


'''simple docstring'''







def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






    """simple docstring"""



    if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
        raise TypeError("plaintext must be a string"		)
    if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
        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
    __magic_name__				:					List[str]						       =       plaintext.lower()
    __magic_name__				:					Tuple						       =       key.lower()
    __magic_name__				:					Dict						       =       0
    __magic_name__				:					Optional[int]						       =       0
    __magic_name__				:					List[str]						       =       ""
    while plaintext_iterator < len(UpperCamelCase__		):
        if (
            ord(plaintext[plaintext_iterator]		) < 97
            or ord(plaintext[plaintext_iterator]		) > 122
        ):
            ciphertext += plaintext[plaintext_iterator]
            plaintext_iterator += 1
        elif ord(key[key_iterator]		) < 97 or ord(key[key_iterator]		) > 122:
            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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






    """simple docstring"""



    if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
        raise TypeError("ciphertext must be a string"		)
    if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		):
        raise TypeError("key must be a string"		)

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

    __magic_name__				:					List[str]						       =       key.lower()
    __magic_name__				:					str						       =       0
    __magic_name__				:					Optional[Any]						       =       0
    __magic_name__				:					Tuple						       =       ""
    while ciphertext_iterator < len(UpperCamelCase__		):
        if (
            ord(ciphertext[ciphertext_iterator]		) < 97
            or ord(ciphertext[ciphertext_iterator]		) > 122
        ):
            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()
  _SCREAMING_SNAKE_CASE       :   Tuple        = int(input("Type 1 to encrypt or 2 to decrypt:"))
  if operation == 1:
    _SCREAMING_SNAKE_CASE       :   Optional[Any]        = input("Typeplaintext to be encrypted:\n")
    _SCREAMING_SNAKE_CASE       :   Tuple        = input("Type the key:\n")
    print(encrypt(plaintext, key))
  elif operation == 2:
    _SCREAMING_SNAKE_CASE       :   Optional[Any]        = input("Type the ciphertext to be decrypted:\n")
    _SCREAMING_SNAKE_CASE       :   List[str]        = input("Type the key:\n")
    print(decrypt(ciphertext, key))
  decrypt("jsqqs avvwo", "coffee")



'''simple docstring'''







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      (LMSDiscreteScheduler,)
						__snake_case   =      1_0







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							**__UpperCamelCase:	Any     )							->	List[str]:
										__magic_name__				:					List[str]						       =       {
										    "num_train_timesteps": 1100,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__UpperCamelCase     )
										return config







						def lowerCAmelCase__ (  self:	Any     )							->	str:
										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	int:
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]      ,							[0.0_0_0_2, 0.0_0_2, 0.0_2]     ):
														self.check_over_configs(beta_start=__UpperCamelCase      ,							beta_end=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	Any:
										for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Dict     )							->	int:
										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[Any]:
										for t in [0, 500, 800]:
														self.check_over_forward(time_step=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	List[str]     )							->	Optional[int]:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					List[str]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					str						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Dict						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Optional[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Tuple						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Union[str, Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	int     )							->	Tuple:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					Tuple						       =       self.get_scheduler_config(prediction_type="v_prediction"     )
										__magic_name__				:					List[Any]						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					Tuple						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					int						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Dict						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Optional[Any]						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					List[Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 0.0_0_1_7     ) < 1E-2
										assert abs(result_mean.item() - 2.2_676E-06     ) < 1E-3







						def lowerCAmelCase__ (  self:	Union[str, Any]     )							->	str:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					List[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Any						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps      ,							device=__UpperCamelCase     )

										__magic_name__				:					Optional[Any]						       =       self.dummy_model()
										__magic_name__				:					Union[str, Any]						       =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										__magic_name__				:					int						       =       sample.to(__UpperCamelCase     )

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Optional[int]						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					List[Any]						       =       output.prev_sample

										__magic_name__				:					Any						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					int						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					Optional[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase      ,							use_karras_sigmas=__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps      ,							device=__UpperCamelCase     )

										__magic_name__				:					int						       =       self.dummy_model()
										__magic_name__				:					Optional[int]						       =       self.dummy_sample_deter.to(__UpperCamelCase     ) * scheduler.init_noise_sigma
										__magic_name__				:					List[str]						       =       sample.to(__UpperCamelCase     )

										for t in scheduler.timesteps:
														__magic_name__				:					str						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					List[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					str						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					int						       =       output.prev_sample

										__magic_name__				:					str						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Dict						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 3_8_1_2.9_9_2_7     ) < 2E-2
										assert abs(result_mean.item() - 4.9_6_4_8     ) < 1E-3