


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					(EulerDiscreteScheduler,)
					A__     =					10

					def     lowerCamelCase__	(self       :		List[str] ,   **_UpperCAmelCase       :		Optional[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						{
							    """num_train_timesteps""": 1100,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							}

							config.update(**_UpperCAmelCase						)
							return config

					def     lowerCamelCase__	(self       :		Any						)		->  List[str]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[str]						)		->  str:




							"""simple docstring"""


							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=_UpperCAmelCase ,   beta_end=_UpperCAmelCase						)

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




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Optional[int]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[str]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config()
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowercase__	     =						torch.manual_seed(0						)

							lowercase__	     =						self.dummy_model()
							lowercase__	     =						self.dummy_sample_deter * scheduler.init_noise_sigma
							lowercase__	     =						sample.to(_UpperCAmelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowercase__	     =						scheduler.scale_model_input(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						scheduler.step(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						)
									lowercase__	     =						output.prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						torch.mean(torch.abs(_UpperCAmelCase						)						)

							assert abs(result_sum.item() - 10.0_807						) < 1E-2
							assert abs(result_mean.item() - 0.0_131						) < 1E-3

					def     lowerCamelCase__	(self       :		List[Any]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config(prediction_type="""v_prediction"""						)
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowercase__	     =						torch.manual_seed(0						)

							lowercase__	     =						self.dummy_model()
							lowercase__	     =						self.dummy_sample_deter * scheduler.init_noise_sigma
							lowercase__	     =						sample.to(_UpperCAmelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowercase__	     =						scheduler.scale_model_input(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						scheduler.step(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						)
									lowercase__	     =						output.prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						torch.mean(torch.abs(_UpperCAmelCase						)						)

							assert abs(result_sum.item() - 0.0_002						) < 1E-2
							assert abs(result_mean.item() - 2.2_6_7_6E-0_6						) < 1E-3

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config()
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

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

							lowercase__	     =						torch.manual_seed(0						)

							lowercase__	     =						self.dummy_model()
							lowercase__	     =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							lowercase__	     =						sample.to(_UpperCAmelCase						)

							for t in scheduler.timesteps:
									lowercase__	     =						scheduler.scale_model_input(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						scheduler.step(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						)
									lowercase__	     =						output.prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						torch.mean(torch.abs(_UpperCAmelCase						)						)

							assert abs(result_sum.item() - 10.0_807						) < 1E-2
							assert abs(result_mean.item() - 0.0_131						) < 1E-3


					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config()
							lowercase__	     =						scheduler_class(**_UpperCAmelCase ,   use_karras_sigmas=_UpperCAmelCase						)

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

							lowercase__	     =						torch.manual_seed(0						)

							lowercase__	     =						self.dummy_model()
							lowercase__	     =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							lowercase__	     =						sample.to(_UpperCAmelCase						)

							for t in scheduler.timesteps:
									lowercase__	     =						scheduler.scale_model_input(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)

									lowercase__	     =						scheduler.step(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						)
									lowercase__	     =						output.prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						torch.mean(torch.abs(_UpperCAmelCase						)						)

							assert abs(result_sum.item() - 124.52_299_499_511_719						) < 1E-2
							assert abs(result_mean.item() - 0.16_213_932_633_399_963						) < 1E-3





from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()

