
"""simple docstring"""
def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    """"""
	for i in table:
		res += inp[i - 1]
	return res




def      __lowerCamelCase	(		UpperCamelCase__   ):


	"""simple docstring"""






	return data[1:] + data[0]




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    """"""
	for i in range(len(UpperCamelCase__   )   ):
		if a[i] == b[i]:
			res += "0"
		else:
			res += "1"
	return res




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    int("0b" + data[0] + data[-1]					,  2   )
	_UpperCAmelCase			=    int("0b" + data[1:3]					,  2   )
	return bin(s[row][col]   )[2:]




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    message[:4]
	_UpperCAmelCase			=    message[4:]
	_UpperCAmelCase			=    apply_table(UpperCamelCase__					,  UpperCamelCase__   )
	_UpperCAmelCase			=    xor(UpperCamelCase__					,  UpperCamelCase__   )
	_UpperCAmelCase			=    apply_sbox(UpperCamelCase__					,  temp[:4]   )  # noqa: E741
	_UpperCAmelCase			=    apply_sbox(UpperCamelCase__					,  temp[4:]   )
	_UpperCAmelCase			=    """0""" * (2 - len(UpperCamelCase__   )) + l  # noqa: E741
	_UpperCAmelCase			=    """0""" * (2 - len(UpperCamelCase__   )) + r
	_UpperCAmelCase			=    apply_table(l + r					,  UpperCamelCase__   )
	_UpperCAmelCase			=    xor(UpperCamelCase__					,  UpperCamelCase__   )
	return temp + right


if __name__ == "__main__":
			__magic_name__									=					input('''Enter 10 bit key: ''')
			__magic_name__									=					input('''Enter 8 bit message: ''')

			__magic_name__									=					[6, 3, 7, 4, 8, 5, 10, 9]
			__magic_name__									=					[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
			__magic_name__									=					[2, 4, 3, 1]
			__magic_name__									=					[2, 6, 3, 1, 4, 8, 5, 7]
			__magic_name__									=					[4, 1, 3, 5, 7, 2, 8, 6]
			__magic_name__									=					[4, 1, 2, 3, 2, 3, 4, 1]
			__magic_name__									=					[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
			__magic_name__									=					[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

			# key generation
			__magic_name__									=					apply_table(key, paa_table)
			__magic_name__									=					temp[:5]
			__magic_name__									=					temp[5:]
			__magic_name__									=					left_shift(left)
			__magic_name__									=					left_shift(right)
			__magic_name__									=					apply_table(left + right, pa_table)
			__magic_name__									=					left_shift(left)
			__magic_name__									=					left_shift(right)
			__magic_name__									=					left_shift(left)
			__magic_name__									=					left_shift(right)
			__magic_name__									=					apply_table(left + right, pa_table)

			# encryption
			__magic_name__									=					apply_table(message, IP)
			__magic_name__									=					function(expansion, sa, sa, keya, temp)
			__magic_name__									=					temp[4:] + temp[:4]
			__magic_name__									=					function(expansion, sa, sa, keya, temp)
			__magic_name__									=					apply_table(temp, IP_inv)
			print('''Cipher text is:''', CT)

			# decryption
			__magic_name__									=					apply_table(CT, IP)
			__magic_name__									=					function(expansion, sa, sa, keya, temp)
			__magic_name__									=					temp[4:] + temp[:4]
			__magic_name__									=					function(expansion, sa, sa, keya, temp)
			__magic_name__									=					apply_table(temp, IP_inv)
			print('''Plain text after decypting is:''', PT)










import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


a__		     =     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,     add_special_tokens=lowerCAmelCase	)	)
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      sentence[0], sentence[1:-1], sentence[-1]
						__UpperCamelCase					:	Optional[int]             =      sentence[: self.max_seq_length]

						__UpperCamelCase					:	List[str]             =      torch.zeros(self.n_classes	)
						__UpperCamelCase					:	List[str]             =      1

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      torch.stack([row["""image_end_token"""] for row in batch]				)

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 				A__ ()     ->						Optional[Any]:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]







def 				A__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






