

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 			a					(		UpperCAmelCase			):
						_lowercase        =     (DDPMScheduler,)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      {
										    "num_train_timesteps": 1000,
										    "beta_start": 0.00_01,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "variance_type": "fixed_small",
										    "clip_sample": True,
										}

										config.update(**A_		)
										return config


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,   [0.0_02, 0.02, 0.2, 2]		):
														self.check_over_configs(beta_start=A_  ,   beta_end=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.check_over_configs(thresholding=A_		)
										for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=A_  ,   prediction_type=A_  ,   sample_max_value=A_  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for t in [0, 500, 999]:
														self.check_over_forward(time_step=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Any							=      self.get_scheduler_config()
										_UpperCAmelCase    :						List[Any]							=      scheduler_class(**A_		)

										assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_09_79		)		) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1e-5


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						str							=      self.get_scheduler_config()
										_UpperCAmelCase    :						Optional[Any]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						List[Any]							=      len(A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      self.dummy_model()
										_UpperCAmelCase    :						str							=      self.dummy_sample_deter
										_UpperCAmelCase    :						Dict							=      torch.manual_seed(0		)

										for t in reversed(range(A_		)		):
														# 1. predict noise residual
														_UpperCAmelCase    :						str							=      model(A_  ,   A_		)

														# 2. predict previous mean of sample x_t-1
														_UpperCAmelCase    :						str							=      scheduler.step(A_  ,   A_  ,   A_  ,   generator=A_		).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_UpperCAmelCase    :						Dict							=      pred_prev_sample

										_UpperCAmelCase    :						Any							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 2_58.96_06		) < 1e-2
										assert abs(result_mean.item() - 0.33_72		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Dict							=      self.get_scheduler_config(prediction_type="v_prediction"		)
										_UpperCAmelCase    :						Optional[Any]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						Optional[Any]							=      len(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      self.dummy_model()
										_UpperCAmelCase    :						Tuple							=      self.dummy_sample_deter
										_UpperCAmelCase    :						List[Any]							=      torch.manual_seed(0		)

										for t in reversed(range(A_		)		):
														# 1. predict noise residual
														_UpperCAmelCase    :						List[str]							=      model(A_  ,   A_		)

														# 2. predict previous mean of sample x_t-1
														_UpperCAmelCase    :						int							=      scheduler.step(A_  ,   A_  ,   A_  ,   generator=A_		).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_UpperCAmelCase    :						List[Any]							=      pred_prev_sample

										_UpperCAmelCase    :						Union[str, Any]							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						int							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 2_02.02_96		) < 1e-2
										assert abs(result_mean.item() - 0.26_31		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Optional[Any]							=      self.get_scheduler_config()
										_UpperCAmelCase    :						Optional[int]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						Any							=      [100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=A_		)

										_UpperCAmelCase    :						str							=      scheduler.timesteps

										for i, timestep in enumerate(A_		):
														if i == len(A_		) - 1:
																		_UpperCAmelCase    :						str							=      -1
														else:
																		_UpperCAmelCase    :						Tuple							=      timesteps[i + 1]

														_UpperCAmelCase    :						str							=      scheduler.previous_timestep(A_		)
														_UpperCAmelCase    :						Union[str, Any]							=      prev_t.item()

														self.assertEqual(A_  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						str							=      self.get_scheduler_config()
										_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      [100, 87, 50, 51, 0]

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						int							=      self.get_scheduler_config()
										_UpperCAmelCase    :						Union[str, Any]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						str							=      [100, 87, 50, 1, 0]
										_UpperCAmelCase    :						str							=      len(A_		)

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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Dict							=      self.get_scheduler_config()
										_UpperCAmelCase    :						int							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						str							=      [scheduler.config.num_train_timesteps]

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





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


