

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						:Union[str, Any]		):


       """simple docstring"""




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

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase			,   _UpperCamelCase=None			,   _UpperCamelCase=None      )			->       List[Any]:






              """simple docstring"""


              __snake_case					  =       file_names
              __snake_case					  =       image_transform
              __snake_case					  =       label_to_id


       def __len__(		self      )			->       int:






              """simple docstring"""


              return len(self.file_names      )






       def __getitem__(		self			,   _UpperCamelCase      )			->       Union[str, Any]:






              """simple docstring"""


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







def 	lowerCamelCase__			(   __A						:str  ,__A						:Optional[Any]		):


       """simple docstring"""




       __snake_case					  =       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					  =       config["""lr"""]
       __snake_case					  =       int(config["""num_epochs"""]		)
       __snake_case					  =       int(config["""seed"""]		)
       __snake_case					  =       int(config["""batch_size"""]		)
       __snake_case					  =       config["""image_size"""]
       if not isinstance(__A  ,(list, tuple)		):
              __snake_case					  =       (image_size, image_size)

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

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

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

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

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

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

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

       # Instantiate optimizer
       __snake_case					  =       torch.optim.Adam(params=model.parameters()  ,lr=lr / 2_5		)

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

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

              __snake_case					  =       accurate.item() / num_elems
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'epoch {epoch}: {1_0_0 * eval_metric:.2f}'		)







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       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."""		)
       __snake_case					  =       parser.parse_args()
       __snake_case					  =       {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
       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 = 1_0_0_0  ,__A						:int = 1_0_0_0		):


       """simple docstring"""




       __snake_case					  =       [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1  ,__A		):
              for b in range(2  ,__A		):
                     if is_prime(__A		):
                            __snake_case					  =       0
                            __snake_case					  =       0
                            while is_prime((n**2) + (a * n) + b		):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   __snake_case					  =       [count, a, b]
       __snake_case					  =       longest[1] * longest[2]
       return ans


if __name__ == "__main__":
    print(solution(1000, 1000))

