





'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			str								=						(UnCLIPScheduler,)







	def 						__a   (				self			:   Optional[int] , **lowerCamelCase			:   Any		):




				'''simple docstring'''
				a__     = {
				    "num_train_timesteps": 1_0_0_0,
				    "variance_type": "fixed_small_log",
				    "clip_sample": True,
				    "clip_sample_range": 1.0,
				    "prediction_type": "epsilon",
				}

				config.update(**lowerCamelCase		)
				return config







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCamelCase		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				for variance in ["fixed_small_log", "learned_range"]:
							self.check_over_configs(variance_type=lowerCamelCase		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowerCamelCase		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				for clip_sample_range in [1, 5, 1_0, 2_0]:
							self.check_over_configs(clip_sample_range=lowerCamelCase		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				for prediction_type in ["epsilon", "sample"]:
							self.check_over_configs(prediction_type=lowerCamelCase		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				for time_step in [0, 5_0_0, 9_9_9]:
							for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
										if prev_timestep is not None and prev_timestep >= time_step:
													continue

										self.check_over_forward(time_step=lowerCamelCase , prev_timestep=lowerCamelCase		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config(variance_type="fixed_small_log"		)
				a__     = scheduler_class(**lowerCamelCase		)

				assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000e-10		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.0549625		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.9994987		)		) < 1e-5







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config(variance_type="learned_range"		)
				a__     = scheduler_class(**lowerCamelCase		)

				a__     = 0.5

				assert scheduler._get_variance(1 , predicted_variance=lowerCamelCase		) - -10.1712790 < 1e-5
				assert scheduler._get_variance(4_8_7 , predicted_variance=lowerCamelCase		) - -5.7998052 < 1e-5
				assert scheduler._get_variance(9_9_9 , predicted_variance=lowerCamelCase		) - -0.0010011 < 1e-5







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config()
				a__     = scheduler_class(**lowerCamelCase		)

				a__     = scheduler.timesteps

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

				for i, t in enumerate(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() - 252.2682495		) < 1e-2
				assert abs(result_mean.item() - 0.3284743		) < 1e-3







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config()
				a__     = scheduler_class(**lowerCamelCase		)

				scheduler.set_timesteps(2_5		)

				a__     = scheduler.timesteps

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

				for i, t in enumerate(lowerCamelCase		):
							# 1. predict noise residual
							a__     = model(lowerCamelCase , lowerCamelCase		)

							if i + 1 == timesteps.shape[0]:
										a__     = None
							else:
										a__     = timesteps[i + 1]

							# 2. predict previous mean of sample x_t-1
							a__     = scheduler.step(
							    lowerCamelCase , lowerCamelCase , lowerCamelCase , prev_timestep=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() - 258.2044983		) < 1e-2
				assert abs(result_mean.item() - 0.3362038		) < 1e-3







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				pass







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				pass










'''simple docstring'''


import math







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int = 0					,  __lowerCamelCase  :     int = 0     )   ->    list:
			a__     = end or len(__lowerCamelCase     )
			for i in range(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = i
						a__     = array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									a__     = array[temp_index - 1]
									temp_index -= 1
						a__     = temp_index_value
			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    None:  # Max Heap
			a__     = index
			a__     = 2 * index + 1  # Left Node
			a__     = 2 * index + 2  # Right Node

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

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

			if largest != index:
						a__   ,				a__     = array[largest], array[index]
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			a__     = len(__lowerCamelCase     )

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

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

			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			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    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			a__     = low
			a__     = high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						a__   ,				a__     = array[j], array[i]
						i += 1







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			if len(__lowerCamelCase     ) == 0:
						return array
			a__     = 2 * math.ceil(math.loga(len(__lowerCamelCase     )     )     )
			a__     = 16
			return intro_sort(__lowerCamelCase					,  0					,  len(__lowerCamelCase     )					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    list:
			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(__lowerCamelCase     )
						max_depth -= 1
						a__     = median_of_a(__lowerCamelCase					,  __lowerCamelCase					,  start + ((end - start) // 2) + 1					,  end - 1     )
						a__     = partition(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						intro_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						a__     = p
			return insertion_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase_   :   Optional[int]       =					input("Enter numbers separated by a comma : ").strip()
		lowerCAmelCase_   :   List[str]       =					[float(item) for item in user_input.split(",")]
		print(sort(unsorted))



