



'''simple docstring'''






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    (HeunDiscreteScheduler,)
 UpperCAmelCase		      =    10







 def _snake_case					(				self    ,**a_				)						->  Any:
        _UpperCAmelCase    :		int     	=					{
            """num_train_timesteps""": 1_100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**a_				)
        return config







 def _snake_case					(				self				)						->  Dict:
        for timesteps in [10, 50, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        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=a_    ,beta_end=a_				)







 def _snake_case					(				self				)						->  Tuple:
        for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=a_				)







 def _snake_case					(				self				)						->  str:
        for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=a_    ,clip_sample=a_				)







 def _snake_case					(				self				)						->  str:
        for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=a_				)







 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		Any     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		str     	=					self.get_scheduler_config()
        _UpperCAmelCase    :		Optional[int]     	=					scheduler_class(**a_				)

        scheduler.set_timesteps(self.num_inference_steps				)

        _UpperCAmelCase    :		Optional[Any]     	=					self.dummy_model()
        _UpperCAmelCase    :		Union[str, Any]     	=					self.dummy_sample_deter * scheduler.init_noise_sigma
        _UpperCAmelCase    :		Any     	=					sample.to(a_				)

        for i, t in enumerate(scheduler.timesteps				):
               _UpperCAmelCase    :		Optional[Any]     	=					scheduler.scale_model_input(a_    ,a_				)

               _UpperCAmelCase    :		int     	=					model(a_    ,a_				)

               _UpperCAmelCase    :		Any     	=					scheduler.step(a_    ,a_    ,a_				)
               _UpperCAmelCase    :		List[str]     	=					output.prev_sample

        _UpperCAmelCase    :		Dict     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		Optional[int]     	=					torch.mean(torch.abs(a_				)				)

        if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3
        else:
               # CUDA
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		Union[str, Any]     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		List[Any]     	=					self.get_scheduler_config(prediction_type="""v_prediction"""				)
        _UpperCAmelCase    :		List[str]     	=					scheduler_class(**a_				)

        scheduler.set_timesteps(self.num_inference_steps				)

        _UpperCAmelCase    :		List[Any]     	=					self.dummy_model()
        _UpperCAmelCase    :		List[Any]     	=					self.dummy_sample_deter * scheduler.init_noise_sigma
        _UpperCAmelCase    :		Optional[int]     	=					sample.to(a_				)

        for i, t in enumerate(scheduler.timesteps				):
               _UpperCAmelCase    :		List[Any]     	=					scheduler.scale_model_input(a_    ,a_				)

               _UpperCAmelCase    :		int     	=					model(a_    ,a_				)

               _UpperCAmelCase    :		str     	=					scheduler.step(a_    ,a_    ,a_				)
               _UpperCAmelCase    :		int     	=					output.prev_sample

        _UpperCAmelCase    :		str     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		Optional[int]     	=					torch.mean(torch.abs(a_				)				)

        if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7				) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0				) < 1E-3
        else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[Any]     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		Union[str, Any]     	=					self.get_scheduler_config()
        _UpperCAmelCase    :		Optional[int]     	=					scheduler_class(**a_				)

        scheduler.set_timesteps(self.num_inference_steps    ,device=a_				)

        _UpperCAmelCase    :		Any     	=					self.dummy_model()
        _UpperCAmelCase    :		Any     	=					self.dummy_sample_deter.to(a_				) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
               _UpperCAmelCase    :		List[Any]     	=					scheduler.scale_model_input(a_    ,a_				)

               _UpperCAmelCase    :		Tuple     	=					model(a_    ,a_				)

               _UpperCAmelCase    :		List[Any]     	=					scheduler.step(a_    ,a_    ,a_				)
               _UpperCAmelCase    :		Optional[int]     	=					output.prev_sample

        _UpperCAmelCase    :		int     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		Any     	=					torch.mean(torch.abs(a_				)				)

        if str(a_				).startswith("""cpu"""				):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3
        elif str(a_				).startswith("""mps"""				):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002				) < 1E-2
        else:
               # CUDA
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Dict     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		Any     	=					self.get_scheduler_config()
        _UpperCAmelCase    :		str     	=					scheduler_class(**a_    ,use_karras_sigmas=a_				)

        scheduler.set_timesteps(self.num_inference_steps    ,device=a_				)

        _UpperCAmelCase    :		int     	=					self.dummy_model()
        _UpperCAmelCase    :		Union[str, Any]     	=					self.dummy_sample_deter.to(a_				) * scheduler.init_noise_sigma
        _UpperCAmelCase    :		List[str]     	=					sample.to(a_				)

        for t in scheduler.timesteps:
               _UpperCAmelCase    :		Optional[Any]     	=					scheduler.scale_model_input(a_    ,a_				)

               _UpperCAmelCase    :		Optional[Any]     	=					model(a_    ,a_				)

               _UpperCAmelCase    :		Tuple     	=					scheduler.step(a_    ,a_    ,a_				)
               _UpperCAmelCase    :		Optional[int]     	=					output.prev_sample

        _UpperCAmelCase    :		Tuple     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		List[Any]     	=					torch.mean(torch.abs(a_				)				)

        assert abs(result_sum.item() - 0.0_0015				) < 1E-2
        assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7				) < 1E-2








'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterable

class      UpperCamelCase_					:





	def __init__(    self						,						A						,						A       )     ->     int:
								UpperCAmelCase	,	UpperCAmelCase	:						Any					=						float(A       ), float(A       )





	def __eq__(    self						,						A       )     ->     str:
								return self.x == other.x and self.y == other.y





	def __ne__(    self						,						A       )     ->     Dict:
								return not self == other





	def __gt__(    self						,						A       )     ->     Dict:
								if self.x > other.x:
															return True
								elif self.x == other.x:
															return self.y > other.y
								return False





	def __lt__(    self						,						A       )     ->     str:
								return not self > other





	def __ge__(    self						,						A       )     ->     Union[str, Any]:
								if self.x > other.x:
															return True
								elif self.x == other.x:
															return self.y >= other.y
								return False





	def __le__(    self						,						A       )     ->     List[str]:
								if self.x < other.x:
															return True
								elif self.x == other.x:
															return self.y <= other.y
								return False





	def __repr__(    self       )     ->     Tuple:
								return f'''({self.x}, {self.y})'''






	def __hash__(    self       )     ->     List[str]:
								return hash(self.x       )




def  __lowerCamelCase    (   _lowercase      ,							) ->     list[Point]:
							UpperCAmelCase	:						list[Point]					=						[]
							if list_of_tuples:
														for p in list_of_tuples:
																					if isinstance(_lowercase      ,							_lowercase						):
																												points.append(_lowercase						)
																					else:
																												try:
																																			points.append(Point(p[0]      ,							p[1]						)						)
																												except (IndexError, TypeError):
																																			print(
																																			    F'''Ignoring deformed point {p}. All points'''
																																			    """ must have at least 2 coordinates."""						)
							return points



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:

							if not hasattr(_lowercase      ,							"""__iter__"""						):
														UpperCAmelCase	:						List[str]					=						F'''Expecting an iterable object but got an non-iterable type {points}'''
														raise ValueError(_lowercase						)

							if not points:
														UpperCAmelCase	:						Optional[int]					=						F'''Expecting a list of points but got {points}'''
														raise ValueError(_lowercase						)

							return _construct_points(_lowercase						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase						) ->     float:
							UpperCAmelCase	:						Dict					=						(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
							return det



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:
							UpperCAmelCase	:						Optional[int]					=						sorted(_validate_input(_lowercase						)						)
							UpperCAmelCase	:						int					=						len(_lowercase						)
							UpperCAmelCase	:						Any					=						set()

							for i in range(n - 1						):
														for j in range(i + 1      ,							_lowercase						):
																					UpperCAmelCase	:						Optional[Any]					=						False
																					UpperCAmelCase	:						Optional[int]					=						True
																					for k in range(_lowercase						):
																												if k != i and k != j:
																																			UpperCAmelCase	:						Union[str, Any]					=						_det(points[i]      ,							points[j]      ,							points[k]						)

																																			if det_k > 0:
																																										UpperCAmelCase	:						Optional[int]					=						True
																																			elif det_k < 0:
																																										UpperCAmelCase	:						int					=						True
																																			else:
																																										# point[i], point[j], point[k] all lie on a straight line
																																										# if point[k] is to the left of point[i] or it's to the
																																										# right of point[j], then point[i], point[j] cannot be
																																										# part of the convex hull of A
																																										if points[k] < points[i] or points[k] > points[j]:
																																																	UpperCAmelCase	:						List[Any]					=						False
																																																	break

																												if points_left_of_ij and points_right_of_ij:
																																			UpperCAmelCase	:						Union[str, Any]					=						False
																																			break

																					if ij_part_of_convex_hull:
																												convex_set.update([points[i], points[j]]						)

							return sorted(_lowercase						)



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:
							UpperCAmelCase	:						List[str]					=						sorted(_validate_input(_lowercase						)						)
							UpperCAmelCase	:						Optional[Any]					=						len(_lowercase						)

							# divide all the points into an upper hull and a lower hull
							# the left most point and the right most point are definitely
							# members of the convex hull by definition.
							# use these two anchors to divide all the points into two hulls,
							# an upper hull and a lower hull.

							# all points to the left (above) the line joining the extreme points belong to the
							# upper hull
							# all points to the right (below) the line joining the extreme points below to the
							# lower hull
							# ignore all points on the line joining the extreme points since they cannot be
							# part of the convex hull

							UpperCAmelCase	:						List[str]					=						points[0]
							UpperCAmelCase	:						Union[str, Any]					=						points[n - 1]

							UpperCAmelCase	:						int					=						{left_most_point, right_most_point}
							UpperCAmelCase	:						Dict					=						[]
							UpperCAmelCase	:						Any					=						[]

							for i in range(1      ,							n - 1						):
														UpperCAmelCase	:						Any					=						_det(_lowercase      ,							_lowercase      ,							points[i]						)

														if det > 0:
																					upper_hull.append(points[i]						)
														elif det < 0:
																					lower_hull.append(points[i]						)

							_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)
							_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)

							return sorted(_lowercase						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     None:
							if points:
														UpperCAmelCase	:						Union[str, Any]					=						None
														UpperCAmelCase	:						int					=						float("""-inf"""						)
														UpperCAmelCase	:						Tuple					=						[]

														for p in points:
																					UpperCAmelCase	:						List[Any]					=						_det(_lowercase      ,							_lowercase      ,							_lowercase						)

																					if det > 0:
																												candidate_points.append(_lowercase						)

																												if det > extreme_point_distance:
																																			UpperCAmelCase	:						List[str]					=						det
																																			UpperCAmelCase	:						Optional[Any]					=						p

														if extreme_point:
																					_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)
																					convex_set.add(_lowercase						)
																					_construct_hull(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						)



def  __lowerCamelCase    (   _lowercase						) ->     list[Point]:
							UpperCAmelCase	:						Any					=						sorted(_validate_input(_lowercase						)						)
							UpperCAmelCase	:						Union[str, Any]					=						len(_lowercase						)

							UpperCAmelCase	:						Dict					=						points[:2]
							for i in range(2      ,							_lowercase						):
														UpperCAmelCase	:						Dict					=						_det(convex_hull[1]      ,							convex_hull[0]      ,							points[i]						)
														if det > 0:
																					convex_hull.insert(0      ,							points[i]						)
																					break
														elif det < 0:
																					convex_hull.append(points[i]						)
																					break
														else:
																					UpperCAmelCase	:						Dict					=						points[i]
							i += 1

							for j in range(_lowercase      ,							_lowercase						):
														if (
														    _det(convex_hull[0]      ,							convex_hull[-1]      ,							points[j]						) > 0
														    and _det(convex_hull[-1]      ,							convex_hull[0]      ,							points[1]						) < 0
														):
																					# The point lies within the convex hull
																					continue

														convex_hull.insert(0      ,							points[j]						)
														convex_hull.append(points[j]						)
														while _det(convex_hull[0]      ,							convex_hull[1]      ,							convex_hull[2]						) >= 0:
																					del convex_hull[1]
														while _det(convex_hull[-1]      ,							convex_hull[-2]      ,							convex_hull[-3]						) <= 0:
																					del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
							return sorted(convex_hull[1:] if len(_lowercase						) > 3 else convex_hull						)



def  __lowerCamelCase    (   ) ->     Tuple:
							UpperCAmelCase	:						Tuple					=						[
							    (0, 3),
							    (2, 2),
							    (1, 1),
							    (2, 1),
							    (3, 0),
							    (0, 0),
							    (3, 3),
							    (2, -1),
							    (2, -4),
							    (1, -3),
							]
							# the convex set of points is
							# [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
							UpperCAmelCase	:						Tuple					=						convex_hull_bf(_lowercase						)

							UpperCAmelCase	:						List[Any]					=						convex_hull_recursive(_lowercase						)
							assert results_bf == results_recursive

							UpperCAmelCase	:						Union[str, Any]					=						convex_hull_melkman(_lowercase						)
							assert results_bf == results_melkman

							print(_lowercase						)


if __name__ == "__main__":
							main()

