



from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@abstractmethod
	def __lt__(self	, lowerCamelCase_	):
						"""simple docstring"""

						pass




	@abstractmethod
	def __gt__(self	, lowerCamelCase_	):
						"""simple docstring"""

						pass




	@abstractmethod
	def __eq__(self	, lowerCamelCase_	):
						"""simple docstring"""

						pass




_lowercase:				Union[str, Any]       =	TypeVar("T", bound=Comparable)



class 			_lowercase							(     Generic[T]			):





	"""simple docstring"""




	def __init__(self	):
						"""simple docstring"""

						a        =     []
						a        =     0




	def __repr__(self	):
						"""simple docstring"""

						return str(self.h	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						if child_idx > 0:
											return (child_idx - 1) // 2
						return None




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     2 * parent_idx + 1
						if left_child_index < self.heap_size:
											return left_child_index
						return None




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     2 * parent_idx + 2
						if right_child_index < self.heap_size:
											return right_child_index
						return None




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						if index < self.heap_size:
											a        =     index
											a        =     self.left_child_idx(lowerCamelCase_	)
											a        =     self.right_child_idx(lowerCamelCase_	)
											# check which child is larger than its parent
											if left_child is not None and self.h[left_child] > self.h[violation]:
																a        =     left_child
											if right_child is not None and self.h[right_child] > self.h[violation]:
																a        =     right_child
											# if violation indeed exists
											if violation != index:
																# swap to fix the violation
																a  ,       a        =     self.h[index], self.h[violation]
																# fix the subsequent violation recursively if any
																self.max_heapify(lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     list(lowerCamelCase_	)
						a        =     len(self.h	)
						if self.heap_size > 1:
											# max_heapify from right to left but exclude leaves (last level)
											for i in range(self.heap_size // 2 - 1	, -1	, -1	):
																self.max_heapify(lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if self.heap_size >= 2:
											a        =     self.h[0]
											a        =     self.h.pop(-1	)
											self.heap_size -= 1
											self.max_heapify(0	)
											return me
						elif self.heap_size == 1:
											self.heap_size -= 1
											return self.h.pop(-1	)
						else:
											raise Exception("Empty heap"	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						self.h.append(lowerCamelCase_	)
						a        =     (self.heap_size - 1) // 2
						self.heap_size += 1
						while idx >= 0:
											self.max_heapify(lowerCamelCase_	)
											a        =     (idx - 1) // 2




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.heap_size
						for j in range(size - 1	, 0	, -1	):
											a  ,       a        =     self.h[j], self.h[0]
											self.heap_size -= 1
											self.max_heapify(0	)
						a        =     size


if __name__ == "__main__":
				import doctest

				# run doc test
				doctest.testmod()

				# demo
				for unsorted in [
				    [0],
				    [2],
				    [3, 5],
				    [5, 3],
				    [5, 5],
				    [0, 0, 0, 0],
				    [1, 1, 1, 1],
				    [2, 2, 3, 5],
				    [0, 2, 2, 3, 5],
				    [2, 5, 3, 0, 2, 3, 0, 3],
				    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
				    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
				    [-45, -2, -5],
				]:
								print(F"""unsorted array: {unsorted}""")

								_lowercase:				Heap[int]       =	Heap()
								heap.build_max_heap(unsorted)
								print(F"""after build heap: {heap}""")

								print(F"""max value: {heap.extract_max()}""")
								print(F"""after max value removed: {heap}""")

								heap.insert(100)
								print(F"""after new value 100 inserted: {heap}""")

								heap.heap_sort()
								print(F"""heap-sorted array: {heap}\n""")





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							(EulerAncestralDiscreteScheduler,)
	__A					=							10




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_	)
						return config




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""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=lowerCamelCase_	, beta_end=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config(prediction_type="v_prediction"	)
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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

						scheduler.set_timesteps(self.num_inference_steps	, device=lowerCamelCase_	)
						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for t in scheduler.timesteps:
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

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