator
     if args.with_tracking:
          __lowerCAmelCase								=					Accelerator(
              cpu=args.cpu					, mixed_precision=args.mixed_precision					, log_with="all"					, project_dir=args.project_dir       )
     else:
          __lowerCAmelCase								=					Accelerator(cpu=args.cpu					, mixed_precision=args.mixed_precision       )

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

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

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

     # Build the label correspondences
     __lowerCAmelCase								=					[extract_label(__snake_case       ) for fname in file_names]
     __lowerCAmelCase								=					list(set(__snake_case       )       )
     id_to_label.sort()
     __lowerCAmelCase								=					{lbl: i for i, lbl in enumerate(__snake_case       )}

     # Set the seed before splitting the data.
     np.random.seed(__snake_case       )
     torch.manual_seed(__snake_case       )
     torch.cuda.manual_seed_all(__snake_case       )

     # Split our filenames between train and validation
     __lowerCAmelCase								=					np.random.permutation(len(__snake_case       )       )
     __lowerCAmelCase								=					int(0.8 * len(__snake_case       )       )
     __lowerCAmelCase								=					random_perm[:cut]
     __lowerCAmelCase								=					random_perm[cut:]

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

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

     # Instantiate dataloaders.
     __lowerCAmelCase								=					DataLoader(__snake_case					, shuffle=__snake_case					, batch_size=__snake_case					, num_workers=4       )
     __lowerCAmelCase								=					DataLoader(__snake_case					, shuffle=__snake_case					, batch_size=__snake_case					, num_workers=4       )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __lowerCAmelCase								=					create_model("resnet50d"					, pretrained=__snake_case					, num_classes=len(__snake_case       )       )

     # 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								=					model.to(accelerator.device       )

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

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

     # Instantiate optimizer
     __lowerCAmelCase								=					torch.optim.Adam(params=model.parameters()					, lr=lr / 25       )

     # Instantiate learning rate scheduler
     __lowerCAmelCase								=					OneCycleLR(optimizer=__snake_case					, max_lr=__snake_case					, epochs=__snake_case					, steps_per_epoch=len(__snake_case       )       )

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

          if "epoch" in training_difference:
               __lowerCAmelCase								=					int(training_difference.replace("epoch_"					, ""       )       ) + 1
               __lowerCAmelCase								=					None
          else:
               __lowerCAmelCase								=					int(training_difference.replace("step_"					, ""       )       )
               __lowerCAmelCase								=					resume_step // len(__snake_case       )
               resume_step -= starting_epoch * len(__snake_case       )

    # Now we train the model
     for epoch in range(__snake_case					, __snake_case       ):
          model.train()
          if args.with_tracking:
               __lowerCAmelCase								=					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
               __lowerCAmelCase								=					accelerator.skip_first_batches(__snake_case					, __snake_case       )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               __lowerCAmelCase								=					train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __lowerCAmelCase								=					{k: v.to(accelerator.device       ) for k, v in batch.items()}
               __lowerCAmelCase								=					(batch["image"] - mean) / std
               __lowerCAmelCase								=					model(__snake_case       )
               __lowerCAmelCase								=					torch.nn.functional.cross_entropy(__snake_case					, batch["label"]       )
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__snake_case       )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(__snake_case					, __snake_case       ):
                    __lowerCAmelCase								=					F"""step_{overall_step}"""
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __lowerCAmelCase								=					os.path.join(args.output_dir					, __snake_case       )
                         accelerator.save_state(__snake_case       )
          model.eval()
          __lowerCAmelCase								=					0
          __lowerCAmelCase								=					0
          for step, batch in enumerate(__snake_case       ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __lowerCAmelCase								=					{k: v.to(accelerator.device       ) for k, v in batch.items()}
               __lowerCAmelCase								=					(batch["image"] - mean) / std
               with torch.no_grad():
                    __lowerCAmelCase								=					model(__snake_case       )
               __lowerCAmelCase								=					outputs.argmax(dim=-1       )
               __lowerCAmelCase			, __lowerCAmelCase								=					accelerator.gather_for_metrics((predictions, batch["label"])       )
               __lowerCAmelCase								=					predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          __lowerCAmelCase								=					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(__snake_case       ),
                       "epoch": epoch,
                   }					, step=__snake_case					, )
          if checkpointing_steps == "epoch":
               __lowerCAmelCase								=					F"""epoch_{epoch}"""
               if args.output_dir is not None:
                    __lowerCAmelCase								=					os.path.join(args.output_dir					, __snake_case       )
               accelerator.save_state(__snake_case       )

     if args.with_tracking:
          accelerator.end_training()





def       __lowerCAmelCase      (						):
     __lowerCAmelCase								=					argparse.ArgumentParser(description="Simple example of training script."       )
     parser.add_argument("--data_dir"					, required=__snake_case					, 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=__snake_case					, default=__snake_case					, 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=__snake_case					, default=__snake_case					, 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=__snake_case					, 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=__snake_case					, default=__snake_case					, 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=__snake_case					, default="logs"					, help="Location on where to store experiment tracking logs` and relevent project information"					, )
     __lowerCAmelCase								=					parser.parse_args()
     __lowerCAmelCase								=					{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
     training_function(__snake_case					, __snake_case       )


if __name__ == "__main__":
 main()






