





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    (KDPMaDiscreteScheduler,)
					A__				 =    1_0





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       {
										    "num_train_timesteps": 1100,
										    "beta_start": 0.0_001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**_lowerCAmelCase	)
										return config





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





					def 					__magic_name__ ( self	):
										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=_lowerCAmelCase	,  beta_end=_lowerCAmelCase	)





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





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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.scheduler_classes[0]
										lowerCamelCase__       =       self.get_scheduler_config(prediction_type="v_prediction"	)
										lowerCamelCase__       =       scheduler_class(**_lowerCAmelCase	)

										scheduler.set_timesteps(self.num_inference_steps	)

										lowerCamelCase__       =       self.dummy_model()
										lowerCamelCase__       =       self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase__       =       sample.to(_lowerCAmelCase	)

										for i, t in enumerate(scheduler.timesteps	):
															lowerCamelCase__       =       scheduler.scale_model_input(_lowerCAmelCase	,  _lowerCAmelCase	)

															lowerCamelCase__       =       model(_lowerCAmelCase	,  _lowerCAmelCase	)

															lowerCamelCase__       =       scheduler.step(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       output.prev_sample

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

										if torch_device in ["cpu", "mps"]:
															assert abs(result_sum.item() - 4.69_34E-07	) < 1E-2
															assert abs(result_mean.item() - 6.11_12E-10	) < 1E-3
										else:
															# CUDA
															assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07	) < 1E-2
															assert abs(result_mean.item() - 0.0_002	) < 1E-3





					def 					__magic_name__ ( self	):
										if torch_device == "mps":
															return
										lowerCamelCase__       =       self.scheduler_classes[0]
										lowerCamelCase__       =       self.get_scheduler_config()
										lowerCamelCase__       =       scheduler_class(**_lowerCAmelCase	)

										scheduler.set_timesteps(self.num_inference_steps	)

										lowerCamelCase__       =       self.dummy_model()
										lowerCamelCase__       =       self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase__       =       sample.to(_lowerCAmelCase	)

										for i, t in enumerate(scheduler.timesteps	):
															lowerCamelCase__       =       scheduler.scale_model_input(_lowerCAmelCase	,  _lowerCAmelCase	)

															lowerCamelCase__       =       model(_lowerCAmelCase	,  _lowerCAmelCase	)

															lowerCamelCase__       =       scheduler.step(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       output.prev_sample

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

										if torch_device in ["cpu", "mps"]:
															assert abs(result_sum.item() - 20.4_125	) < 1E-2
															assert abs(result_mean.item() - 0.0_266	) < 1E-3
										else:
															# CUDA
															assert abs(result_sum.item() - 20.4_125	) < 1E-2
															assert abs(result_mean.item() - 0.0_266	) < 1E-3






					def 					__magic_name__ ( self	):
										if torch_device == "mps":
															return
										lowerCamelCase__       =       self.scheduler_classes[0]
										lowerCamelCase__       =       self.get_scheduler_config()
										lowerCamelCase__       =       scheduler_class(**_lowerCAmelCase	)

										scheduler.set_timesteps(self.num_inference_steps	,  device=_lowerCAmelCase	)

										lowerCamelCase__       =       self.dummy_model()
										lowerCamelCase__       =       self.dummy_sample_deter.to(_lowerCAmelCase	) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
															lowerCamelCase__       =       scheduler.scale_model_input(_lowerCAmelCase	,  _lowerCAmelCase	)

															lowerCamelCase__       =       model(_lowerCAmelCase	,  _lowerCAmelCase	)

															lowerCamelCase__       =       scheduler.step(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       output.prev_sample

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

										if str(_lowerCAmelCase	).startswith("cpu"	):
															# The following sum varies between 148 and 156 on mps. Why?
															assert abs(result_sum.item() - 20.4_125	) < 1E-2
															assert abs(result_mean.item() - 0.0_266	) < 1E-3
										else:
															# CUDA
															assert abs(result_sum.item() - 20.4_125	) < 1E-2
															assert abs(result_mean.item() - 0.0_266	) < 1E-3








def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [1]
					for i in range(2,     a):
										factorials.append(factorials[-1] * i)
					assert 0 <= k < factorials[-1] * n, "k out of bounds"

					lowerCamelCase__       =       []
					lowerCamelCase__       =       list(range(a))

					# Find permutation
					while factorials:
										lowerCamelCase__       =       factorials.pop()
										lowerCamelCase__    ,		lowerCamelCase__       =       divmod(a,     a)
										permutation.append(elements[number])
										elements.remove(elements[number])
					permutation.append(elements[0])

					return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()

