




import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 	__lowerCAmelCase			(			__magic_name__				):
       UpperCamelCase__        =						(KDPMaAncestralDiscreteScheduler,)
       UpperCamelCase__        =						10
       def    lowerCamelCase__							(		self		:Any	,   **__magic_name__		:List[Any]       ):







        '''simple docstring'''







        a		       =							{
            'num_train_timesteps': 1100,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**UpperCAmelCase_       )
        return config
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=UpperCAmelCase_       )
       def    lowerCamelCase__							(		self		:List[str]       ):







        '''simple docstring'''







        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]	,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=UpperCAmelCase_	,   beta_end=UpperCAmelCase_       )
       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=UpperCAmelCase_       )
       def    lowerCamelCase__							(		self		:int       ):







        '''simple docstring'''







        if torch_device == "mps":
         return
        a		       =							self.scheduler_classes[0]
        a		       =							self.get_scheduler_config()
        a		       =							scheduler_class(**UpperCAmelCase_       )

        scheduler.set_timesteps(self.num_inference_steps       )

        a		       =							torch.manual_seed(0       )

        a		       =							self.dummy_model()
        a		       =							self.dummy_sample_deter * scheduler.init_noise_sigma
        a		       =							sample.to(UpperCAmelCase_       )

        for i, t in enumerate(scheduler.timesteps       ):
         a		       =							scheduler.scale_model_input(UpperCAmelCase_	,   UpperCAmelCase_       )

         a		       =							model(UpperCAmelCase_	,   UpperCAmelCase_       )

         a		       =							scheduler.step(UpperCAmelCase_	,   UpperCAmelCase_	,   UpperCAmelCase_	,   generator=UpperCAmelCase_       )
         a		       =							output.prev_sample

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

        assert abs(result_sum.item() - 1_3849.3877       ) < 1E-2
        assert abs(result_mean.item() - 18.0331       ) < 5E-3
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=UpperCAmelCase_       )
       def    lowerCamelCase__							(		self		:List[str]       ):







        '''simple docstring'''







        if torch_device == "mps":
         return
        a		       =							self.scheduler_classes[0]
        a		       =							self.get_scheduler_config(prediction_type="""v_prediction"""       )
        a		       =							scheduler_class(**UpperCAmelCase_       )

        scheduler.set_timesteps(self.num_inference_steps       )

        a		       =							self.dummy_model()
        a		       =							self.dummy_sample_deter * scheduler.init_noise_sigma
        a		       =							sample.to(UpperCAmelCase_       )

        a		       =							torch.manual_seed(0       )

        for i, t in enumerate(scheduler.timesteps       ):
         a		       =							scheduler.scale_model_input(UpperCAmelCase_	,   UpperCAmelCase_       )

         a		       =							model(UpperCAmelCase_	,   UpperCAmelCase_       )

         a		       =							scheduler.step(UpperCAmelCase_	,   UpperCAmelCase_	,   UpperCAmelCase_	,   generator=UpperCAmelCase_       )
         a		       =							output.prev_sample

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

        assert abs(result_sum.item() - 328.9970       ) < 1E-2
        assert abs(result_mean.item() - 0.4284       ) < 1E-3





       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        if torch_device == "mps":
         return
        a		       =							self.scheduler_classes[0]
        a		       =							self.get_scheduler_config()
        a		       =							scheduler_class(**UpperCAmelCase_       )

        scheduler.set_timesteps(self.num_inference_steps	,   device=UpperCAmelCase_       )
        a		       =							torch.manual_seed(0       )

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

        for t in scheduler.timesteps:
         a		       =							scheduler.scale_model_input(UpperCAmelCase_	,   UpperCAmelCase_       )

         a		       =							model(UpperCAmelCase_	,   UpperCAmelCase_       )

         a		       =							scheduler.step(UpperCAmelCase_	,   UpperCAmelCase_	,   UpperCAmelCase_	,   generator=UpperCAmelCase_       )
         a		       =							output.prev_sample

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

        assert abs(result_sum.item() - 1_3849.3818       ) < 1E-1
        assert abs(result_mean.item() - 18.0331       ) < 1E-3







__UpperCamelCase      : Union[str, Any]					      =   {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}





def 	__A	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    char.upper()					)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
		import doctest

		doctest.testmod()  # Fist ensure that all our tests are passing...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

		print(F'Encrypted: {cipher_text}')
		print(F'Decrypted with key: {decrypt(key, cipher_text)}')

