





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	(				__lowercase:		Any):



  '''simple docstring'''






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

class 	__snake_case					(      _a ):






    '''simple docstring'''





    def __init__( self    :		Any      ,    snake_case    :		Optional[Any]      ,    snake_case    :		Tuple=None      ,    snake_case    :		Union[str, Any]=None				):

      """simple docstring"""





      __lowerCAmelCase								=					file_names
      __lowerCAmelCase								=					image_transform
      __lowerCAmelCase								=					label_to_id





    def __len__( self    :		Union[str, Any]				):

      """simple docstring"""





      return len(self.file_names				)





    def __getitem__( self    :		List[str]      ,    snake_case    :		int				):

      """simple docstring"""





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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[int]  ,				__lowercase:		str):



  '''simple docstring'''






  __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(__lowercase  ,				(list, tuple)):
    __lowerCAmelCase								=					(image_size, image_size)

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

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

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

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

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

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

  # Instantiate dataloaders.
  __lowerCAmelCase								=					DataLoader(__lowercase  ,				shuffle=__lowercase  ,				batch_size=__lowercase  ,				num_workers=4)
  __lowerCAmelCase								=					DataLoader(__lowercase  ,				shuffle=__lowercase  ,				batch_size=__lowercase  ,				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=__lowercase  ,				num_classes=len(__lowercase))

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

  # 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(
      __lowercase  ,				__lowercase  ,				__lowercase  ,				__lowercase  ,				__lowercase)

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

    model.eval()
    __lowerCAmelCase								=					0
    __lowerCAmelCase								=					0
    for _, batch in enumerate(__lowercase):
      # 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(__lowercase)
      __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}: {1_00 * eval_metric:.2f}''')


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					argparse.ArgumentParser(description='''Simple example of training script.''')
  parser.add_argument('''--data_dir'''  ,				required=__lowercase  ,				help='''The data folder on disk.''')
  parser.add_argument(
      '''--mixed_precision'''  ,				type=__lowercase  ,				default=__lowercase  ,				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=__lowercase  ,				default=__lowercase  ,				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.''')
  __lowerCAmelCase								=					parser.parse_args()
  __lowerCAmelCase								=					{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
  training_function(__lowercase  ,				__lowercase)


if __name__ == "__main__":
      main()







def        __SCREAMING_SNAKE_CASE	(				__lowercase:		int = 10_00):



  '''simple docstring'''






  __lowerCAmelCase								=					0
  for i in range(__lowercase):
    if i % 3 == 0 or i % 5 == 0:
      result += i
  return result


if __name__ == "__main__":
      print(f"""{solution() = }""")
