"""simple docstring"""






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


lowerCAmelCase__						:						Tuple		  =			{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        snake_case (				nn.Module       ):




					"""simple docstring"""

					def __init__(       self     :			List[str]					,lowerCamelCase__     :			Union[str, Any]    ):
									super().__init__()
									UpperCAmelCase__    =							torchvision.models.resnetaaa(pretrained=lowerCamelCase__    )
									UpperCAmelCase__    =							list(model.children()    )[:-2]
									UpperCAmelCase__    =							nn.Sequential(*lowerCamelCase__    )
									UpperCAmelCase__    =							nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str    ):
									# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
									UpperCAmelCase__    =							self.pool(self.model(lowerCamelCase__    )    )
									UpperCAmelCase__    =							torch.flatten(lowerCamelCase__					,start_dim=2    )
									UpperCAmelCase__    =							out.transpose(1					,2    ).contiguous()
									return out  # BxNx2048






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def __init__(       self     :			Tuple					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							[json.loads(lowerCamelCase__    ) for l in open(lowerCamelCase__    )]
									UpperCAmelCase__    =							os.path.dirname(lowerCamelCase__    )
									UpperCAmelCase__    =							tokenizer
									UpperCAmelCase__    =							labels
									UpperCAmelCase__    =							len(lowerCamelCase__    )
									UpperCAmelCase__    =							max_seq_length

									UpperCAmelCase__    =							transforms







					def __len__(       self     :			Optional[Any]    ):
									return len(self.data    )







					def __getitem__(       self     :			Tuple					,lowerCamelCase__     :			Optional[Any]    ):
									UpperCAmelCase__    =							torch.LongTensor(self.tokenizer.encode(self.data[index]['text']					,add_special_tokens=lowerCamelCase__    )    )
									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							sentence[0], sentence[1:-1], sentence[-1]
									UpperCAmelCase__    =							sentence[: self.max_seq_length]

									UpperCAmelCase__    =							torch.zeros(self.n_classes    )
									UpperCAmelCase__    =							1

									UpperCAmelCase__    =							Image.open(os.path.join(self.data_dir					,self.data[index]['img']    )    ).convert('RGB'    )
									UpperCAmelCase__    =							self.transforms(lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							Counter()
									for row in self.data:
													label_freqs.update(row['label']    )
									return label_freqs






def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[len(row['sentence'] ) for row in batch]
				UpperCAmelCase__   ,			UpperCAmelCase__    =							len(lowerCamelCase ), max(lowerCamelCase )

				UpperCAmelCase__    =							torch.zeros(lowerCamelCase      ,    lowerCamelCase      ,    dtype=torch.long )
				UpperCAmelCase__    =							torch.zeros(lowerCamelCase      ,    lowerCamelCase      ,    dtype=torch.long )

				for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase      ,    lowerCamelCase ) ):
								UpperCAmelCase__    =							input_row['sentence']
								UpperCAmelCase__    =							1

				UpperCAmelCase__    =							torch.stack([row['image'] for row in batch] )
				UpperCAmelCase__    =							torch.stack([row['label'] for row in batch] )
				UpperCAmelCase__    =							torch.stack([row['image_start_token'] for row in batch] )
				UpperCAmelCase__    =							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_		(      ):
				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_		(      ):
				return transforms.Compose(
				    [
				        transforms.Resize(2_5_6 ),
				        transforms.CenterCrop(2_2_4 ),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46777044, 0.44531429, 0.40661017]      ,    std=[0.12221994, 0.12145835, 0.14380469]      ,    ),
				    ] )




"""simple docstring"""






import random
class        snake_case :




					"""simple docstring"""

					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							[ord(lowerCamelCase__    ) for i in text]
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for i in plain:
													UpperCAmelCase__    =							random.randint(1					,300    )
													UpperCAmelCase__    =							(i + k) * k
													cipher.append(lowerCamelCase__    )
													key.append(lowerCamelCase__    )
									return cipher, key







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			list[int]    ):
									UpperCAmelCase__    =							[]
									for i in range(len(lowerCamelCase__    )    ):
													UpperCAmelCase__    =							int((cipher[i] - (key[i]) ** 2) / key[i]    )
													plain.append(chr(lowerCamelCase__    )    )
									return "".join(lowerCamelCase__    )


if __name__ == "__main__":
						lowerCAmelCase__  ,			lowerCAmelCase__						:						Dict		  =			Onepad().encrypt('Hello')
						print(c, k)
						print(Onepad().decrypt(c, k))



