
"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      List[str]						  =				(HeunDiscreteScheduler,)
				UpperCAmelCase_				:      int						  =				10


				def 							A					(				self		,	**lowercase__			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	{
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**lowercase__			)
									return config


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    Any:
									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]			):
														self.check_over_configs(beta_start=lowercase__		,	beta_end=lowercase__			)


				def 							A					(				self			)    ->    Optional[int]:
									"""simple docstring"""







									for schedule in ["linear", "scaled_linear", "exp"]:
														self.check_over_configs(beta_schedule=lowercase__			)


				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									for clip_sample_range in [1.0, 2.0, 3.0]:
														self.check_over_configs(clip_sample_range=lowercase__		,	clip_sample=lowercase__			)


				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE				=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				=	scheduler_class(**lowercase__			)

									scheduler.set_timesteps(self.num_inference_steps			)

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE				=	sample.to(lowercase__			)

									for i, t in enumerate(scheduler.timesteps			):
														SCREAMING_SNAKE_CASE				=	scheduler.scale_model_input(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3
									else:
														# CUDA
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3


				def 							A					(				self			)    ->    Union[str, 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(**lowercase__			)

									scheduler.set_timesteps(self.num_inference_steps			)

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE				=	sample.to(lowercase__			)

									for i, t in enumerate(scheduler.timesteps			):
														SCREAMING_SNAKE_CASE				=	scheduler.scale_model_input(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6_934E-07			) < 1E-2
														assert abs(result_mean.item() - 6.1_112E-10			) < 1E-3
									else:
														# CUDA
														assert abs(result_sum.item() - 4.693_428_650_170_972E-07			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3


				def 							A					(				self			)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE				=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				=	scheduler_class(**lowercase__			)

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

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
														SCREAMING_SNAKE_CASE				=	scheduler.scale_model_input(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									if str(lowercase__			).startswith('cpu'			):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3
									elif str(lowercase__			).startswith('mps'			):
														# Larger tolerance on mps
														assert abs(result_mean.item() - 0.0_002			) < 1E-2
									else:
														# CUDA
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3




				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE				=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				=	scheduler_class(**lowercase__		,	use_karras_sigmas=lowercase__			)

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

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE				=	sample.to(lowercase__			)

									for t in scheduler.timesteps:
														SCREAMING_SNAKE_CASE				=	scheduler.scale_model_input(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									assert abs(result_sum.item() - 0.00_015			) < 1E-2
									assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07			) < 1E-2







"""simple docstring"""




UpperCamelCase_           ={
    """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_				(							_lowercase   ):
						return [alphabet[char] for char in key.upper()]





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Optional[Any]     =    ''''''
						_UpperCamelCase			:					Tuple     =    0
						_UpperCamelCase			:					Optional[int]     =    generate_table(_lowercase   )
						for char in words.upper():
												cipher += get_opponent(table[count]       ,						_lowercase   )
												_UpperCamelCase			:					Optional[int]     =    (count + 1) % len(_lowercase   )
						return cipher





def        a_				(							_lowercase       ,						_lowercase   ):
						return encrypt(_lowercase       ,						_lowercase   )





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Dict     =    0 if char in table[0] else 1
						_UpperCamelCase			:					str     =    table[row].index(_lowercase   )
						return row, col





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    get_position(_lowercase       ,						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_           =input("""Enter key: """).strip()
						UpperCamelCase_           =input("""Enter text to encrypt: """).strip()
						UpperCamelCase_           =encrypt(key, text)

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



