
"""simple docstring"""
import math

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = 0 ,    _lowerCAmelCase = 0						):
						__lowerCAmelCase     =  end or len(_lowerCAmelCase						)
						for i in range(_lowerCAmelCase ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  i
												__lowerCAmelCase     =  array[i]
												while temp_index != start and temp_index_value < array[temp_index - 1]:
																		__lowerCAmelCase     =  array[temp_index - 1]
																		temp_index -= 1
												__lowerCAmelCase     =  temp_index_value
						return array

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):  # Max Heap
						__lowerCAmelCase     =  index
						__lowerCAmelCase     =  2 * index + 1  # Left Node
						__lowerCAmelCase     =  2 * index + 2  # Right Node

						if left_index < heap_size and array[largest] < array[left_index]:
												__lowerCAmelCase     =  left_index

						if right_index < heap_size and array[largest] < array[right_index]:
												__lowerCAmelCase     =  right_index

						if largest != index:
												__lowerCAmelCase   ,     __lowerCAmelCase     =  array[largest], array[index]
												heapify(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  len(_lowerCAmelCase						)

						for i in range(n // 2 ,    -1 ,    -1						):
												heapify(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						for i in range(n - 1 ,    0 ,    -1						):
												__lowerCAmelCase   ,     __lowerCAmelCase     =  array[0], array[i]
												heapify(_lowerCAmelCase ,    0 ,    _lowerCAmelCase						)

						return array

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						if (array[first_index] > array[middle_index]) != (
						    array[first_index] > array[last_index]
						):
												return array[first_index]
						elif (array[middle_index] > array[first_index]) != (
						    array[middle_index] > array[last_index]
						):
												return array[middle_index]
						else:
												return array[last_index]

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  low
						__lowerCAmelCase     =  high
						while True:
												while array[i] < pivot:
																		i += 1
												j -= 1
												while pivot < array[j]:
																		j -= 1
												if i >= j:
																		return i
												__lowerCAmelCase   ,     __lowerCAmelCase     =  array[j], array[i]
												i += 1

def  lowercase     (_lowerCAmelCase						):
						if len(_lowerCAmelCase						) == 0:
												return array
						__lowerCAmelCase     =  2 * math.ceil(math.loga(len(_lowerCAmelCase						)						)						)
						__lowerCAmelCase     =  16
						return intro_sort(_lowerCAmelCase ,    0 ,    len(_lowerCAmelCase						) ,    _lowerCAmelCase ,    _lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						while end - start > size_threshold:
												if max_depth == 0:
																		return heap_sort(_lowerCAmelCase						)
												max_depth -= 1
												__lowerCAmelCase     =  median_of_a(_lowerCAmelCase ,    _lowerCAmelCase ,    start + ((end - start) // 2) + 1 ,    end - 1						)
												__lowerCAmelCase     =  partition(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
												intro_sort(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
												__lowerCAmelCase     =  p
						return insertion_sort(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			SCREAMING_SNAKE_CASE_					     =	input('''Enter numbers separated by a comma : ''').strip()
			SCREAMING_SNAKE_CASE_					     =	[float(item) for item in user_input.split(''',''')]
			print(sort(unsorted))


"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		(DDIMParallelScheduler,)
							_snake_case						      =		(('''eta''', 0.0), ('''num_inference_steps''', 5_0))




							def    A__						( self    ,				**snake_case_				)       ->  Optional[Any]:
													__lowerCAmelCase     =  {
													    """num_train_timesteps""": 1_000,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													    """clip_sample""": True,
													}

													config.update(**snake_case_				)
													return config




							def    A__						( self    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(**snake_case_				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter

													scheduler.set_timesteps(snake_case_				)

													for t in scheduler.timesteps:
																			__lowerCAmelCase     =  model(snake_case_    ,				snake_case_				)
																			__lowerCAmelCase     =  scheduler.step(snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				).prev_sample

													return sample




							def    A__						( self				)       ->  List[str]:
													for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=snake_case_				)

													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(steps_offset=1				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)
													scheduler.set_timesteps(5				)
													assert torch.equal(scheduler.timesteps    ,				torch.LongTensor([801, 601, 401, 201, 1]				)				)




							def    A__						( self				)       ->  int:
													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=snake_case_    ,				beta_end=snake_case_				)




							def    A__						( self				)       ->  str:
													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=snake_case_				)




							def    A__						( self				)       ->  Dict:
													for timestep_spacing in ["trailing", "leading"]:
																			self.check_over_configs(timestep_spacing=snake_case_				)




							def    A__						( self				)       ->  str:
													for rescale_betas_zero_snr in [True, False]:
																			self.check_over_configs(rescale_betas_zero_snr=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													self.check_over_configs(thresholding=snake_case_				)
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=snake_case_    ,				prediction_type=snake_case_    ,				sample_max_value=snake_case_    ,				)




							def    A__						( self				)       ->  Union[str, Any]:
													for t in [1, 10, 49]:
																			self.check_over_forward(time_step=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, num_inference_steps in zip([1, 10, 50]    ,				[10, 50, 500]				):
																			self.check_over_forward(time_step=snake_case_    ,				num_inference_steps=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, eta in zip([1, 10, 49]    ,				[0.0, 0.5, 1.0]				):
																			self.check_over_forward(time_step=snake_case_    ,				eta=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(420    ,				400				) - 0.14_771				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(980    ,				960				) - 0.32_460				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487    ,				486				) - 0.00_979				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999    ,				998				) - 0.02				)				) < 1e-5




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0
													scheduler.set_timesteps(snake_case_				)

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

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

													__lowerCAmelCase     =  model(samples.flatten(0    ,				1				)    ,				timesteps.flatten(0    ,				1				)				)
													__lowerCAmelCase     =  scheduler.batch_step_no_noise(snake_case_    ,				timesteps.flatten(0    ,				1				)    ,				samples.flatten(0    ,				1				)    ,				snake_case_				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
													assert abs(result_mean.item() - 0.4_982				) < 1e-3




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  self.full_loop()

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 172.0_067				) < 1e-2
													assert abs(result_mean.item() - 0.223_967				) < 1e-3




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  self.full_loop(prediction_type="""v_prediction"""				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 52.5_302				) < 1e-2
													assert abs(result_mean.item() - 0.0_684				) < 1e-3




							def    A__						( self				)       ->  Tuple:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.8_295				) < 1e-2
													assert abs(result_mean.item() - 0.1_951				) < 1e-3




							def    A__						( self				)       ->  Union[str, Any]:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.0_784				) < 1e-2
													assert abs(result_mean.item() - 0.1_941				) < 1e-3
