



"""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__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							fname.split(os.path.sep   )[-1]
      return re.search(r"^(.*)_\d+\.jpg$"			,    lowerCAmelCase__   ).groups()[0]
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						Tuple	,       _UpperCAmelCase    :						str=None	,       _UpperCAmelCase    :						int=None					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							file_names
             UpperCAmelCase_    				=							image_transform
             UpperCAmelCase_    				=							label_to_id



       def __len__(							self    :						Any					)				->		int:


             '''simple docstring'''




             return len(self.file_names					)







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


             '''simple docstring'''




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



def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # 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(lowerCAmelCase__			,    (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(lowerCAmelCase__   )[-1].split("."   )[0]
            accelerator.init_trackers(lowerCAmelCase__			,    lowerCAmelCase__   )

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

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

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

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

      # Instantiate dataloaders.
      UpperCAmelCase_    				=							DataLoader(lowerCAmelCase__			,    shuffle=lowerCAmelCase__			,    batch_size=lowerCAmelCase__			,    num_workers=4   )
      UpperCAmelCase_    				=							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_    				=							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_    				=							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=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_    				=							accelerator.prepare(
          lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      # 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(lowerCAmelCase__   )[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(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_    				=							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(lowerCAmelCase__			,    lowerCAmelCase__   )
                  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(lowerCAmelCase__   )
                  UpperCAmelCase_    				=							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_    				=							f"""step_{overall_step}"""
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    UpperCAmelCase_    				=							os.path.join(args.output_dir			,    lowerCAmelCase__   )
                              accelerator.save_state(lowerCAmelCase__   )
            model.eval()
            UpperCAmelCase_    				=							0
            UpperCAmelCase_    				=							0
            for step, batch in enumerate(lowerCAmelCase__   ):
                  # 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(lowerCAmelCase__   )
                  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(lowerCAmelCase__   ),
                          "epoch": epoch,
                      }			,    step=lowerCAmelCase__			,    )
            if checkpointing_steps == "epoch":
                  UpperCAmelCase_    				=							f"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        UpperCAmelCase_    				=							os.path.join(args.output_dir			,    lowerCAmelCase__   )
                  accelerator.save_state(lowerCAmelCase__   )

      if args.with_tracking:
            accelerator.end_training()
def      a__			(   ):
      UpperCAmelCase_    				=							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_    				=							parser.parse_args()
      UpperCAmelCase_    				=							{"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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      =		{
    """configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		[
      """SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Swinv2ForImageClassification""",
      """Swinv2ForMaskedImageModeling""",
      """Swinv2Model""",
      """Swinv2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




