



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class  _lowercase				(a_ ):
				'''simple docstring'''







				lowercase__				  =  (DDIMScheduler,)
				lowercase__				  =  (("""eta""", 0.0), ("""num_inference_steps""", 50))

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				{
									    "num_train_timesteps": 1000,
									    "beta_start": 0.0_001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									    "clip_sample": True,
									}

									config.update(**snake_case__      )
									return config

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.scheduler_classes[0]
									UpperCamelCase_											=				self.get_scheduler_config(**snake_case__      )
									UpperCamelCase_											=				scheduler_class(**snake_case__      )

									UpperCamelCase_					,      UpperCamelCase_											=				10, 0.0

									UpperCamelCase_											=				self.dummy_model()
									UpperCamelCase_											=				self.dummy_sample_deter

									scheduler.set_timesteps(snake_case__      )

									for t in scheduler.timesteps:
														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )
														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ).prev_sample

									return sample

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for timesteps in [100, 500, 1000]:
														self.check_over_configs(num_train_timesteps=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

									UpperCamelCase_											=				self.scheduler_classes[0]
									UpperCamelCase_											=				self.get_scheduler_config(steps_offset=1      )
									UpperCamelCase_											=				scheduler_class(**snake_case__      )
									scheduler.set_timesteps(5      )
									assert torch.equal(scheduler.timesteps       ,	torch.LongTensor([801, 601, 401, 201, 1]      )      )

				def  _lowerCamelCase   (	self      ):

									'''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=snake_case__       ,	beta_end=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for t in [1, 10, 49]:
														self.check_over_forward(time_step=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for t, num_inference_steps in zip([1, 10, 50]       ,	[10, 50, 500]      ):
														self.check_over_forward(time_step=snake_case__       ,	num_inference_steps=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.scheduler_classes[0]
									UpperCamelCase_											=				self.get_scheduler_config()
									UpperCamelCase_											=				scheduler_class(**snake_case__      )

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,	0      ) - 0.0      )      ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(420       ,	400      ) - 0.14_771      )      ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(980       ,	960      ) - 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(487       ,	486      ) - 0.00_979      )      ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999       ,	998      ) - 0.02      )      ) < 1e-5

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.full_loop()

									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

									assert abs(result_sum.item() - 172.0_067      ) < 1e-2
									assert abs(result_mean.item() - 0.223_967      ) < 1e-3

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.full_loop(prediction_type="v_prediction"      )

									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

									assert abs(result_sum.item() - 52.5_302      ) < 1e-2
									assert abs(result_mean.item() - 0.0_684      ) < 1e-3

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.full_loop(set_alpha_to_one=snake_case__       ,	beta_start=0.01      )
									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.full_loop(set_alpha_to_one=snake_case__       ,	beta_start=0.01      )
									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

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





from typing import Literal

UpperCAmelCase				:							Tuple								="""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				LETTERS if mode == "decrypt" else key
					UpperCamelCase_											=				key if mode == "decrypt" else LETTERS
					UpperCamelCase_											=				""
					# loop through each symbol in the message
					for symbol in message:
										if symbol.upper() in chars_a:
															# encrypt/decrypt the symbol
															UpperCamelCase_											=				chars_a.find(symbol.upper())
															if symbol.isupper():
																				translated += chars_b[sym_index].upper()
															else:
																				translated += chars_b[sym_index].lower()
										else:
															# symbol is not in LETTERS, just add it
															translated += symbol
					return translated



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					return translate_message(_lowerCAmelCase				,		_lowerCAmelCase				,		"encrypt")



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					return translate_message(_lowerCAmelCase				,		_lowerCAmelCase				,		"decrypt")



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				"Hello World"
					UpperCamelCase_											=				"QWERTYUIOPASDFGHJKLZXCVBNM"
					UpperCamelCase_											=				"decrypt"  # set to 'encrypt' or 'decrypt'

					if mode == "encrypt":
										UpperCamelCase_											=				encrypt_message(_lowerCAmelCase				,		_lowerCAmelCase)
					elif mode == "decrypt":
										UpperCamelCase_											=				decrypt_message(_lowerCAmelCase				,		_lowerCAmelCase)
					print(f"""Using the key {key}, the {mode}ed message is: {translated}""")


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
