



"""simple docstring"""





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	(CMStochasticIterativeScheduler,)
       UpperCamelCase		=	10



       def 	lowercase__							(				self     :    Optional[int]						,							**_UpperCAmelCase     :    Dict       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				{
                 "num_train_timesteps": 201,
                 "sigma_min": 0.002,
                 "sigma_max": 80.0,
             }

             config.update(**_UpperCAmelCase       )
             return config



       def 	lowercase__							(				self     :    Dict       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				10

             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				self.scheduler_classes[0](**_UpperCAmelCase       )

             scheduler.set_timesteps(_UpperCAmelCase       )

             UpperCAmelCase_     					=				scheduler.timesteps[0]
             UpperCAmelCase_     					=				scheduler.timesteps[1]

             UpperCAmelCase_     					=				self.dummy_sample
             UpperCAmelCase_     					=				0.1 * sample

             UpperCAmelCase_     					=				scheduler.step(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       ).prev_sample
             UpperCAmelCase_     					=				scheduler.step(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       ).prev_sample

             self.assertEqual(output_a.shape						,							sample.shape       )
             self.assertEqual(output_a.shape						,							output_a.shape       )



       def 	lowercase__							(				self     :    List[Any]       )					->		Tuple:


             '''simple docstring'''




             for timesteps in [10, 50, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=_UpperCAmelCase       )



       def 	lowercase__							(				self     :    str       )					->		Union[str, Any]:


             '''simple docstring'''




             for clip_denoised in [True, False]:
                   self.check_over_configs(clip_denoised=_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Union[str, Any]       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				1
             scheduler.set_timesteps(_UpperCAmelCase       )
             UpperCAmelCase_     					=				scheduler.timesteps

             UpperCAmelCase_     					=				torch.manual_seed(0       )

             UpperCAmelCase_     					=				self.dummy_model()
             UpperCAmelCase_     					=				self.dummy_sample_deter * scheduler.init_noise_sigma

             for i, t in enumerate(_UpperCAmelCase       ):
                   # 1. scale model input
                   UpperCAmelCase_     					=				scheduler.scale_model_input(_UpperCAmelCase						,							_UpperCAmelCase       )

                   # 2. predict noise residual
                   UpperCAmelCase_     					=				model(_UpperCAmelCase						,							_UpperCAmelCase       )

                   # 3. predict previous sample x_t-1
                   UpperCAmelCase_     					=				scheduler.step(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							generator=_UpperCAmelCase       ).prev_sample

                   UpperCAmelCase_     					=				pred_prev_sample

             UpperCAmelCase_     					=				torch.sum(torch.abs(_UpperCAmelCase       )       )
             UpperCAmelCase_     					=				torch.mean(torch.abs(_UpperCAmelCase       )       )

             assert abs(result_sum.item() - 192.7614       ) < 1e-2
             assert abs(result_mean.item() - 0.2510       ) < 1e-3



       def 	lowercase__							(				self     :    List[str]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				[106, 0]
             scheduler.set_timesteps(timesteps=_UpperCAmelCase       )
             UpperCAmelCase_     					=				scheduler.timesteps

             UpperCAmelCase_     					=				torch.manual_seed(0       )

             UpperCAmelCase_     					=				self.dummy_model()
             UpperCAmelCase_     					=				self.dummy_sample_deter * scheduler.init_noise_sigma

             for t in timesteps:
                   # 1. scale model input
                   UpperCAmelCase_     					=				scheduler.scale_model_input(_UpperCAmelCase						,							_UpperCAmelCase       )

                   # 2. predict noise residual
                   UpperCAmelCase_     					=				model(_UpperCAmelCase						,							_UpperCAmelCase       )

                   # 3. predict previous sample x_t-1
                   UpperCAmelCase_     					=				scheduler.step(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							generator=_UpperCAmelCase       ).prev_sample

                   UpperCAmelCase_     					=				pred_prev_sample

             UpperCAmelCase_     					=				torch.sum(torch.abs(_UpperCAmelCase       )       )
             UpperCAmelCase_     					=				torch.mean(torch.abs(_UpperCAmelCase       )       )

             assert abs(result_sum.item() - 347.6357       ) < 1e-2
             assert abs(result_mean.item() - 0.4527       ) < 1e-3



       def 	lowercase__							(				self     :    Tuple       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				[39, 30, 12, 15, 0]

             with self.assertRaises(_UpperCAmelCase						,							msg="`timesteps` must be in descending order."       ):
                   scheduler.set_timesteps(timesteps=_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Any       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				[39, 30, 12, 1, 0]
             UpperCAmelCase_     					=				len(_UpperCAmelCase       )

             with self.assertRaises(_UpperCAmelCase						,							msg="Can only pass one of `num_inference_steps` or `timesteps`."       ):
                   scheduler.set_timesteps(num_inference_steps=_UpperCAmelCase						,							timesteps=_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Union[str, Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				[scheduler.config.num_train_timesteps]

             with self.assertRaises(
                 _UpperCAmelCase						,							msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"						,							):
                   scheduler.set_timesteps(timesteps=_UpperCAmelCase       )






import string

import numpy
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int:
       return b if a == 0 else greatest_common_divisor(b % a			,							UpperCAmelCase_				)


class 			lowercase_		:
       __lowerCamelCase				       =						string.ascii_uppercase + string.digits
       # This cipher takes alphanumerics into account
       # i.e. a total of 36 characters

       # take x and return x % len(key_string)
       __lowerCamelCase				       =						numpy.vectorize(lambda A       : x % 3_6       )

       __lowerCamelCase				       =						numpy.vectorize(A       )


       def __init__(    self						,       __A				)						->    None:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.modulus(__A				)  # mod36 calc's on the encrypt key
              self.check_determinant()  # validate the determinant of the encryption key
              SCREAMING_SNAKE_CASE_							:				Dict		=encrypt_key.shape[0]


       def 	_snake_case				(    self						,       __A				)						->    int:
              return self.key_string.index(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              return self.key_string[round(__A				)]


       def 	_snake_case				(    self				)						->    None:
              SCREAMING_SNAKE_CASE_							:				List[str]		=round(numpy.linalg.det(self.encrypt_key				)				)

              if det < 0:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=det % len(self.key_string				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=len(self.key_string				)
              if greatest_common_divisor(__A						,       len(self.key_string				)				) != 1:
                     SCREAMING_SNAKE_CASE_							:				Any		=(
                         F'determinant modular {req_l} of encryption key({det}) '
                         F'is not co prime w.r.t {req_l}.\nTry another key.'
                     )
                     raise ValueError(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[char for char in text.upper() if char in self.key_string]

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=chars[-1]
              while len(__A				) % self.break_key != 0:
                     chars.append(__A				)

              return "".join(__A				)


       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.process_text(text.upper()				)
              SCREAMING_SNAKE_CASE_							:				Dict		=''''''

              for i in range(0						,       len(__A				) - self.break_key + 1						,       self.break_key				):
                     SCREAMING_SNAKE_CASE_							:				Any		=text[i : i + self.break_key]
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=[self.replace_letters(__A				) for char in batch]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=numpy.array([vec]				).T
                     SCREAMING_SNAKE_CASE_							:				List[str]		=self.modulus(self.encrypt_key.dot(__A				)				).T.tolist()[
                         0
                     ]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=''''''.join(
                         self.replace_digits(__A				) for num in batch_encrypted				)
                     encrypted += encrypted_batch

              return encrypted


       def 	_snake_case				(    self				)						->    numpy.ndarray:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=round(numpy.linalg.det(self.encrypt_key				)				)

              if det < 0:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=det % len(self.key_string				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=None
              for i in range(len(self.key_string				)				):
                     if (det * i) % len(self.key_string				) == 1:
                            SCREAMING_SNAKE_CASE_							:				List[str]		=i
                            break

              SCREAMING_SNAKE_CASE_							:				List[str]		=(
                  det_inv
                  * numpy.linalg.det(self.encrypt_key				)
                  * numpy.linalg.inv(self.encrypt_key				)
              )

              return self.to_int(self.modulus(__A				)				)





       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				int		=self.make_decrypt_key()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.process_text(text.upper()				)
              SCREAMING_SNAKE_CASE_							:				Any		=''''''

              for i in range(0						,       len(__A				) - self.break_key + 1						,       self.break_key				):
                     SCREAMING_SNAKE_CASE_							:				List[str]		=text[i : i + self.break_key]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[self.replace_letters(__A				) for char in batch]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=numpy.array([vec]				).T
                     SCREAMING_SNAKE_CASE_							:				str		=self.modulus(decrypt_key.dot(__A				)				).T.tolist()[0]
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=''''''.join(
                         self.replace_digits(__A				) for num in batch_decrypted				)
                     decrypted += decrypted_batch

              return decrypted






def 							SCREAMING_SNAKE_CASE_	(					)			->						None:
       SCREAMING_SNAKE_CASE_							:				List[str]		=int(input('''Enter the order of the encryption key: '''				)				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[]

       print('''Enter each row of the encryption key with space separated integers'''				)
       for _ in range(UpperCAmelCase_				):
              SCREAMING_SNAKE_CASE_							:				Tuple		=[int(UpperCAmelCase_				) for x in input().split()]
              hill_matrix.append(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=HillCipher(numpy.array(UpperCAmelCase_				)				)

       print('''Would you like to encrypt or decrypt some text? (1 or 2)'''				)
       SCREAMING_SNAKE_CASE_							:				Dict		=input('''\n1. Encrypt\n2. Decrypt\n'''				)
       if option == "1":
              SCREAMING_SNAKE_CASE_							:				List[Any]		=input('''What text would you like to encrypt?: '''				)
              print('''Your encrypted text is:'''				)
              print(hc.encrypt(UpperCAmelCase_				)				)
       elif option == "2":
              SCREAMING_SNAKE_CASE_							:				Any		=input('''What text would you like to decrypt?: '''				)
              print('''Your decrypted text is:'''				)
              print(hc.decrypt(UpperCAmelCase_				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
