


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						(DDPMScheduler,)




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    **_a					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ={
										    '''num_train_timesteps''': 1000,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''variance_type''': '''fixed_small''',
										    '''clip_sample''': True,
										}

										config.update(**_a  )
										return config




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


										"""simple docstring"""





										for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=_a  )




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


										"""simple docstring"""





										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,    [0.0_02, 0.02, 0.2, 2]  ):
														self.check_over_configs(beta_start=_a				,    beta_end=_a  )




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


										"""simple docstring"""





										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=_a  )




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


										"""simple docstring"""





										for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=_a  )




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


										"""simple docstring"""





										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=_a  )




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


										"""simple docstring"""





										self.check_over_configs(thresholding=_a  )
										for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=_a				,    prediction_type=_a				,    sample_max_value=_a				,    )




						def 							__UpperCamelCase				(       self					:      str  )							->     Any:


										"""simple docstring"""





										for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(prediction_type=_a  )




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


										"""simple docstring"""





										for t in [0, 500, 999]:
														self.check_over_forward(time_step=_a  )




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_09_79  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5




						def 							__UpperCamelCase				(       self					:      str  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =len(_a  )

										_SCREAMING_SNAKE_CASE			  =self.dummy_model()
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample_deter
										_SCREAMING_SNAKE_CASE			  =torch.manual_seed(0  )

										for t in reversed(range(_a  )  ):
														# 1. predict noise residual
														_SCREAMING_SNAKE_CASE			  =model(_a				,    _a  )

														# 2. predict previous mean of sample x_t-1
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    generator=_a  ).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_SCREAMING_SNAKE_CASE			  =pred_prev_sample

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 2_58.96_06  ) < 1E-2
										assert abs(result_mean.item() - 0.33_72  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config(prediction_type='''v_prediction'''  )
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =len(_a  )

										_SCREAMING_SNAKE_CASE			  =self.dummy_model()
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample_deter
										_SCREAMING_SNAKE_CASE			  =torch.manual_seed(0  )

										for t in reversed(range(_a  )  ):
														# 1. predict noise residual
														_SCREAMING_SNAKE_CASE			  =model(_a				,    _a  )

														# 2. predict previous mean of sample x_t-1
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    generator=_a  ).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_SCREAMING_SNAKE_CASE			  =pred_prev_sample

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 2_02.02_96  ) < 1E-2
										assert abs(result_mean.item() - 0.26_31  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      Any  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=_a  )

										_SCREAMING_SNAKE_CASE			  =scheduler.timesteps

										for i, timestep in enumerate(_a  ):
														if i == len(_a  ) - 1:
																		_SCREAMING_SNAKE_CASE			  =-1
														else:
																		_SCREAMING_SNAKE_CASE			  =timesteps[i + 1]

														_SCREAMING_SNAKE_CASE			  =scheduler.previous_timestep(_a  )
														_SCREAMING_SNAKE_CASE			  =prev_t.item()

														self.assertEqual(_a				,    _a  )




						def 							__UpperCamelCase				(       self					:      str  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[100, 87, 50, 51, 0]

										with self.assertRaises(_a				,    msg='''`custom_timesteps` must be in descending order.'''  ):
														scheduler.set_timesteps(timesteps=_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[100, 87, 50, 1, 0]
										_SCREAMING_SNAKE_CASE			  =len(_a  )

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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[scheduler.config.num_train_timesteps]

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



import string

import numpy







def 						lowerCamelCase(   a__	,a__):
				return b if a == 0 else greatest_common_divisor(b % a	,a__)

class 						A__							:
						UpperCAmelCase             =						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)
						UpperCAmelCase             =						numpy.vectorize(lambda UpperCamelCase__  : x % 36  )

						UpperCAmelCase             =						numpy.vectorize(UpperCamelCase__  )




						def __init__(       self					:      int				,    _a					:      numpy.ndarray  )							->     None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.modulus(_a  )  # mod36 calc's on the encrypt key
										self.check_determinant()  # validate the determinant of the encryption key
										_SCREAMING_SNAKE_CASE			  =encrypt_key.shape[0]




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      str  )							->     int:


										"""simple docstring"""





										return self.key_string.index(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      int  )							->     str:


										"""simple docstring"""





										return self.key_string[round(_a  )]




						def 							__UpperCamelCase				(       self					:      int  )							->     None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =round(numpy.linalg.det(self.encrypt_key  )  )

										if det < 0:
														_SCREAMING_SNAKE_CASE			  =det % len(self.key_string  )

										_SCREAMING_SNAKE_CASE			  =len(self.key_string  )
										if greatest_common_divisor(_a				,    len(self.key_string  )  ) != 1:
														_SCREAMING_SNAKE_CASE			  =(
														    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 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =[char for char in text.upper() if char in self.key_string]

										_SCREAMING_SNAKE_CASE			  =chars[-1]
										while len(_a  ) % self.break_key != 0:
														chars.append(_a  )

										return "".join(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.process_text(text.upper()  )
										_SCREAMING_SNAKE_CASE			  =''''''

										for i in range(0				,    len(_a  ) - self.break_key + 1				,    self.break_key  ):
														_SCREAMING_SNAKE_CASE			  =text[i : i + self.break_key]
														_SCREAMING_SNAKE_CASE			  =[self.replace_letters(_a  ) for char in batch]
														_SCREAMING_SNAKE_CASE			  =numpy.array([vec]  ).T
														_SCREAMING_SNAKE_CASE			  =self.modulus(self.encrypt_key.dot(_a  )  ).T.tolist()[
														    0
														]
														_SCREAMING_SNAKE_CASE			  =''''''.join(
														    self.replace_digits(_a  ) for num in batch_encrypted  )
														encrypted += encrypted_batch

										return encrypted




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     numpy.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =round(numpy.linalg.det(self.encrypt_key  )  )

										if det < 0:
														_SCREAMING_SNAKE_CASE			  =det % len(self.key_string  )
										_SCREAMING_SNAKE_CASE			  =None
										for i in range(len(self.key_string  )  ):
														if (det * i) % len(self.key_string  ) == 1:
																		_SCREAMING_SNAKE_CASE			  =i
																		break

										_SCREAMING_SNAKE_CASE			  =(
										    det_inv
										    * numpy.linalg.det(self.encrypt_key  )
										    * numpy.linalg.inv(self.encrypt_key  )
										)

										return self.to_int(self.modulus(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.make_decrypt_key()
										_SCREAMING_SNAKE_CASE			  =self.process_text(text.upper()  )
										_SCREAMING_SNAKE_CASE			  =''''''

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

										return decrypted







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =int(input('''Enter the order of the encryption key: '''))
				_SCREAMING_SNAKE_CASE			  =[]

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

				_SCREAMING_SNAKE_CASE			  =HillCipher(numpy.array(a__))

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()