

"""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__ :       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()       ,					14372758528.0  )
        assert np.isclose(result_mean.item()       ,					18714530.0  )




  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  )




"""simple docstring"""
def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
      if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
            raise TypeError("""plaintext must be a string"""						)
      if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
            raise TypeError("""key must be a string"""						)

      if not plaintext:
            raise ValueError("""plaintext is empty"""						)
      if not key:
            raise ValueError("""key is empty"""						)

      key += plaintext
      UpperCAmelCase__ :       Dict						=						plaintext.lower()
      UpperCAmelCase__ :       Tuple						=						key.lower()
      UpperCAmelCase__ :       Optional[int]						=						0
      UpperCAmelCase__ :       Tuple						=						0
      UpperCAmelCase__ :       Optional[int]						=						""""""
      while plaintext_iterator < len(lowerCAmelCase						):
            if (
                ord(plaintext[plaintext_iterator]						) < 97
                or ord(plaintext[plaintext_iterator]						) > 1_22
            ):
                  ciphertext += plaintext[plaintext_iterator]
                  plaintext_iterator += 1
            elif ord(key[key_iterator]						) < 97 or ord(key[key_iterator]						) > 1_22:
                  key_iterator += 1
            else:
                  ciphertext += chr(
                      (
                          (ord(plaintext[plaintext_iterator]						) - 97 + ord(key[key_iterator]						))
                          - 97
                      )
                      % 26
                      + 97						)
                  key_iterator += 1
                  plaintext_iterator += 1
      return ciphertext




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
      if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
            raise TypeError("""ciphertext must be a string"""						)
      if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
            raise TypeError("""key must be a string"""						)

      if not ciphertext:
            raise ValueError("""ciphertext is empty"""						)
      if not key:
            raise ValueError("""key is empty"""						)

      UpperCAmelCase__ :       Union[str, Any]						=						key.lower()
      UpperCAmelCase__ :       int						=						0
      UpperCAmelCase__ :       int						=						0
      UpperCAmelCase__ :       int						=						""""""
      while ciphertext_iterator < len(lowerCAmelCase						):
            if (
                ord(ciphertext[ciphertext_iterator]						) < 97
                or ord(ciphertext[ciphertext_iterator]						) > 1_22
            ):
                  plaintext += ciphertext[ciphertext_iterator]
            else:
                  plaintext += chr(
                      (ord(ciphertext[ciphertext_iterator]						) - ord(key[key_iterator]						)) % 26
                      + 97						)
                  key += chr(
                      (ord(ciphertext[ciphertext_iterator]						) - ord(key[key_iterator]						)) % 26
                      + 97						)
                  key_iterator += 1
            ciphertext_iterator += 1
      return plaintext


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    _A						=     int(input("""Type 1 to encrypt or 2 to decrypt:"""))
    if operation == 1:
        _A						=     input("""Typeplaintext to be encrypted:\n""")
        _A						=     input("""Type the key:\n""")
        print(encrypt(plaintext, key))
    elif operation == 2:
        _A						=     input("""Type the ciphertext to be decrypted:\n""")
        _A						=     input("""Type the key:\n""")
        print(decrypt(ciphertext, key))
    decrypt("""jsqqs avvwo""", """coffee""")

