
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     UpperCamelCase      (					__lowerCamelCase  :				Optional[int]					):
       snake_case :     List[str]           =   fname.split(os.path.sep					)[-1]
       return re.search(r"^(.*)_\d+\.jpg$"       ,      __lowerCamelCase					).groups()[0]






class 						UpperCAmelCase	(		A_				):



   def __init__(self	:      List[Any]					,		snake_case__	:      Any					,		snake_case__	:      str=None					,		snake_case__	:      Union[str, Any]=None				)	->			List[str]:


          '''simple docstring'''


          snake_case :     str           =   file_names
          snake_case :     List[str]           =   image_transform
          snake_case :     str           =   label_to_id



   def __len__(self	:      Tuple				)	->			str:


          '''simple docstring'''


          return len(self.file_names				)



   def __getitem__(self	:      List[str]					,		snake_case__	:      Tuple				)	->			List[str]:


          '''simple docstring'''


          snake_case :     int           =   self.file_names[idx]
          snake_case :     Optional[Any]           =   PIL.Image.open(snake_case__				)
          snake_case :     Any           =   raw_image.convert("RGB"				)
          if self.image_transform is not None:
                 snake_case :     List[str]           =   self.image_transform(snake_case__				)
          snake_case :     List[str]           =   extract_label(snake_case__				)
          if self.label_to_id is not None:
                 snake_case :     int           =   self.label_to_id[label]
          return {"image": image, "label": label}



def     UpperCamelCase      (					__lowerCamelCase  :				Union[str, Any]       ,      __lowerCamelCase  :				str					):
       # Initialize accelerator
       snake_case :     Union[str, Any]           =   Accelerator(cpu=args.cpu       ,      mixed_precision=args.mixed_precision					)

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       snake_case :     Any           =   config["lr"]
       snake_case :     int           =   int(config["num_epochs"]					)
       snake_case :     int           =   int(config["seed"]					)
       snake_case :     str           =   int(config["batch_size"]					)
       snake_case :     Tuple           =   config["image_size"]
       if not isinstance(__lowerCamelCase       ,      (list, tuple)					):
              snake_case :     Any           =   (image_size, image_size)

       # Grab all the image filenames
       snake_case :     List[Any]           =   [os.path.join(args.data_dir       ,      __lowerCamelCase					) for fname in os.listdir(args.data_dir					) if fname.endswith(".jpg"					)]

       # Build the label correspondences
       snake_case :     Any           =   [extract_label(__lowerCamelCase					) for fname in file_names]
       snake_case :     str           =   list(set(__lowerCamelCase					)					)
       id_to_label.sort()
       snake_case :     Any           =   {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
       snake_case :     List[Any]           =   np.random.permutation(len(__lowerCamelCase					)					)
       snake_case :     Dict           =   int(0.8 * len(__lowerCamelCase					)					)
       snake_case :     Optional[Any]           =   random_perm[:cut]
       snake_case :     Optional[Any]           =   random_perm[cut:]

       # For training we use a simple RandomResizedCrop
       snake_case :     Dict           =   Compose([RandomResizedCrop(__lowerCamelCase       ,      scale=(0.5, 1.0)					), ToTensor()]					)
       snake_case :     List[Any]           =   PetsDataset(
           [file_names[i] for i in train_split]       ,      image_transform=__lowerCamelCase       ,      label_to_id=__lowerCamelCase					)

       # For evaluation, we use a deterministic Resize
       snake_case :     Optional[Any]           =   Compose([Resize(__lowerCamelCase					), ToTensor()]					)
       snake_case :     Union[str, Any]           =   PetsDataset([file_names[i] for i in eval_split]       ,      image_transform=__lowerCamelCase       ,      label_to_id=__lowerCamelCase					)

       # Instantiate dataloaders.
       snake_case :     Dict           =   DataLoader(__lowerCamelCase       ,      shuffle=__lowerCamelCase       ,      batch_size=__lowerCamelCase       ,      num_workers=4					)
       snake_case :     Any           =   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)
       snake_case :     Optional[int]           =   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).
       snake_case :     int           =   model.to(accelerator.device					)

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

       # We normalize the batches of images to be a bit faster.
       snake_case :     List[str]           =   torch.tensor(model.default_cfg["mean"]					)[None, :, None, None].to(accelerator.device					)
       snake_case :     Optional[int]           =   torch.tensor(model.default_cfg["std"]					)[None, :, None, None].to(accelerator.device					)

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

       # Instantiate learning rate scheduler
       snake_case :     Any           =   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.
       snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case :     Union[str, Any]           =   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`.
                     snake_case :     Tuple           =   {k: v.to(accelerator.device					) for k, v in batch.items()}
                     snake_case :     List[Any]           =   (batch["image"] - mean) / std
                     snake_case :     List[Any]           =   model(__lowerCamelCase					)
                     snake_case :     int           =   torch.nn.functional.cross_entropy(__lowerCamelCase       ,      batch["label"]					)
                     accelerator.backward(__lowerCamelCase					)
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()

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

              snake_case :     int           =   accurate.item() / num_elems
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}"""					)


def     UpperCamelCase      (					):
       snake_case :     Dict           =   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."					)
       snake_case :     Any           =   parser.parse_args()
       snake_case :     str           =   {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
       training_function(__lowerCamelCase       ,      __lowerCamelCase					)


if __name__ == "__main__":
   main()







import math
from itertools import permutations


def     UpperCamelCase      (					__lowerCamelCase  :				int					):

       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(__lowerCamelCase					) + 1					)       ,      6					):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


def     UpperCamelCase      (					__lowerCamelCase  :				int       ,      __lowerCamelCase  :				list					):
       snake_case       ,      snake_case :     List[str]           =   0, len(__lowerCamelCase					) - 1
       while left <= right:
              snake_case :     int           =   (left + right) // 2
              if prime_list[middle] == target:
                     return True
              elif prime_list[middle] < target:
                     snake_case :     Dict           =   middle + 1
              else:
                     snake_case :     Optional[int]           =   middle - 1

       return False


def     UpperCamelCase      (					):
       snake_case :     List[Any]           =   [n for n in range(1001       ,      10000       ,      2					) if is_prime(__lowerCamelCase					)]
       snake_case :     Dict           =   []

       for number in prime_list:
              snake_case :     Dict           =   []

              for prime_member in permutations(list(str(__lowerCamelCase					)					)					):
                     snake_case :     Dict           =   int("".join(__lowerCamelCase					)					)

                     if prime % 2 == 0:
                            continue

                     if search(__lowerCamelCase       ,      __lowerCamelCase					):
                            tmp_numbers.append(__lowerCamelCase					)

              tmp_numbers.sort()
              if len(__lowerCamelCase					) >= 3:
                     candidates.append(__lowerCamelCase					)

       snake_case :     Union[str, Any]           =   []
       for candidate in candidates:
              snake_case :     Dict           =   len(__lowerCamelCase					)
              snake_case :     Any           =   False

              for i in range(__lowerCamelCase					):
                     for j in range(i + 1       ,      __lowerCamelCase					):
                            for k in range(j + 1       ,      __lowerCamelCase					):
                                   if (
                                       abs(candidate[i] - candidate[j]					)
                                       == abs(candidate[j] - candidate[k]					)
                                       and len({candidate[i], candidate[j], candidate[k]}					) == 3
                                   ):
                                          passed.append(
                                              sorted([candidate[i], candidate[j], candidate[k]]					)					)
                                          snake_case :     Optional[Any]           =   True

                                   if found:
                                          break
                            if found:
                                   break
                     if found:
                            break

       snake_case :     Tuple           =   set()
       for seq in passed:
              answer.add("".join([str(__lowerCamelCase					) for i in seq]					)					)

       return max(int(__lowerCamelCase					) for x in answer					)


if __name__ == "__main__":
   print(solution())





