





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:     Tuple				)			->		int:







      '''simple docstring'''






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





class A_      (    _lowerCamelCase     ):




   def __init__(self			:int					,     _UpperCamelCase			:List[str]					,     _UpperCamelCase			:List[str]=None					,     _UpperCamelCase			:str=None				)->      Union[str, Any]:
         __A      =       file_names
         __A      =       image_transform
         __A      =       label_to_id




   def __len__(self			:Dict				)->      int:
         return len(self.file_names				)






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





def _a       (			lowerCamelCase:     Union[str, Any]      ,       lowerCamelCase:     Optional[Any]				)			->		Optional[Any]:







      '''simple docstring'''






      __A      =       Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision				)

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      __A      =       config['''lr''']
      __A      =       int(config['''num_epochs''']				)
      __A      =       int(config['''seed''']				)
      __A      =       int(config['''batch_size''']				)
      __A      =       config['''image_size''']
      if not isinstance(lowerCamelCase      ,       (list, tuple)				):
            __A      =       (image_size, image_size)

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

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

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

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

      # Instantiate dataloaders.
      __A      =       DataLoader(lowerCamelCase      ,       shuffle=lowerCamelCase      ,       batch_size=lowerCamelCase      ,       num_workers=4				)
      __A      =       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      =       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      =       model.to(accelerator.device				)

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

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

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

      # Instantiate learning rate scheduler
      __A      =       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      =       accelerator.prepare(
          lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase				)

      # Now we train the model
      for epoch in range(lowerCamelCase				):
            model.train()
            for step, batch in enumerate(lowerCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  __A      =       {k: v.to(accelerator.device				) for k, v in batch.items()}
                  __A      =       (batch['''image'''] - mean) / std
                  __A      =       model(lowerCamelCase				)
                  __A      =       torch.nn.functional.cross_entropy(lowerCamelCase      ,       batch['''label''']				)
                  accelerator.backward(lowerCamelCase				)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()

            model.eval()
            __A      =       0
            __A      =       0
            for _, batch in enumerate(lowerCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  __A      =       {k: v.to(accelerator.device				) for k, v in batch.items()}
                  __A      =       (batch['''image'''] - mean) / std
                  with torch.no_grad():
                        __A      =       model(lowerCamelCase				)
                  __A      =       outputs.argmax(dim=-1				)
                  __A     ,		__A      =       accelerator.gather_for_metrics((predictions, batch['''label'''])				)
                  __A      =       predictions == references
                  num_elems += accurate_preds.shape[0]
                  accurate += accurate_preds.long().sum()

            __A      =       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 _a       (			)			->		List[str]:







      '''simple docstring'''






      __A      =       argparse.ArgumentParser(description='''Simple example of training script.'''				)
      parser.add_argument('''--data_dir'''      ,       required=lowerCamelCase      ,       help='''The data folder on disk.'''				)
      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(
          '''--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('''--cpu'''      ,       action='''store_true'''      ,       help='''If passed, will train on the CPU.'''				)
      __A      =       parser.parse_args()
      __A      =       {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
      training_function(lowerCamelCase      ,       lowerCamelCase				)


if __name__ == "__main__":
       main()












from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__      :							Optional[Any]									=  logging.get_logger(__name__)

snake_case__      :							List[str]									=  {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class A_      (    _lowerCamelCase     ):
   lowerCAmelCase__       =				"""nystromformer"""




   def __init__(self			:Union[str, Any]					,     _UpperCamelCase			:Optional[int]=3_0000					,     _UpperCamelCase			:Optional[int]=768					,     _UpperCamelCase			:List[str]=12					,     _UpperCamelCase			:Dict=12					,     _UpperCamelCase			:Any=3072					,     _UpperCamelCase			:List[str]="gelu_new"					,     _UpperCamelCase			:str=0.1					,     _UpperCamelCase			:Dict=0.1					,     _UpperCamelCase			:List[Any]=510					,     _UpperCamelCase			:str=2					,     _UpperCamelCase			:List[str]=64					,     _UpperCamelCase			:List[str]=64					,     _UpperCamelCase			:Optional[int]=65					,     _UpperCamelCase			:Optional[int]=False					,     _UpperCamelCase			:Optional[int]=0.0_2					,     _UpperCamelCase			:Dict=1e-5					,     _UpperCamelCase			:Tuple=1					,     _UpperCamelCase			:Union[str, Any]=0					,     _UpperCamelCase			:Optional[int]=2					,     **_UpperCamelCase			:Dict					,     )->      Any:
         __A      =       vocab_size
         __A      =       max_position_embeddings
         __A      =       hidden_size
         __A      =       num_hidden_layers
         __A      =       num_attention_heads
         __A      =       intermediate_size
         __A      =       hidden_act
         __A      =       hidden_dropout_prob
         __A      =       attention_probs_dropout_prob
         __A      =       initializer_range
         __A      =       type_vocab_size
         __A      =       segment_means_seq_len
         __A      =       num_landmarks
         __A      =       conv_kernel_size
         __A      =       inv_coeff_init_option
         __A      =       layer_norm_eps
         super().__init__(pad_token_id=_UpperCamelCase					,     bos_token_id=_UpperCamelCase					,     eos_token_id=_UpperCamelCase					,     **_UpperCamelCase				)





