
'''simple docstring'''
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							(DDPMScheduler,)







				def 						UpperCAmelCase_    (       self						,				**A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     {
											    'num_train_timesteps': 1000,
											    'beta_start': 0.0_001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											    'variance_type': 'fixed_small',
											    'clip_sample': True,
											}

											config.update(**A_     )
											return config







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											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=A_						,				beta_end=A_     )







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







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''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     )->		Union[str, Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     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     ) - 0.00_979     )     ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.02     )     ) < 1e-5







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     len(A_     )

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

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

																		# 2. predict previous mean of sample x_t-1
																		UpperCamelCase   			=     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   			=     pred_prev_sample

											UpperCamelCase   			=     torch.sum(torch.abs(A_     )     )
											UpperCamelCase   			=     torch.mean(torch.abs(A_     )     )

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







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config(prediction_type='v_prediction'     )
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     len(A_     )

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

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

																		# 2. predict previous mean of sample x_t-1
																		UpperCamelCase   			=     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   			=     pred_prev_sample

											UpperCamelCase   			=     torch.sum(torch.abs(A_     )     )
											UpperCamelCase   			=     torch.mean(torch.abs(A_     )     )

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







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [100, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=A_     )

											UpperCamelCase   			=     scheduler.timesteps

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

																		UpperCamelCase   			=     scheduler.previous_timestep(A_     )
																		UpperCamelCase   			=     prev_t.item()

																		self.assertEqual(A_						,				A_     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [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     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [100, 87, 50, 1, 0]
											UpperCamelCase   			=     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     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [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_     )






'''simple docstring'''
from __future__ import annotations

lowerCAmelCase					:    Union[str, Any]           = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase					:    List[str]           = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							UpperCamelCase   			=     len(A)

							for i in range(A):
														UpperCamelCase   			=     -1
														for j in range(i + 1		,     A):
																					if arr[i] < arr[j]:
																												UpperCamelCase   			=     arr[j]
																												break
														result.append(A)
							return result







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							for i, outer in enumerate(A):
														UpperCamelCase   			=     -1
														for inner in arr[i + 1 :]:
																					if outer < inner:
																												UpperCamelCase   			=     inner
																												break
														result.append(A)
							return result







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     len(A)
							UpperCamelCase   			=     []
							UpperCamelCase   			=     [-1] * arr_size

							for index in reversed(range(A)):
														if stack:
																					while stack[-1] <= arr[index]:
																												stack.pop()
																												if not stack:
																																			break
														if stack:
																					UpperCamelCase   			=     stack[-1]
														stack.append(arr[index])
							return result


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

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						lowerCAmelCase					:    Optional[Any]           = (
						    'from __main__ import arr, next_greatest_element_slow, '
						    'next_greatest_element_fast, next_greatest_element'
						)
						print(
						    'next_greatest_element_slow():',
						    timeit('next_greatest_element_slow(arr)', setup=setup),
						)
						print(
						    'next_greatest_element_fast():',
						    timeit('next_greatest_element_fast(arr)', setup=setup),
						)
						print(
						    '     next_greatest_element():',
						    timeit('next_greatest_element(arr)', setup=setup),
						)




