






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			(DDIMScheduler,)
							SCREAMING_SNAKE_CASE_						     =			(('eta', 0.0), ('num_inference_steps', 50))
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,**__lowerCamelCase		:					Optional[Any]					):





								'''simple docstring'''



								a		=			{
								    '''num_train_timesteps''': 10_00,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**__lowerCamelCase					)
								return config
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,**__lowerCamelCase		:					Any					):





								'''simple docstring'''



								a		=			self.scheduler_classes[0]
								a		=			self.get_scheduler_config(**__lowerCamelCase					)
								a		=			scheduler_class(**__lowerCamelCase					)

								a	, a		=			10, 0.0

								a		=			self.dummy_model()
								a		=			self.dummy_sample_deter

								scheduler.set_timesteps(__lowerCamelCase					)

								for t in scheduler.timesteps:
									a		=			model(__lowerCamelCase							,__lowerCamelCase					)
									a		=			scheduler.step(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					).prev_sample

								return sample
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								for timesteps in [1_00, 5_00, 10_00]:
									self.check_over_configs(num_train_timesteps=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=__lowerCamelCase					)

								a		=			self.scheduler_classes[0]
								a		=			self.get_scheduler_config(steps_offset=1					)
								a		=			scheduler_class(**__lowerCamelCase					)
								scheduler.set_timesteps(5					)
								assert torch.equal(scheduler.timesteps							,torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]					)					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]							,[0.002, 0.02, 0.2, 2]					):
									self.check_over_configs(beta_start=__lowerCamelCase							,beta_end=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								self.check_over_configs(thresholding=__lowerCamelCase					)
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(
										    thresholding=__lowerCamelCase							,prediction_type=__lowerCamelCase							,sample_max_value=__lowerCamelCase							,)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								for t in [1, 10, 49]:
									self.check_over_forward(time_step=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								for t, num_inference_steps in zip([1, 10, 50]							,[10, 50, 5_00]					):
									self.check_over_forward(time_step=__lowerCamelCase							,num_inference_steps=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								for t, eta in zip([1, 10, 49]							,[0.0, 0.5, 1.0]					):
									self.check_over_forward(time_step=__lowerCamelCase							,eta=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								a		=			self.scheduler_classes[0]
								a		=			self.get_scheduler_config()
								a		=			scheduler_class(**__lowerCamelCase					)

								assert torch.sum(torch.abs(scheduler._get_variance(0							,0					) - 0.0					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_20							,4_00					) - 0.14_771					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_80							,9_60					) - 0.32_460					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(0							,0					) - 0.0					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87							,4_86					) - 0.00_979					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99							,9_98					) - 0.02					)					) < 1e-5
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			self.full_loop()

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

								assert abs(result_sum.item() - 172.0_067					) < 1e-2
								assert abs(result_mean.item() - 0.223_967					) < 1e-3
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			self.full_loop(prediction_type='''v_prediction'''					)

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

								assert abs(result_sum.item() - 52.5_302					) < 1e-2
								assert abs(result_mean.item() - 0.0_684					) < 1e-3
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								a		=			self.full_loop(set_alpha_to_one=__lowerCamelCase							,beta_start=0.01					)
								a		=			torch.sum(torch.abs(__lowerCamelCase					)					)
								a		=			torch.mean(torch.abs(__lowerCamelCase					)					)

								assert abs(result_sum.item() - 149.8_295					) < 1e-2
								assert abs(result_mean.item() - 0.1_951					) < 1e-3




							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a		=			self.full_loop(set_alpha_to_one=__lowerCamelCase							,beta_start=0.01					)
								a		=			torch.sum(torch.abs(__lowerCamelCase					)					)
								a		=			torch.mean(torch.abs(__lowerCamelCase					)					)

								assert abs(result_sum.item() - 149.0_784					) < 1e-2
								assert abs(result_mean.item() - 0.1_941					) < 1e-3













import random
import sys

UpperCamelCase__						:      List[str]								=					"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def   SCREAMING_SNAKE_CASE__						(   )   ->  None:


	"""simple docstring"""



	a		=			input('''Enter message: '''  )
	a		=			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	a		=			input('''Encrypt/Decrypt [e/d]: '''  )

	check_valid_key(snake_case_  )

	if resp.lower().startswith('''e'''  ):
		a		=			'''encrypt'''
		a		=			encrypt_message(snake_case_,  snake_case_  )
	elif resp.lower().startswith('''d'''  ):
		a		=			'''decrypt'''
		a		=			decrypt_message(snake_case_,  snake_case_  )

	print(f"""\n{mode.title()}ion: \n{translated}"""  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			list(snake_case_  )
	a		=			list(snake_case_  )
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''encrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''decrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			''''''
	a		=			LETTERS
	a		=			key

	if mode == "decrypt":
		a	, a		=			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			a		=			chars_a.find(symbol.upper()  )
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated





def   SCREAMING_SNAKE_CASE__						(   )   ->  str:


	"""simple docstring"""



	a		=			list(snake_case_  )
	random.shuffle(snake_case_  )
	return "".join(snake_case_  )


if __name__ == "__main__":
				main()





