'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							(DDIMParallelScheduler,)
		lowercase   :      Union[str, Any]       =							(('eta', 0.0), ('num_inference_steps', 50))






		def 		UpperCamelCase__       (   self		,						**__A				) ->     List[str]:
								_lowerCAmelCase										={
								    'num_train_timesteps': 1000,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'clip_sample': True,
								}

								config.update(**__A				)
								return config






		def 		UpperCamelCase__       (   self		,						**__A				) ->     str:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config(**__A				)
								_lowerCAmelCase										=scheduler_class(**__A				)

								_lowerCAmelCase						,				_lowerCAmelCase										=10, 0.0

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter

								scheduler.set_timesteps(__A				)

								for t in scheduler.timesteps:
														_lowerCAmelCase										=model(__A		,						__A				)
														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A		,						__A				).prev_sample

								return sample






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								for timesteps in [100, 500, 1000]:
														self.check_over_configs(num_train_timesteps=__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__A				)

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






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								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=__A		,						beta_end=__A				)






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






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__A				)






		def 		UpperCamelCase__       (   self				) ->     Dict:
								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=__A				)






		def 		UpperCamelCase__       (   self				) ->     Dict:
								for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=__A				)






		def 		UpperCamelCase__       (   self				) ->     int:
								for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								self.check_over_configs(thresholding=__A				)
								for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(
																				    thresholding=__A		,						prediction_type=__A		,						sample_max_value=__A		,						)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								for t in [1, 10, 49]:
														self.check_over_forward(time_step=__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								for t, num_inference_steps in zip([1, 10, 50]		,						[10, 50, 500]				):
														self.check_over_forward(time_step=__A		,						num_inference_steps=__A				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								for t, eta in zip([1, 10, 49]		,						[0.0, 0.5, 1.0]				):
														self.check_over_forward(time_step=__A		,						eta=__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A				)

								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 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A				)

								_lowerCAmelCase						,				_lowerCAmelCase										=10, 0.0
								scheduler.set_timesteps(__A				)

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter
								_lowerCAmelCase										=self.dummy_sample_deter + 0.1
								_lowerCAmelCase										=self.dummy_sample_deter - 0.1

								_lowerCAmelCase										=samplea.shape[0]
								_lowerCAmelCase										=torch.stack([samplea, samplea, samplea]		,						dim=0				)
								_lowerCAmelCase										=torch.arange(__A				)[0:3, None].repeat(1		,						__A				)

								_lowerCAmelCase										=model(samples.flatten(0		,						1				)		,						timesteps.flatten(0		,						1				)				)
								_lowerCAmelCase										=scheduler.batch_step_no_noise(__A		,						timesteps.flatten(0		,						1				)		,						samples.flatten(0		,						1				)		,						__A				)

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								assert abs(result_sum.item() - 1_147.7_904				) < 1E-2
								assert abs(result_mean.item() - 0.4_982				) < 1E-3






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.full_loop()

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

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






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.full_loop(prediction_type='v_prediction'				)

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

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






		def 		UpperCamelCase__       (   self				) ->     Any:
								# We specify different beta, so that the first alpha is 0.99
								_lowerCAmelCase										=self.full_loop(set_alpha_to_one=__A		,						beta_start=0.01				)
								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

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






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								# We specify different beta, so that the first alpha is 0.99
								_lowerCAmelCase										=self.full_loop(set_alpha_to_one=__A		,						beta_start=0.01				)
								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

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






'''simple docstring'''

lowercase_     		= {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase_     		= {value: key for key, value in MORSE_CODE_DICT.items()}



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return " ".join(MORSE_CODE_DICT[char] for char in message.upper()      )



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return "".join(REVERSE_DICT[char] for char in message.split()      )



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										='Morse code here!'
						print(a__      )
						_lowerCAmelCase										=encrypt(a__      )
						print(a__      )
						_lowerCAmelCase										=decrypt(a__      )
						print(a__      )


if __name__ == "__main__":
							main()





