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






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    Dict     ,       lowercase__   :    List[Any]=None     ,       lowercase__   :    int=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   file_names
        lowerCAmelCase__	      =   image_transform
        lowerCAmelCase__	      =   label_to_id




 def __len__(     self   :    List[str]):







        '''simple docstring'''



        return len(self.file_names)




 def __getitem__(     self   :    List[str]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



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






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       # 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(lowerCAmelCase__      ,    (list, tuple)      ):
              lowerCAmelCase__	      =   (image_size, image_size)

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

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

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

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

       # Instantiate dataloaders.
       lowerCAmelCase__	      =   DataLoader(lowerCAmelCase__      ,    shuffle=lowerCAmelCase__      ,    batch_size=lowerCAmelCase__      ,    num_workers=4      )
       lowerCAmelCase__	      =   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)
       lowerCAmelCase__	      =   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).
       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 / 2_5      )

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

              model.eval()
              lowerCAmelCase__	      =   0
              lowerCAmelCase__	      =   0
              for _, batch in enumerate(lowerCAmelCase__      ):
                     # 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(lowerCAmelCase__      )
                     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_0_0 * eval_metric:.2f}"""      )






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   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.'      )
       lowerCAmelCase__	      =   parser.parse_args()
       lowerCAmelCase__	      =   {'lr': 3e-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
       training_function(lowerCAmelCase__      ,    lowerCAmelCase__      )


if __name__ == "__main__":
    main()

import random
import sys

from . import cryptomath_module as cryptomath

lowerCAmelCase__      		=					(
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if mode == "encrypt":
              if key_a == 1:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'A is set to 1. Choose different key'      )
              if key_b == 0:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'B is set to 0. Choose different key'      )
       if key_a < 0 or key_b < 0 or key_b > len(lowerCAmelCase__      ) - 1:
              sys.exit(
                  'Key A must be greater than 0 and key B must '
                  F"""be between 0 and {len(lowerCAmelCase__      ) - 1}."""      )
       if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) != 1:
              sys.exit(
                  F"""Key A {key_a} and the symbol set size {len(lowerCAmelCase__      )} """
                  'are not relatively prime. Choose a different key.'      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'encrypt'      )
       lowerCAmelCase__	      =   ''
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(lowerCAmelCase__      )]
              else:
                     cipher_text += symbol
       return cipher_text






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'decrypt'      )
       lowerCAmelCase__	      =   ''
       lowerCAmelCase__	      =   cryptomath.find_mod_inverse(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     plain_text += SYMBOLS[
                         (sym_index - key_b) * mod_inverse_of_key_a % len(lowerCAmelCase__      )
                     ]
              else:
                     plain_text += symbol
       return plain_text






def  __lowerCamelCase						(		):
       while True:
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) == 1 and key_b % len(lowerCAmelCase__      ) != 0:
                     return key_b * len(lowerCAmelCase__      ) + key_b






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   input('Enter message: '      ).strip()
       lowerCAmelCase__	      =   int(input('Enter key [2000 - 9000]: '      ).strip()      )
       lowerCAmelCase__	      =   input('Encrypt/Decrypt [E/D]: '      ).strip().lower()

       if mode.startswith('e'      ):
              lowerCAmelCase__	      =   'encrypt'
              lowerCAmelCase__	      =   encrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       elif mode.startswith('d'      ):
              lowerCAmelCase__	      =   'decrypt'
              lowerCAmelCase__	      =   decrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       print(F"""\n{mode.title()}ed text: \n{translated}"""      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # main()
