
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_       (			A			:      str						):




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


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      def __init__( self					,					_UpperCAmelCase					,					_UpperCAmelCase=None					,					_UpperCAmelCase=None):
            lowerCAmelCase_						=      file_names
            lowerCAmelCase_						=      image_transform
            lowerCAmelCase_						=      label_to_id







      def __len__( self):
            return len(self.file_names)







      def __getitem__( self					,					_UpperCAmelCase):
            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_       (			A			:      Optional[int]		,					A			:      Optional[Any]						):




      """simple docstring"""
      # Initialize accelerator
      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(A		,					(list, tuple)						):
            lowerCAmelCase_						=      (image_size, image_size)

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

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

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

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

      # Instantiate dataloaders.
      lowerCAmelCase_						=      DataLoader(A		,					shuffle=A		,					batch_size=A		,					num_workers=4						)
      lowerCAmelCase_						=      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)
      lowerCAmelCase_						=      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).
      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=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.
      lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_						=      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`.
                  lowerCAmelCase_						=      {k: v.to(accelerator.device						) for k, v in batch.items()}
                  lowerCAmelCase_						=      (batch['''image'''] - mean) / std
                  lowerCAmelCase_						=      model(A						)
                  lowerCAmelCase_						=      torch.nn.functional.cross_entropy(A		,					batch['''label''']						)
                  accelerator.backward(A						)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()

            model.eval()
            lowerCAmelCase_						=      0
            lowerCAmelCase_						=      0
            for _, batch in enumerate(A						):
                  # 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(A						)
                  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 lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      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.'''						)
      lowerCAmelCase_						=      parser.parse_args()
      lowerCAmelCase_						=      {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
      training_function(A		,					A						)


if __name__ == "__main__":
 main()





import math
def lowerCamelCase_       (			A			:      int						):




      """simple docstring"""

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5		,					int(math.sqrt(A						) + 1						)		,					6						):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True
def lowerCamelCase_       (			A			:      int = 2_00_00_00						):




      """simple docstring"""
      return sum(num for num in range(3		,					A		,					2						) if is_prime(A						)						) + 2 if n > 2 else 0


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



