code: 
  """simple docstring"""
  import os
  import sys
  
  from . import rsa_key_generator as rkg
  
  _A						=     1_28
  _A						=     2_56
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase = DEFAULT_BLOCK_SIZE						)  ->   list[int]:
        UpperCAmelCase__ :       List[Any]						=						message.encode("""ascii"""						)
  
        UpperCAmelCase__ :       Optional[int]						=						[]
        for block_start in range(0 ,  len(lowerCAmelCase						) ,  lowerCAmelCase						):
              UpperCAmelCase__ :       Optional[int]						=						0
              for i in range(lowerCAmelCase ,  min(block_start + block_size ,  len(lowerCAmelCase						)						)						):
                    block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
              block_ints.append(lowerCAmelCase						)
        return block_ints
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase = DEFAULT_BLOCK_SIZE						)  ->   str:
        UpperCAmelCase__ :       list[str]						=						[]
        for block_int in block_ints:
              UpperCAmelCase__ :       list[str]						=						[]
              for i in range(block_size - 1 ,  -1 ,  -1						):
                    if len(lowerCAmelCase						) + i < message_length:
                          UpperCAmelCase__ :       Dict						=						block_int // (BYTE_SIZE**i)
                          UpperCAmelCase__ :       Optional[int]						=						block_int % (BYTE_SIZE**i)
                          block_message.insert(0 ,  chr(lowerCAmelCase						)						)
              message.extend(lowerCAmelCase						)
        return "".join(lowerCAmelCase						)
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase = DEFAULT_BLOCK_SIZE						)  ->   list[int]:
        UpperCAmelCase__ :       Any						=						[]
        UpperCAmelCase__ :       Tuple						=						key
  
        for block in get_blocks_from_text(lowerCAmelCase ,  lowerCAmelCase						):
              encrypted_blocks.append(pow(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)						)
        return encrypted_blocks
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase = DEFAULT_BLOCK_SIZE ,  )  ->   str:
        UpperCAmelCase__ :       Optional[int]						=						[]
        UpperCAmelCase__ :       Union[str, Any]						=						key
        for block in encrypted_blocks:
              decrypted_blocks.append(pow(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)						)
        return get_text_from_blocks(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   tuple[int, int, int]:
        with open(lowerCAmelCase						) as fo:
              UpperCAmelCase__ :       List[Any]						=						fo.read()
        UpperCAmelCase__ :       Optional[Any]						=						content.split(""","""						)
        return (int(lowerCAmelCase						), int(lowerCAmelCase						), int(lowerCAmelCase						))
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase = DEFAULT_BLOCK_SIZE ,  )  ->   str:
        UpperCAmelCase__ :       Any						=						read_key_file(lowerCAmelCase						)
        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 ,  lowerCAmelCase						)						)
  
        UpperCAmelCase__ :       Tuple						=						[str(lowerCAmelCase						) for i in encrypt_message(lowerCAmelCase ,  (n, e) ,  lowerCAmelCase						)]
  
        UpperCAmelCase__ :       str						=						",".join(lowerCAmelCase						)
        UpperCAmelCase__ :       Tuple						=						F"""{len(lowerCAmelCase						)}_{block_size}_{encrypted_content}"""
        with open(lowerCAmelCase ,  """w"""						) as fo:
              fo.write(lowerCAmelCase						)
        return encrypted_content
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
        UpperCAmelCase__ :       Optional[Any]						=						read_key_file(lowerCAmelCase						)
        with open(lowerCAmelCase						) as fo:
              UpperCAmelCase__ :       str						=						fo.read()
        UpperCAmelCase__ :       Dict						=						content.split("""_"""						)
        UpperCAmelCase__ :       str						=						int(lowerCAmelCase						)
        UpperCAmelCase__ :       Optional[int]						=						int(lowerCAmelCase						)
  
        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 ,  lowerCAmelCase						)						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						[]
        for block in encrypted_message.split(""","""						):
              encrypted_blocks.append(int(lowerCAmelCase						)						)
  
        return decrypt_message(lowerCAmelCase ,  lowerCAmelCase ,  (n, d) ,  lowerCAmelCase						)
  
  
  
  
  def 				a__	(	)  ->   None:
        UpperCAmelCase__ :       Optional[Any]						=						"encrypted_file.txt"
        UpperCAmelCase__ :       Any						=						input(R"""Encrypt\Decrypt [e\d]: """						)
  
        if response.lower().startswith("""e"""						):
              UpperCAmelCase__ :       str						=						"encrypt"
        elif response.lower().startswith("""d"""						):
              UpperCAmelCase__ :       Union[str, Any]						=						"decrypt"
  
        if mode == "encrypt":
              if not os.path.exists("""rsa_pubkey.txt"""						):
                    rkg.make_key_files("""rsa""" ,  10_24						)
  
              UpperCAmelCase__ :       List[str]						=						input("""\nEnter message: """						)
              UpperCAmelCase__ :       List[str]						=						"rsa_pubkey.txt"
              print(F"""Encrypting and writing to {filename}..."""						)
              UpperCAmelCase__ :       Optional[int]						=						encrypt_and_write_to_file(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
              print("""\nEncrypted text:"""						)
              print(lowerCAmelCase						)
  
        elif mode == "decrypt":
              UpperCAmelCase__ :       Dict						=						"rsa_privkey.txt"
              print(F"""Reading from {filename} and decrypting..."""						)
              UpperCAmelCase__ :       int						=						read_from_file_and_decrypt(lowerCAmelCase ,  lowerCAmelCase						)
              print("""writing decryption to rsa_decryption.txt..."""						)
              with open("""rsa_decryption.txt""" ,  """w"""						) as dec:
                    dec.write(lowerCAmelCase						)
  
              print("""\nDecryption:"""						)
              print(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      main()
  
  
style_context: 
  
  """simple docstring"""
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
    SCREAMING_SNAKE_CASE							=     ()
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						4
          UpperCAmelCase__ :       Tuple						=						3
          UpperCAmelCase__ :       Optional[Any]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )
  
          return sample
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						4
          UpperCAmelCase__ :       Optional[Any]						=						3
          UpperCAmelCase__ :       List[str]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
          UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						sample / num_elems
          UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )
  
          return sample
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
                return sample * t / (t + 1)
  
          return model
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						{
              """num_train_timesteps""": 2000,
              """snr""": 0.15,
              """sigma_min""": 0.01,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }
  
          config.update(**_lowerCamelCase  )
          return config
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       int						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
          kwargs.update(_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       List[str]						=						self.dummy_sample
                UpperCAmelCase__ :       List[str]						=						0.1 * sample
  
                UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
                UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
                self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
          UpperCAmelCase__ :       int						=						self.get_scheduler_config()
          UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						3
  
          UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
          UpperCAmelCase__ :       int						=						self.dummy_sample_deter
  
          scheduler.set_sigmas(_lowerCamelCase  )
          scheduler.set_timesteps(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )
  
          for i, t in enumerate(scheduler.timesteps  ):
                UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]
  
                for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                            UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
                UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean
  
          UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
          UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )
  
          assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
          assert np.isclose(result_mean.item()       ,					18_714_530.0  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
                UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      scheduler.set_timesteps(_lowerCamelCase  )
                elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      UpperCAmelCase__ :       Dict						=						num_inference_steps
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                self.assertEqual(output_a.shape       ,					sample.shape  )
                self.assertEqual(output_a.shape       ,					output_a.shape  )
  
  