




from __future__ import annotations


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					0
	snake_case_		   =					len(_A							) - 1
	while left <= right:
		snake_case_		   =					(left + right) // 2
		if left == right:
			if sorted_list[middle] < item:
				snake_case_		   =					middle + 1
			break
		elif sorted_list[middle] < item:
			snake_case_		   =					middle + 1
		else:
			snake_case_		   =					middle - 1
	sorted_list.insert(_A					,     _A							)
	return sorted_list


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					[]
	while left and right:
		if left[0][0] < right[0][0]:
			result.append(left.pop(0							)							)
		else:
			result.append(right.pop(0							)							)
	return result + left + right


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					len(_A							)
	if length <= 1:
		return list_ad
	snake_case_		   =					length // 2
	return merge(sortlist_ad(list_ad[:middle]							)					,     sortlist_ad(list_ad[middle:]							)							)


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	if len(_A							) <= 1:
		return collection
	snake_case_		   =					[]
	snake_case_		   =					False
	for i in range(0					,     len(_A							)					,     2							):
		if i == len(_A							) - 1:
			snake_case_		   =					True
		else:
			if collection[i] < collection[i + 1]:
				two_paired_list.append([collection[i], collection[i + 1]]							)
			else:
				two_paired_list.append([collection[i + 1], collection[i]]							)
	snake_case_		   =					sortlist_ad(_A							)
	snake_case_		   =					[i[0] for i in sorted_list_ad]
	result.append(sorted_list_ad[-1][1]							)
	if has_last_odd_item:
		snake_case_		   =					collection[-1]
		snake_case_		   =					binary_search_insertion(_A					,     _A							)
	snake_case_		   =					False
	for i in range(len(_A							) - 1							):
		if result[i] == collection[-1] and has_last_odd_item:
			snake_case_		   =					True
		snake_case_		   =					sorted_list_ad[i][1]
		# If last_odd_item is inserted before the item's index,
		# you should forward index one more.
		if is_last_odd_item_inserted_before_this_index:
			snake_case_		   =					result[: i + 2] + binary_search_insertion(result[i + 2 :]					,     _A							)
		else:
			snake_case_		   =					result[: i + 1] + binary_search_insertion(result[i + 1 :]					,     _A							)

	return result


if __name__ == "__main__":
		lowercase__    :					List[str]					=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__    :					Optional[Any]					=						[int(item) for item in user_input.split(",")]
		print(merge_insertion_sort(unsorted))






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






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






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






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






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






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

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

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

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

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

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

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
