
"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class 			snake_case  :

							def __init__(self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			data
									SCREAMING_SNAKE_CASE_    			=			None







class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			None
									SCREAMING_SNAKE_CASE_    			=			None

							def __iter__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.head
									while self.head:
											yield node.data
											SCREAMING_SNAKE_CASE_    			=			node.next
											if node == self.head:
													break

							def __len__(self     ):

									"""simple docstring"""







									return sum(1 for _ in self     )

							def __repr__(self     ):

									"""simple docstring"""







									return "->".join(str(SCREAMING_SNAKE_CASE_     ) for item in iter(self     )     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									self.insert_nth(len(self     )							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									self.insert_nth(0							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if index < 0 or index > len(self     ):
											raise IndexError('''list index out of range.'''     )
									SCREAMING_SNAKE_CASE_    			=			Node(SCREAMING_SNAKE_CASE_     )
									if self.head is None:
											SCREAMING_SNAKE_CASE_    			=			new_node  # first node points itself
											SCREAMING_SNAKE_CASE_    			=			SCREAMING_SNAKE_CASE_    			=			new_node
									elif index == 0:  # insert at head
											SCREAMING_SNAKE_CASE_    			=			self.head
											SCREAMING_SNAKE_CASE_    			=			SCREAMING_SNAKE_CASE_    			=			new_node
									else:
											SCREAMING_SNAKE_CASE_    			=			self.head
											for _ in range(index - 1     ):
													SCREAMING_SNAKE_CASE_    			=			temp.next
											SCREAMING_SNAKE_CASE_    			=			temp.next
											SCREAMING_SNAKE_CASE_    			=			new_node
											if index == len(self     ) - 1:  # insert at tail
													SCREAMING_SNAKE_CASE_    			=			new_node

							def _lowercase       (self     ):

									"""simple docstring"""







									return self.delete_nth(0     )

							def _lowercase       (self     ):

									"""simple docstring"""







									return self.delete_nth(len(self     ) - 1     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_ = 0     ):

									"""simple docstring"""







									if not 0 <= index < len(self     ):
											raise IndexError('''list index out of range.'''     )
									SCREAMING_SNAKE_CASE_    			=			self.head
									if self.head == self.tail:  # just one node
											SCREAMING_SNAKE_CASE_    			=			SCREAMING_SNAKE_CASE_    			=			None
									elif index == 0:  # delete head node
											SCREAMING_SNAKE_CASE_    			=			self.tail.next.next
											SCREAMING_SNAKE_CASE_    			=			self.head.next
									else:
											SCREAMING_SNAKE_CASE_    			=			self.head
											for _ in range(index - 1     ):
													SCREAMING_SNAKE_CASE_    			=			temp.next
											SCREAMING_SNAKE_CASE_    			=			temp.next
											SCREAMING_SNAKE_CASE_    			=			temp.next.next
											if index == len(self     ) - 1:  # delete at tail
													SCREAMING_SNAKE_CASE_    			=			temp
									return delete_node.data







							def _lowercase       (self     ):

									"""simple docstring"""







									return len(self     ) == 0







def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			CircularLinkedList()
		assert len(__a     ) == 0
		assert circular_linked_list.is_empty() is True
		assert str(__a     ) == ""

		try:
				circular_linked_list.delete_front()
				raise AssertionError  # This should not happen
		except IndexError:
				assert True  # This should happen

		try:
				circular_linked_list.delete_tail()
				raise AssertionError  # This should not happen
		except IndexError:
				assert True  # This should happen

		try:
				circular_linked_list.delete_nth(-1     )
				raise AssertionError
		except IndexError:
				assert True

		try:
				circular_linked_list.delete_nth(0     )
				raise AssertionError
		except IndexError:
				assert True

		assert circular_linked_list.is_empty() is True
		for i in range(5     ):
				assert len(__a     ) == i
				circular_linked_list.insert_nth(__a,				i + 1     )
		assert str(__a     ) == "->".join(str(__a     ) for i in range(1,				6     )     )

		circular_linked_list.insert_tail(6     )
		assert str(__a     ) == "->".join(str(__a     ) for i in range(1,				7     )     )
		circular_linked_list.insert_head(0     )
		assert str(__a     ) == "->".join(str(__a     ) for i in range(0,				7     )     )

		assert circular_linked_list.delete_front() == 0
		assert circular_linked_list.delete_tail() == 6
		assert str(__a     ) == "->".join(str(__a     ) for i in range(1,				6     )     )
		assert circular_linked_list.delete_nth(2     ) == 3

		circular_linked_list.insert_nth(2,				3     )
		assert str(__a     ) == "->".join(str(__a     ) for i in range(1,				6     )     )

		assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
						import doctest

						doctest.testmod()

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

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

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

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

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







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

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

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

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

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass