






from __future__ import annotations

__UpperCAmelCase     							=						tuple[int, int, int]
__UpperCAmelCase     							=						tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
__UpperCAmelCase     							=						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
__UpperCAmelCase     							=						'EGZWVONAHDCLFQMSIPJBYUKXTR'
__UpperCAmelCase     							=						'FOBHMDKEXQNRAULPGSJVTYICZW'
__UpperCAmelCase     							=						'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
__UpperCAmelCase     							=						{
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
__UpperCAmelCase     							=						'RMDJXFUWGISLHVTCQNKYPBEZOA'
__UpperCAmelCase     							=						'SGLCPQWZHKXAREONTFBVIYJUDM'
__UpperCAmelCase     							=						'HVSICLTYKQUBXDWAJZOMFGPREN'
__UpperCAmelCase     							=						'RZWQHFMVDBKICJLNTUXAGYPSOE'
__UpperCAmelCase     							=						'LFKIJODBEGAMQPXVUHYSTCZRWN'
__UpperCAmelCase     							=						'KOAEGVDHXPQZMLFTYWJNBRCIUS'




def 					lowercase__				(					__snake_case   :							RotorPositionT					,	__snake_case   :							RotorSelectionT					,	__snake_case   :							str  ):


					'''simple docstring'''


					if (unique_rotsel := len(set(snake_case_  )  )) < 3:
										UpperCAmelCase_   :				int						=						F"Please use 3 unique rotors (not {unique_rotsel})"
										raise Exception(snake_case_  )

					# Checks if rotor positions are valid
					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				List[str]						=						rotpos
					if not 0 < rotorposa <= len(snake_case_  ):
										UpperCAmelCase_   :				Any						=						F"First rotor position is not within range of 1..26 ({rotorposa}"
										raise ValueError(snake_case_  )
					if not 0 < rotorposa <= len(snake_case_  ):
										UpperCAmelCase_   :				Any						=						F"Second rotor position is not within range of 1..26 ({rotorposa})"
										raise ValueError(snake_case_  )
					if not 0 < rotorposa <= len(snake_case_  ):
										UpperCAmelCase_   :				Dict						=						F"Third rotor position is not within range of 1..26 ({rotorposa})"
										raise ValueError(snake_case_  )

					# Validates string and returns dict
					UpperCAmelCase_   :				Tuple						=						_plugboard(snake_case_  )

					return rotpos, rotsel, pbdict




def 					lowercase__				(					__snake_case   :							str  ):


					'''simple docstring'''


					if not isinstance(snake_case_					,	snake_case_  ):
										UpperCAmelCase_   :				List[str]						=						F"Plugboard setting isn't type string ({type(snake_case_  )})"
										raise TypeError(snake_case_  )
					elif len(snake_case_  ) % 2 != 0:
										UpperCAmelCase_   :				Tuple						=						F"Odd number of symbols ({len(snake_case_  )})"
										raise Exception(snake_case_  )
					elif pbstring == "":
										return {}

					pbstring.replace(' '					,	''  )

					# Checks if all characters are unique
					UpperCAmelCase_   :				List[str]						=						set()
					for i in pbstring:
										if i not in abc:
															UpperCAmelCase_   :				Tuple						=						F"'{i}' not in list of symbols"
															raise Exception(snake_case_  )
										elif i in tmppbl:
															UpperCAmelCase_   :				Dict						=						F"Duplicate symbol ({i})"
															raise Exception(snake_case_  )
										else:
															tmppbl.add(snake_case_  )
					del tmppbl

					# Created the dictionary
					UpperCAmelCase_   :				Optional[Any]						=						{}
					for j in range(0					,	len(snake_case_  ) - 1					,	2  ):
										UpperCAmelCase_   :				List[Any]						=						pbstring[j + 1]
										UpperCAmelCase_   :				List[str]						=						pbstring[j]

					return pb




def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							RotorPositionT					,	__snake_case   :							RotorSelectionT = (rotora, rotora, rotora)					,	__snake_case   :							str = ""					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						text.upper()
					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						_validator(
					    snake_case_					,	snake_case_					,	plugb.upper()  )

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						rotor_position
					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						rotor_selection
					rotorposa -= 1
					rotorposa -= 1
					rotorposa -= 1

					UpperCAmelCase_   :				Dict						=						[]

					# encryption/decryption process --------------------------
					for symbol in text:
										if symbol in abc:
															# 1st plugboard --------------------------
															if symbol in plugboard:
																				UpperCAmelCase_   :				List[Any]						=						plugboard[symbol]

															# rotor ra --------------------------
															UpperCAmelCase_   :				int						=						abc.index(snake_case_  ) + rotorposa
															UpperCAmelCase_   :				List[str]						=						rotora[index % len(snake_case_  )]

															# rotor rb --------------------------
															UpperCAmelCase_   :				List[str]						=						abc.index(snake_case_  ) + rotorposa
															UpperCAmelCase_   :				Optional[int]						=						rotora[index % len(snake_case_  )]

															# rotor rc --------------------------
															UpperCAmelCase_   :				Dict						=						abc.index(snake_case_  ) + rotorposa
															UpperCAmelCase_   :				Optional[int]						=						rotora[index % len(snake_case_  )]

															# reflector --------------------------
															# this is the reason you don't need another machine to decipher

															UpperCAmelCase_   :				Tuple						=						reflector[symbol]

															# 2nd rotors
															UpperCAmelCase_   :				Tuple						=						abc[rotora.index(snake_case_  ) - rotorposa]
															UpperCAmelCase_   :				Dict						=						abc[rotora.index(snake_case_  ) - rotorposa]
															UpperCAmelCase_   :				Union[str, Any]						=						abc[rotora.index(snake_case_  ) - rotorposa]

															# 2nd plugboard
															if symbol in plugboard:
																				UpperCAmelCase_   :				Optional[Any]						=						plugboard[symbol]

															# moves/resets rotor positions
															rotorposa += 1
															if rotorposa >= len(snake_case_  ):
																				UpperCAmelCase_   :				Optional[Any]						=						0
																				rotorposa += 1
															if rotorposa >= len(snake_case_  ):
																				UpperCAmelCase_   :				int						=						0
																				rotorposa += 1
															if rotorposa >= len(snake_case_  ):
																				UpperCAmelCase_   :				int						=						0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
										result.append(snake_case_  )

					return "".join(snake_case_  )


if __name__ == "__main__":
				__UpperCAmelCase     							=						'This is my Python script that emulates the Enigma machine from WWII.'
				__UpperCAmelCase     							=						(1, 1, 1)
				__UpperCAmelCase     							=						'pictures'
				__UpperCAmelCase     							=						(rotora, rotora, rotora)
				__UpperCAmelCase     							=						enigma(message, rotor_pos, rotor_sel, pb)

				print('Encrypted message:', en)
				print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))














import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Any							=  (DDPMParallelScheduler,)



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						{
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_UpperCamelCase       )
							return config



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,		[0.0_02, 0.02, 0.2, 2]       ):
												self.check_over_configs(beta_start=_UpperCamelCase    ,		beta_end=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							self.check_over_configs(thresholding=_UpperCamelCase       )
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=_UpperCamelCase    ,		prediction_type=_UpperCamelCase    ,		sample_max_value=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							for t in [0, 5_0_0, 9_9_9]:
												self.check_over_forward(time_step=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				int						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_09_79       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1E-5



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						self.dummy_model()
							UpperCAmelCase_   :				List[Any]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						self.dummy_sample_deter + 0.1
							UpperCAmelCase_   :				str						=						self.dummy_sample_deter - 0.1

							UpperCAmelCase_   :				Tuple						=						samplea.shape[0]
							UpperCAmelCase_   :				Tuple						=						torch.stack([samplea, samplea, samplea]    ,		dim=0       )
							UpperCAmelCase_   :				str						=						torch.arange(_UpperCamelCase       )[0:3, None].repeat(1    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						model(samples.flatten(0    ,		1       )    ,		timesteps.flatten(0    ,		1       )       )
							UpperCAmelCase_   :				Dict						=						scheduler.batch_step_no_noise(_UpperCamelCase    ,		timesteps.flatten(0    ,		1       )    ,		samples.flatten(0    ,		1       )       )

							UpperCAmelCase_   :				List[Any]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 11_53.18_33       ) < 1E-2
							assert abs(result_mean.item() - 0.50_05       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Any						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						self.dummy_model()
							UpperCAmelCase_   :				Optional[int]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Union[str, Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				str						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				str						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Optional[int]						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_58.96_06       ) < 1E-2
							assert abs(result_mean.item() - 0.33_72       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[int]						=						self.get_scheduler_config(prediction_type='v_prediction'       )
							UpperCAmelCase_   :				Tuple						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						self.dummy_model()
							UpperCAmelCase_   :				List[str]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Any						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Optional[Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				Any						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				List[str]						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_02.02_96       ) < 1E-2
							assert abs(result_mean.item() - 0.26_31       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				str						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 1, 0]

							scheduler.set_timesteps(timesteps=_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						scheduler.timesteps

							for i, timestep in enumerate(_UpperCamelCase       ):
												if i == len(_UpperCamelCase       ) - 1:
																	UpperCAmelCase_   :				List[str]						=						-1
												else:
																	UpperCAmelCase_   :				int						=						timesteps[i + 1]

												UpperCAmelCase_   :				int						=						scheduler.previous_timestep(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						prev_t.item()

												self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Tuple						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 5_1, 0]

							with self.assertRaises(_UpperCamelCase    ,		msg='`custom_timesteps` must be in descending order.'       ):
												scheduler.set_timesteps(timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[str]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				List[str]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						[1_0_0, 8_7, 5_0, 1, 0]
							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

							with self.assertRaises(_UpperCamelCase    ,		msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'       ):
												scheduler.set_timesteps(num_inference_steps=_UpperCamelCase    ,		timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Optional[Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    _UpperCamelCase    ,		msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,		):
												scheduler.set_timesteps(timesteps=_UpperCamelCase       )






