


'''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 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Any      )						->    Optional[Any]:





   '''simple docstring'''







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







class        UpperCAmelCase       (				__SCREAMING_SNAKE_CASE):

 def __init__(       self       :		Tuple,    a_       :		int,    a_       :		List[Any]=None,    a_       :		Any=None    ):





    """simple docstring"""



    UpperCamelCase__         =  file_names
    UpperCamelCase__         =  image_transform
    UpperCamelCase__         =  label_to_id

 def __len__(       self       :		str    ):





    """simple docstring"""



    return len(self.file_names    )

 def __getitem__(       self       :		List[Any],    a_       :		int    ):





    """simple docstring"""



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







def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: List[Any]			,						_UpperCamelCase					: Tuple      )						->    Dict:





   '''simple docstring'''







   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(A__			,						(list, tuple)      ):
      UpperCamelCase__         =  (image_size, image_size)

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

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

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

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

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

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

   # Instantiate dataloaders.
   UpperCamelCase__         =  DataLoader(A__			,						shuffle=A__			,						batch_size=A__			,						num_workers=4      )
   UpperCamelCase__         =  DataLoader(A__			,						shuffle=A__			,						batch_size=A__			,						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=A__			,						num_classes=len(A__      )      )

   # 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=A__			,						max_lr=A__			,						epochs=A__			,						steps_per_epoch=len(A__      )      )

   # 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__         =  accelerator.prepare(
       A__			,						A__			,						A__			,						A__			,						A__      )

   # Now we train the model
   for epoch in range(A__      ):
      model.train()
      for step, batch in enumerate(A__      ):
         # 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(A__      )
         UpperCamelCase__         =  torch.nn.functional.cross_entropy(A__			,						batch["label"]      )
         accelerator.backward(A__      )
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()

      model.eval()
      UpperCamelCase__         =  0
      UpperCamelCase__         =  0
      for _, batch in enumerate(A__      ):
         # 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(A__      )
         UpperCamelCase__         =  outputs.argmax(dim=-1      )
         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}: {1_00 * eval_metric:.2f}'      )



def 				SCREAMING_SNAKE_CASE__(						)						->    Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__         =  argparse.ArgumentParser(description="Simple example of training script."      )
   parser.add_argument("--data_dir"			,						required=A__			,						help="The data folder on disk."      )
   parser.add_argument(
       "--mixed_precision"			,						type=A__			,						default=A__			,						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(
       "--checkpointing_steps"			,						type=A__			,						default=A__			,						help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."			,						)
   parser.add_argument("--cpu"			,						action="store_true"			,						help="If passed, will train on the CPU."      )
   UpperCamelCase__         =  parser.parse_args()
   UpperCamelCase__         =  {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
   training_function(A__			,						A__      )


if __name__ == "__main__":
       main()



import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowerCAmelCase__       :	int     				=logging.get_logger(__name__)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __init__(	self       ,		*lowerCAmelCase__       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										warnings.warn(
										    'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use ViltImageProcessor instead.'       ,		lowerCAmelCase__       ,		)
										super().__init__(*lowerCAmelCase__       ,		**lowerCAmelCase__    )





