


import os
import sys

from . import rsa_key_generator as rkg

UpperCAmelCase     =       128
UpperCAmelCase     =       256






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE = DEFAULT_BLOCK_SIZE   )->    list[int]:




      """simple docstring"""

      snake_case_			= message.encode('''ascii'''   )

      snake_case_			= []
      for block_start in range(0   ,   len(SCREAMING_SNAKE_CASE   )   ,   SCREAMING_SNAKE_CASE   ):
            snake_case_			= 0
            for i in range(SCREAMING_SNAKE_CASE   ,   min(block_start + block_size   ,   len(SCREAMING_SNAKE_CASE   )   )   ):
                  block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
            block_ints.append(SCREAMING_SNAKE_CASE   )
      return block_ints






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE = DEFAULT_BLOCK_SIZE   )->    str:




      """simple docstring"""

      snake_case_			= []
      for block_int in block_ints:
            snake_case_			= []
            for i in range(block_size - 1   ,   -1   ,   -1   ):
                  if len(SCREAMING_SNAKE_CASE   ) + i < message_length:
                        snake_case_			= block_int // (BYTE_SIZE**i)
                        snake_case_			= block_int % (BYTE_SIZE**i)
                        block_message.insert(0   ,   chr(SCREAMING_SNAKE_CASE   )   )
            message.extend(SCREAMING_SNAKE_CASE   )
      return "".join(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE = DEFAULT_BLOCK_SIZE   )->    list[int]:




      """simple docstring"""

      snake_case_			= []
      snake_case_			,       snake_case_			= key

      for block in get_blocks_from_text(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
            encrypted_blocks.append(pow(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   )
      return encrypted_blocks






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE = DEFAULT_BLOCK_SIZE   ,   )->    str:




      """simple docstring"""

      snake_case_			= []
      snake_case_			,       snake_case_			= key
      for block in encrypted_blocks:
            decrypted_blocks.append(pow(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   )
      return get_text_from_blocks(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    tuple[int, int, int]:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE   ) as fo:
            snake_case_			= fo.read()
      snake_case_			,       snake_case_			,       snake_case_			= content.split(''','''   )
      return (int(SCREAMING_SNAKE_CASE   ), int(SCREAMING_SNAKE_CASE   ), int(SCREAMING_SNAKE_CASE   ))






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE = DEFAULT_BLOCK_SIZE   ,   )->    str:




      """simple docstring"""

      snake_case_			,       snake_case_			,       snake_case_			= read_key_file(SCREAMING_SNAKE_CASE   )
      if key_size < block_size * 8:
            sys.exit(
                '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
                '''requires the block size to be equal to or greater than the key size. '''
                '''Either decrease the block size or use different keys.'''.format(
                    block_size * 8   ,   SCREAMING_SNAKE_CASE   )   )

      snake_case_			= [str(SCREAMING_SNAKE_CASE   ) for i in encrypt_message(SCREAMING_SNAKE_CASE   ,   (n, e)   ,   SCREAMING_SNAKE_CASE   )]

      snake_case_			= ''','''.join(SCREAMING_SNAKE_CASE   )
      snake_case_			= f'''{len(SCREAMING_SNAKE_CASE   )}_{block_size}_{encrypted_content}'''
      with open(SCREAMING_SNAKE_CASE   ,   '''w'''   ) as fo:
            fo.write(SCREAMING_SNAKE_CASE   )
      return encrypted_content






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    str:




      """simple docstring"""

      snake_case_			,       snake_case_			,       snake_case_			= read_key_file(SCREAMING_SNAKE_CASE   )
      with open(SCREAMING_SNAKE_CASE   ) as fo:
            snake_case_			= fo.read()
      snake_case_			,       snake_case_			,       snake_case_			= content.split('''_'''   )
      snake_case_			= int(SCREAMING_SNAKE_CASE   )
      snake_case_			= int(SCREAMING_SNAKE_CASE   )

      if key_size < block_size * 8:
            sys.exit(
                '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
                '''requires the block size to be equal to or greater than the key size. '''
                '''Did you specify the correct key file and encrypted file?'''.format(
                    block_size * 8   ,   SCREAMING_SNAKE_CASE   )   )

      snake_case_			= []
      for block in encrypted_message.split(''','''   ):
            encrypted_blocks.append(int(SCREAMING_SNAKE_CASE   )   )

      return decrypt_message(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   (n, d)   ,   SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       ()->    None:




      """simple docstring"""

      snake_case_			= '''encrypted_file.txt'''
      snake_case_			= input(R'''Encrypt\Decrypt [e\d]: '''   )

      if response.lower().startswith('''e'''   ):
            snake_case_			= '''encrypt'''
      elif response.lower().startswith('''d'''   ):
            snake_case_			= '''decrypt'''

      if mode == "encrypt":
            if not os.path.exists('''rsa_pubkey.txt'''   ):
                  rkg.make_key_files('''rsa'''   ,   1024   )

            snake_case_			= input('''\nEnter message: '''   )
            snake_case_			= '''rsa_pubkey.txt'''
            print(f'''Encrypting and writing to {filename}...'''   )
            snake_case_			= encrypt_and_write_to_file(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

            print('''\nEncrypted text:'''   )
            print(SCREAMING_SNAKE_CASE   )

      elif mode == "decrypt":
            snake_case_			= '''rsa_privkey.txt'''
            print(f'''Reading from {filename} and decrypting...'''   )
            snake_case_			= read_from_file_and_decrypt(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
            print('''writing decryption to rsa_decryption.txt...'''   )
            with open('''rsa_decryption.txt'''   ,   '''w'''   ) as dec:
                  dec.write(SCREAMING_SNAKE_CASE   )

            print('''\nDecryption:'''   )
            print(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
   main()



import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPTextConfig(
           bos_token_id=0						, eos_token_id=2						, hidden_size=self.text_embedder_hidden_size						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, layer_norm_eps=1E-05						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=10_00						, )
       return CLIPTextModelWithProjection(_UpperCAmelCase   )



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

       snake_case_			= {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       snake_case_			= PriorTransformer(**_UpperCAmelCase   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       snake_case_			= nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size						, image_size=2_24						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_channels=3						, num_hidden_layers=5						, patch_size=14						, )

       snake_case_			= CLIPVisionModelWithProjection(_UpperCAmelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPImageProcessor(
           crop_size=2_24						, do_center_crop=_UpperCAmelCase						, do_normalize=_UpperCAmelCase						, do_resize=_UpperCAmelCase						, image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]						, image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]						, resample=3						, size=2_24						, )

       return image_processor



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

       snake_case_			= UnCLIPScheduler(
           variance_type='''fixed_small_log'''						, prediction_type='''sample'''						, num_train_timesteps=10_00						, clip_sample=_UpperCAmelCase						, clip_sample_range=10.0						, )

       snake_case_			= {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

       snake_case_			= {
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''strength''': 0.5,
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**_UpperCAmelCase   )
       snake_case_			= pipe.to(_UpperCAmelCase   )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(_UpperCAmelCase   )						, return_dict=_UpperCAmelCase						, )[0]

       snake_case_			= image[0, -10:]
       snake_case_			= image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       snake_case_			= np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2



 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

       self._test_inference_batch_single_identical(
           test_max_difference=_UpperCAmelCase						, relax_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

       self._test_attention_slicing_forward_pass(
           test_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )