

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 :		Dict		):
			__UpperCamelCase    :    Tuple								=      fname.split(os.path.sep		)[-1]
			return re.search(R"^(.*)_\d+\.jpg$"     ,			_lowerCAmelCase		).groups()[0]
class 		SCREAMING_SNAKE_CASE__					(		A__   ):







						"""simple docstring"""




						def __init__(				self , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None    )							->	Dict:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      file_names
									__UpperCamelCase    :    List[Any]								=      image_transform
									__UpperCamelCase    :    int								=      label_to_id

						def __len__(				self    )							->	Optional[Any]:




									'''simple docstring'''


									return len(self.file_names    )




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




									'''simple docstring'''


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







def  UpperCAmelCase_   (_lowerCAmelCase :		List[Any]     ,			_lowerCAmelCase :		Optional[int]		):
			__UpperCamelCase    :    str								=      Accelerator(cpu=args.cpu     ,			mixed_precision=args.mixed_precision		)

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

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

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

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

			# For evaluation, we use a deterministic Resize
			__UpperCamelCase    :    Optional[int]								=      Compose([Resize(_lowerCAmelCase		), ToTensor()]		)
			__UpperCamelCase    :    Optional[Any]								=      PetsDataset([file_names[i] for i in eval_split]     ,			image_transform=_lowerCAmelCase     ,			label_to_id=_lowerCAmelCase		)

			# Instantiate dataloaders.
			__UpperCamelCase    :    Dict								=      DataLoader(_lowerCAmelCase     ,			shuffle=_lowerCAmelCase     ,			batch_size=_lowerCAmelCase     ,			num_workers=4		)
			__UpperCamelCase    :    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)
			__UpperCamelCase    :    str								=      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).
			__UpperCamelCase    :    Tuple								=      model.to(accelerator.device		)

			# Freezing the base model
			for param in model.parameters():
						__UpperCamelCase    :    str								=      False
			for param in model.get_classifier().parameters():
						__UpperCamelCase    :    Optional[int]								=      True

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

			# Instantiate optimizer
			__UpperCamelCase    :    List[str]								=      torch.optim.Adam(params=model.parameters()     ,			lr=lr / 25		)

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

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

						__UpperCamelCase    :    Union[str, Any]								=      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  UpperCAmelCase_   ():
			__UpperCamelCase    :    str								=      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."		)
			__UpperCamelCase    :    Any								=      parser.parse_args()
			__UpperCamelCase    :    int								=      {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
			training_function(_lowerCAmelCase     ,			_lowerCAmelCase		)


if __name__ == "__main__":
					main()


import random
import sys

from . import cryptomath_module as cryptomath

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







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			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  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			__UpperCamelCase							,				__UpperCamelCase    :    int								=      divmod(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			check_keys(_lowerCAmelCase     ,			_lowerCAmelCase     ,			"encrypt"		)
			__UpperCamelCase    :    List[Any]								=      ""
			for symbol in message:
						if symbol in SYMBOLS:
									__UpperCamelCase    :    Tuple								=      SYMBOLS.find(_lowerCAmelCase		)
									cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_lowerCAmelCase		)]
						else:
									cipher_text += symbol
			return cipher_text







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      divmod(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			check_keys(_lowerCAmelCase     ,			_lowerCAmelCase     ,			"decrypt"		)
			__UpperCamelCase    :    List[Any]								=      ""
			__UpperCamelCase    :    Tuple								=      cryptomath.find_mod_inverse(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			for symbol in message:
						if symbol in SYMBOLS:
									__UpperCamelCase    :    List[Any]								=      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  UpperCAmelCase_   ():
			while True:
						__UpperCamelCase    :    Optional[int]								=      random.randint(2     ,			len(_lowerCAmelCase		)		)
						__UpperCamelCase    :    Optional[int]								=      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  UpperCAmelCase_   ():
			__UpperCamelCase    :    List[Any]								=      input("Enter message: "		).strip()
			__UpperCamelCase    :    List[Any]								=      int(input("Enter key [2000 - 9000]: "		).strip()		)
			__UpperCamelCase    :    Optional[int]								=      input("Encrypt/Decrypt [E/D]: "		).strip().lower()

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# main()