






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			(KDPMaDiscreteScheduler,)
				_snake_case				:   List[Any]        =			10







				def 				__a   ( self     :List[str]  ,     **lowerCamelCase__     :Union[str, Any]     ):
					UpperCamelCase__ :int						=      {
					    """num_train_timesteps""": 11_00,
					    """beta_start""": 0.0001,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**lowerCamelCase__     )
					return config







				def 				__a   ( self     :Any     ):
					for timesteps in [10, 50, 1_00, 10_00]:
						self.check_over_configs(num_train_timesteps=lowerCamelCase__     )







				def 				__a   ( self     :List[str]     ):
					for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]  ,     [0.0002, 0.002, 0.02]     ):
						self.check_over_configs(beta_start=lowerCamelCase__  ,     beta_end=lowerCamelCase__     )







				def 				__a   ( self     :Tuple     ):
					for schedule in ["linear", "scaled_linear"]:
						self.check_over_configs(beta_schedule=lowerCamelCase__     )







				def 				__a   ( self     :List[str]     ):
					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=lowerCamelCase__     )







				def 				__a   ( self     :int     ):
					UpperCamelCase__ :Optional[int]						=      self.scheduler_classes[0]
					UpperCamelCase__ :Optional[Any]						=      self.get_scheduler_config(prediction_type="""v_prediction"""     )
					UpperCamelCase__ :int						=      scheduler_class(**lowerCamelCase__     )

					scheduler.set_timesteps(self.num_inference_steps     )

					UpperCamelCase__ :Optional[Any]						=      self.dummy_model()
					UpperCamelCase__ :List[Any]						=      self.dummy_sample_deter * scheduler.init_noise_sigma
					UpperCamelCase__ :Dict						=      sample.to(lowerCamelCase__     )

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

						UpperCamelCase__ :Dict						=      model(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :int						=      scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )
						UpperCamelCase__ :int						=      output.prev_sample

					UpperCamelCase__ :Dict						=      torch.sum(torch.abs(lowerCamelCase__     )     )
					UpperCamelCase__ :Tuple						=      torch.mean(torch.abs(lowerCamelCase__     )     )

					if torch_device in ["cpu", "mps"]:
						assert abs(result_sum.item() - 4.6934e-07     ) < 1e-2
						assert abs(result_mean.item() - 6.1112e-10     ) < 1e-3
					else:
						# CUDA
						assert abs(result_sum.item() - 4.693_4286_5017_0972e-07     ) < 1e-2
						assert abs(result_mean.item() - 0.0002     ) < 1e-3







				def 				__a   ( self     :Optional[int]     ):
					if torch_device == "mps":
						return
					UpperCamelCase__ :Optional[int]						=      self.scheduler_classes[0]
					UpperCamelCase__ :Union[str, Any]						=      self.get_scheduler_config()
					UpperCamelCase__ :List[Any]						=      scheduler_class(**lowerCamelCase__     )

					scheduler.set_timesteps(self.num_inference_steps     )

					UpperCamelCase__ :Any						=      self.dummy_model()
					UpperCamelCase__ :Tuple						=      self.dummy_sample_deter * scheduler.init_noise_sigma
					UpperCamelCase__ :Tuple						=      sample.to(lowerCamelCase__     )

					for i, t in enumerate(scheduler.timesteps     ):
						UpperCamelCase__ :List[Any]						=      scheduler.scale_model_input(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :str						=      model(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :Union[str, Any]						=      scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )
						UpperCamelCase__ :Union[str, Any]						=      output.prev_sample

					UpperCamelCase__ :Tuple						=      torch.sum(torch.abs(lowerCamelCase__     )     )
					UpperCamelCase__ :Any						=      torch.mean(torch.abs(lowerCamelCase__     )     )

					if torch_device in ["cpu", "mps"]:
						assert abs(result_sum.item() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3
					else:
						# CUDA
						assert abs(result_sum.item() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3







				def 				__a   ( self     :str     ):
					if torch_device == "mps":
						return
					UpperCamelCase__ :str						=      self.scheduler_classes[0]
					UpperCamelCase__ :int						=      self.get_scheduler_config()
					UpperCamelCase__ :List[str]						=      scheduler_class(**lowerCamelCase__     )

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

					UpperCamelCase__ :Optional[int]						=      self.dummy_model()
					UpperCamelCase__ :Optional[int]						=      self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
						UpperCamelCase__ :Union[str, Any]						=      scheduler.scale_model_input(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :List[Any]						=      model(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :Union[str, Any]						=      scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )
						UpperCamelCase__ :int						=      output.prev_sample

					UpperCamelCase__ :str						=      torch.sum(torch.abs(lowerCamelCase__     )     )
					UpperCamelCase__ :List[str]						=      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() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3
					else:
						# CUDA
						assert abs(result_sum.item() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3
"""simple docstring"""



from __future__ import annotations

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) == 0:
														return []
							A__				,							A__       :     Dict								=			min(lowerCAmelCase							), max(lowerCAmelCase							)
							A__       :     List[Any]								=			int(max_value - min_value							) + 1
							A__       :     list[list]								=			[[] for _ in range(lowerCAmelCase							)]

							for i in my_list:
														buckets[int(i - min_value							)].append(lowerCAmelCase							)

							return [v for bucket in buckets for v in sorted(lowerCAmelCase							)]


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





