




"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     (KDPMaDiscreteScheduler,)
			lowerCamelCase__ =     10
			def    A_       (    self    , **lowercase    ):
							_lowerCamelCase	:     int       =       {
							    'num_train_timesteps': 1100,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							}

							config.update(**lowercase    )
							return config
			def    A_       (    self    ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase    )
			def    A_       (    self    ):
							for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    , [0.00_02, 0.0_02, 0.02]    ):
											self.check_over_configs(beta_start=lowercase    , beta_end=lowercase    )
			def    A_       (    self    ):
							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowercase    )
			def    A_       (    self    ):
							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.scheduler_classes[0]
							_lowerCamelCase	:     int       =       self.get_scheduler_config(prediction_type='v_prediction'    )
							_lowerCamelCase	:     Dict       =       scheduler_class(**lowercase    )

							scheduler.set_timesteps(self.num_inference_steps    )

							_lowerCamelCase	:     Union[str, Any]       =       self.dummy_model()
							_lowerCamelCase	:     Optional[int]       =       self.dummy_sample_deter * scheduler.init_noise_sigma
							_lowerCamelCase	:     Optional[Any]       =       sample.to(lowercase    )

							for i, t in enumerate(scheduler.timesteps    ):
											_lowerCamelCase	:     Any       =       scheduler.scale_model_input(lowercase    , lowercase    )

											_lowerCamelCase	:     Union[str, Any]       =       model(lowercase    , lowercase    )

											_lowerCamelCase	:     Dict       =       scheduler.step(lowercase    , lowercase    , lowercase    )
											_lowerCamelCase	:     Tuple       =       output.prev_sample

							_lowerCamelCase	:     Optional[Any]       =       torch.sum(torch.abs(lowercase    )    )
							_lowerCamelCase	:     Any       =       torch.mean(torch.abs(lowercase    )    )

							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() - 0.00_02    ) < 1E-3
			def    A_       (    self    ):
							if torch_device == "mps":
											return
							_lowerCamelCase	:     Dict       =       self.scheduler_classes[0]
							_lowerCamelCase	:     Optional[int]       =       self.get_scheduler_config()
							_lowerCamelCase	:     str       =       scheduler_class(**lowercase    )

							scheduler.set_timesteps(self.num_inference_steps    )

							_lowerCamelCase	:     int       =       self.dummy_model()
							_lowerCamelCase	:     Optional[Any]       =       self.dummy_sample_deter * scheduler.init_noise_sigma
							_lowerCamelCase	:     List[str]       =       sample.to(lowercase    )

							for i, t in enumerate(scheduler.timesteps    ):
											_lowerCamelCase	:     Union[str, Any]       =       scheduler.scale_model_input(lowercase    , lowercase    )

											_lowerCamelCase	:     Dict       =       model(lowercase    , lowercase    )

											_lowerCamelCase	:     str       =       scheduler.step(lowercase    , lowercase    , lowercase    )
											_lowerCamelCase	:     Union[str, Any]       =       output.prev_sample

							_lowerCamelCase	:     Dict       =       torch.sum(torch.abs(lowercase    )    )
							_lowerCamelCase	:     List[str]       =       torch.mean(torch.abs(lowercase    )    )

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.41_25    ) < 1E-2
											assert abs(result_mean.item() - 0.02_66    ) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.41_25    ) < 1E-2
											assert abs(result_mean.item() - 0.02_66    ) < 1E-3






			def    A_       (    self    ):
							if torch_device == "mps":
											return
							_lowerCamelCase	:     Any       =       self.scheduler_classes[0]
							_lowerCamelCase	:     Dict       =       self.get_scheduler_config()
							_lowerCamelCase	:     Tuple       =       scheduler_class(**lowercase    )

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

							_lowerCamelCase	:     Tuple       =       self.dummy_model()
							_lowerCamelCase	:     Optional[Any]       =       self.dummy_sample_deter.to(lowercase    ) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											_lowerCamelCase	:     List[str]       =       scheduler.scale_model_input(lowercase    , lowercase    )

											_lowerCamelCase	:     List[str]       =       model(lowercase    , lowercase    )

											_lowerCamelCase	:     str       =       scheduler.step(lowercase    , lowercase    , lowercase    )
											_lowerCamelCase	:     str       =       output.prev_sample

							_lowerCamelCase	:     int       =       torch.sum(torch.abs(lowercase    )    )
							_lowerCamelCase	:     int       =       torch.mean(torch.abs(lowercase    )    )

							if str(lowercase    ).startswith('cpu'    ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.41_25    ) < 1E-2
											assert abs(result_mean.item() - 0.02_66    ) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.41_25    ) < 1E-2
											assert abs(result_mean.item() - 0.02_66    ) < 1E-3





"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):  # This function is recursive
				_lowerCamelCase	:     Optional[Any]       =       len(lowercase__						)
				# If the array contains only one element, we return it (it's the stop condition of
				# recursion)
				if array_length <= 1:
								return array
								# Else
				_lowerCamelCase	:     Optional[Any]       =       array[0]
				_lowerCamelCase	:     Optional[int]       =       False
				_lowerCamelCase	:     List[str]       =       1
				_lowerCamelCase	:     list[int]       =       []
				while not is_found and i < array_length:
								if array[i] < pivot:
												_lowerCamelCase	:     Union[str, Any]       =       True
												_lowerCamelCase	:     str       =       [element for element in array[i:] if element >= array[i]]
												_lowerCamelCase	:     str       =       longest_subsequence(lowercase__						)
												if len(lowercase__						) > len(lowercase__						):
																_lowerCamelCase	:     Any       =       temp_array
								else:
												i += 1

				_lowerCamelCase	:     Optional[Any]       =       [element for element in array[1:] if element >= pivot]
				_lowerCamelCase	:     Dict       =       [pivot, *longest_subsequence(lowercase__						)]
				if len(lowercase__						) > len(lowercase__						):
								return temp_array
				else:
								return longest_subseq


if __name__ == "__main__":
			import doctest

			doctest.testmod()