





'''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							:     Optional[Any]		     ={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        UpperCAmelCase     (	nn.Module    ):
 def __init__(    self				:int						,	lowercase_				:Tuple							)->							Optional[Any]:
     super().__init__()
     A__    =      torchvision.models.resnetaaa(pretrained=lowercase_							)
     A__    =      list(model.children()							)[:-2]
     A__    =      nn.Sequential(*lowercase_							)
     A__    =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]							)



 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:List[Any]							)->							List[str]:
     # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
     A__    =      self.pool(self.model(lowercase_							)							)
     A__    =      torch.flatten(lowercase_						,	start_dim=2							)
     A__    =      out.transpose(1						,	2							).contiguous()
     return out  # BxNx2048






class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:List[str]						,	lowercase_				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]							)->							List[str]:
     A__    =      [json.loads(lowercase_							) for l in open(lowercase_							)]
     A__    =      os.path.dirname(lowercase_							)
     A__    =      tokenizer
     A__    =      labels
     A__    =      len(lowercase_							)
     A__    =      max_seq_length

     A__    =      transforms
 def __len__(    self				:int							)->							str:
     return len(self.data							)
 def __getitem__(    self				:Tuple						,	lowercase_				:Union[str, Any]							)->							str:
     A__    =      torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]						,	add_special_tokens=lowercase_							)							)
     A__,     A__,     A__    =      sentence[0], sentence[1:-1], sentence[-1]
     A__    =      sentence[: self.max_seq_length]

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

     A__    =      Image.open(os.path.join(self.data_dir						,	self.data[index]["img"]							)							).convert("RGB"							)
     A__    =      self.transforms(lowercase_							)

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



 def 					UpperCAmelCase_							(    self				:Any							)->							Optional[int]:
     A__    =      Counter()
     for row in self.data:
         label_freqs.update(row["label"]							)
     return label_freqs





def 						UpperCamelCase    (							_lowerCamelCase : Tuple	):
    A__    =      [len(row["sentence"]	) for row in batch]
    A__,     A__    =      len(_lowerCamelCase	), max(_lowerCamelCase	)

    A__    =      torch.zeros(_lowerCamelCase						,	_lowerCamelCase						,	dtype=torch.long	)
    A__    =      torch.zeros(_lowerCamelCase						,	_lowerCamelCase						,	dtype=torch.long	)

    for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase						,	_lowerCamelCase	)	):
        A__    =      input_row["sentence"]
        A__    =      1

    A__    =      torch.stack([row["image"] for row in batch]	)
    A__    =      torch.stack([row["label"] for row in batch]	)
    A__    =      torch.stack([row["image_start_token"] for row in batch]	)
    A__    =      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 						UpperCamelCase    (							):
    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 						UpperCamelCase    (							):
    return transforms.Compose(
        [
            transforms.Resize(2_56	),
            transforms.CenterCrop(2_24	),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]						,	std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]						,	),
        ]	)










'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812





def 						UpperCamelCase    (							):
    print("Making key files..."	)
    make_key_files("rsa"						,	10_24	)
    print("Key files generation successful."	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    print("Generating prime p..."	)
    A__    =      rabinMiller.generate_large_prime(_lowerCamelCase	)
    print("Generating prime q..."	)
    A__    =      rabinMiller.generate_large_prime(_lowerCamelCase	)
    A__    =      p * q

    print("Generating e that is relatively prime to (p - 1) * (q - 1)..."	)
    while True:
        A__    =      random.randrange(2 ** (key_size - 1)						,	2 ** (key_size)	)
        if cryptoMath.gcd(_lowerCamelCase						,	(p - 1) * (q - 1)	) == 1:
            break

    print("Calculating d that is mod inverse of e..."	)
    A__    =      cryptoMath.find_mod_inverse(_lowerCamelCase						,	(p - 1) * (q - 1)	)

    A__    =      (n, e)
    A__    =      (n, d)
    return (public_key, private_key)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : int	):
    if os.path.exists(F"{name}_pubkey.txt"	) or os.path.exists(F"{name}_privkey.txt"	):
        print("\nWARNING:"	)
        print(
            F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
            "Use a different name or delete these files and re-run this program."	)
        sys.exit()

    A__,     A__    =      generate_key(_lowerCamelCase	)
    print(F"\nWriting public key to file {name}_pubkey.txt..."	)
    with open(F"{name}_pubkey.txt"						,	"w"	) as out_file:
        out_file.write(F"{key_size},{public_key[0]},{public_key[1]}"	)

    print(F"Writing private key to file {name}_privkey.txt..."	)
    with open(F"{name}_privkey.txt"						,	"w"	) as out_file:
        out_file.write(F"{key_size},{private_key[0]},{private_key[1]}"	)


if __name__ == "__main__":
  main()



