



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


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







				lowercase__				  =  (KDPMaAncestralDiscreteScheduler,)
				lowercase__				  =  10

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				{
									    "num_train_timesteps": 1100,
									    "beta_start": 0.0_001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**snake_case__      )
									return config

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''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=snake_case__       ,	beta_end=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

									scheduler.set_timesteps(self.num_inference_steps      )

									UpperCamelCase_											=				torch.manual_seed(0      )

									UpperCamelCase_											=				self.dummy_model()
									UpperCamelCase_											=				self.dummy_sample_deter * scheduler.init_noise_sigma
									UpperCamelCase_											=				sample.to(snake_case__      )

									for i, t in enumerate(scheduler.timesteps      ):
														UpperCamelCase_											=				scheduler.scale_model_input(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=snake_case__      )
														UpperCamelCase_											=				output.prev_sample

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

									assert abs(result_sum.item() - 13_849.3_877      ) < 1e-2
									assert abs(result_mean.item() - 18.0_331      ) < 5e-3

				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'''


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

									scheduler.set_timesteps(self.num_inference_steps      )

									UpperCamelCase_											=				self.dummy_model()
									UpperCamelCase_											=				self.dummy_sample_deter * scheduler.init_noise_sigma
									UpperCamelCase_											=				sample.to(snake_case__      )

									UpperCamelCase_											=				torch.manual_seed(0      )

									for i, t in enumerate(scheduler.timesteps      ):
														UpperCamelCase_											=				scheduler.scale_model_input(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=snake_case__      )
														UpperCamelCase_											=				output.prev_sample

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

									assert abs(result_sum.item() - 328.9_970      ) < 1e-2
									assert abs(result_mean.item() - 0.4_284      ) < 1e-3

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

									scheduler.set_timesteps(self.num_inference_steps       ,	device=snake_case__      )
									UpperCamelCase_											=				torch.manual_seed(0      )

									UpperCamelCase_											=				self.dummy_model()
									UpperCamelCase_											=				self.dummy_sample_deter.to(snake_case__      ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
														UpperCamelCase_											=				scheduler.scale_model_input(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=snake_case__      )
														UpperCamelCase_											=				output.prev_sample

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

									assert abs(result_sum.item() - 13_849.3_818      ) < 1e-1
									assert abs(result_mean.item() - 18.0_331      ) < 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()
