




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					(DDPMParallelScheduler,)





				def 							__UpperCAmelCase							(      self				:     str					,      **__lowerCamelCase				:     Dict							)					->     List[str]:
							a    =			{
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0_001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "variance_type": "fixed_small",
							    "clip_sample": True,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							for timesteps in [1, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Dict:
							for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,      [0.002, 0.02, 0.2, 2]							):
										self.check_over_configs(beta_start=__lowerCamelCase					,      beta_end=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     Any:
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     int:
							for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     List[str]:
							self.check_over_configs(thresholding=__lowerCamelCase							)
							for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=__lowerCamelCase					,      prediction_type=__lowerCamelCase					,      sample_max_value=__lowerCamelCase					,      )





				def 							__UpperCAmelCase							(      self				:     Any							)					->     List[str]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     int:
							for t in [0, 5_00, 9_99]:
										self.check_over_forward(time_step=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     List[Any]:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87							) - 0.00_979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99							) - 0.02							)							) < 1e-5





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Dict:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							a    =			len(__lowerCamelCase							)

							a    =			self.dummy_model()
							a    =			self.dummy_sample_deter
							a    =			self.dummy_sample_deter + 0.1
							a    =			self.dummy_sample_deter - 0.1

							a    =			samplea.shape[0]
							a    =			torch.stack([samplea, samplea, samplea]					,      dim=0							)
							a    =			torch.arange(__lowerCamelCase							)[0:3, None].repeat(1					,      __lowerCamelCase							)

							a    =			model(samples.flatten(0					,      1							)					,      timesteps.flatten(0					,      1							)							)
							a    =			scheduler.batch_step_no_noise(__lowerCamelCase					,      timesteps.flatten(0					,      1							)					,      samples.flatten(0					,      1							)							)

							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 1_153.1_833							) < 1e-2
							assert abs(result_mean.item() - 0.5_005							) < 1e-3





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[str]:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							a    =			len(__lowerCamelCase							)

							a    =			self.dummy_model()
							a    =			self.dummy_sample_deter
							a    =			torch.manual_seed(0							)

							for t in reversed(range(__lowerCamelCase							)							):
										# 1. predict noise residual
										a    =			model(__lowerCamelCase					,      __lowerCamelCase							)

										# 2. predict previous mean of sample x_t-1
										a    =			scheduler.step(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      generator=__lowerCamelCase							).prev_sample

										a    =			pred_prev_sample

							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 258.9_606							) < 1e-2
							assert abs(result_mean.item() - 0.3_372							) < 1e-3





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[str]:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config(prediction_type="v_prediction"							)
							a    =			scheduler_class(**__lowerCamelCase							)

							a    =			len(__lowerCamelCase							)

							a    =			self.dummy_model()
							a    =			self.dummy_sample_deter
							a    =			torch.manual_seed(0							)

							for t in reversed(range(__lowerCamelCase							)							):
										# 1. predict noise residual
										a    =			model(__lowerCamelCase					,      __lowerCamelCase							)

										# 2. predict previous mean of sample x_t-1
										a    =			scheduler.step(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      generator=__lowerCamelCase							).prev_sample

										a    =			pred_prev_sample

							a    =			torch.sum(torch.abs(__lowerCamelCase							)							)
							a    =			torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_sum.item() - 202.0_296							) < 1e-2
							assert abs(result_mean.item() - 0.2_631							) < 1e-3





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     List[str]:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							a    =			[1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=__lowerCamelCase							)

							a    =			scheduler.timesteps

							for i, timestep in enumerate(__lowerCamelCase							):
										if i == len(__lowerCamelCase							) - 1:
													a    =			-1
										else:
													a    =			timesteps[i + 1]

										a    =			scheduler.previous_timestep(__lowerCamelCase							)
										a    =			prev_t.item()

										self.assertEqual(__lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[Any]:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							a    =			[1_00, 87, 50, 51, 0]

							with self.assertRaises(__lowerCamelCase					,      msg="`custom_timesteps` must be in descending order."							):
										scheduler.set_timesteps(timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Any:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							a    =			[1_00, 87, 50, 1, 0]
							a    =			len(__lowerCamelCase							)

							with self.assertRaises(__lowerCamelCase					,      msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."							):
										scheduler.set_timesteps(num_inference_steps=__lowerCamelCase					,      timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     int:
							a    =			self.scheduler_classes[0]
							a    =			self.get_scheduler_config()
							a    =			scheduler_class(**__lowerCamelCase							)

							a    =			[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    __lowerCamelCase					,      msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"					,      ):
										scheduler.set_timesteps(timesteps=__lowerCamelCase							)












from __future__ import annotations




def 			__magic_name__				( A		:					list   ):






			'''simple docstring'''







			if len(A   ) == 0:
						return []
			a    ,		a    =			min(A   ), max(A   )
			a    =			int(max_value - min_value   ) + 1
			a    =			[[] for _ in range(A   )]

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

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


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]






