'''simple docstring'''

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Tuple       =							(KDPMaDiscreteScheduler,)
		lowercase   :      Optional[Any]       =							10






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Dict:
								_lowerCAmelCase										={
								    'num_train_timesteps': 1100,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**__A				)
								return config






		def 		UpperCamelCase__       (   self				) ->     str:







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






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:







								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=__A		,						beta_end=__A				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:







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






		def 		UpperCamelCase__       (   self				) ->     Any:







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






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config(prediction_type='v_prediction'				)
								_lowerCAmelCase										=scheduler_class(**__A				)

								scheduler.set_timesteps(self.num_inference_steps				)

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

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

														_lowerCAmelCase										=model(__A		,						__A				)

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

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

								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 		UpperCamelCase__       (   self				) ->     List[str]:







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

								scheduler.set_timesteps(self.num_inference_steps				)

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

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

														_lowerCAmelCase										=model(__A		,						__A				)

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

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

								if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 20.4125				) < 1E-2
														assert abs(result_mean.item() - 0.0_266				) < 1E-3
								else:
														# CUDA
														assert abs(result_sum.item() - 20.4125				) < 1E-2
														assert abs(result_mean.item() - 0.0_266				) < 1E-3






		def 		UpperCamelCase__       (   self				) ->     Any:







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

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

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

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

														_lowerCAmelCase										=model(__A		,						__A				)

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

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

								if str(__A				).startswith('cpu'				):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 20.4125				) < 1E-2
														assert abs(result_mean.item() - 0.0_266				) < 1E-3
								else:
														# CUDA
														assert abs(result_sum.item() - 20.4125				) < 1E-2
														assert abs(result_mean.item() - 0.0_266				) < 1E-3






'''simple docstring'''

from __future__ import annotations

lowercase_     		= 10



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=1
						_lowerCAmelCase										=max(a__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												_lowerCAmelCase										=[[] for _ in range(a__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		_lowerCAmelCase										=int((i / placement) % RADIX      )
																		buckets[tmp].append(a__      )
												# put each buckets' contents into list_of_ints
												_lowerCAmelCase										=0
												for b in range(a__      ):
																		for i in buckets[b]:
																								_lowerCAmelCase										=i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
							import doctest

							doctest.testmod()





