





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Union[str, Any]      =		1
       __snake_case						:   List[Any]      =		2
       __snake_case						:   Union[str, Any]      =		3



       def __repr__(     self	:   Optional[Any]			):
           return F'{self.__class__.__name__}.{self.name}'






def 			__UpperCamelCase				(			lowercase__	:       tuple[int, int]		,    lowercase__	:       int		,    lowercase__	:       int		)	->      float:
    '''simple docstring'''





    lowerCAmelCase_					:	str						=   point
    return degrees(atana(y - miny		,    x - minx		)		)
def 			__UpperCamelCase				(			lowercase__	:       tuple[int, int]		,    lowercase__	:       tuple[int, int]		,    lowercase__	:       tuple[int, int]		)	->      Direction:
    '''simple docstring'''





    lowerCAmelCase_					:	Dict						=   starting
    lowerCAmelCase_					:	Dict						=   via
    lowerCAmelCase_					:	int						=   target
    lowerCAmelCase_					:	Tuple						=   degrees(atana(ya - ya		,    xa - xa		)		)
    via_angle %= 360
    lowerCAmelCase_					:	Optional[int]						=   degrees(atana(ya - ya		,    xa - xa		)		)
    target_angle %= 360
    # t-
    #  \ \
    #   \ v
    #    \|
    #     s
    # via_angle is always lower than target_angle, if direction is left.
    # If they are same, it means they are on a same line of convex hull.
    if target_angle > via_angle:
        return Direction.left
    elif target_angle == via_angle:
        return Direction.straight
    else:
        return Direction.right

def 			__UpperCamelCase				(			lowercase__	:       list[tuple[int, int]]		)	->      list[tuple[int, int]]:
    '''simple docstring'''





    if len(lowercase__		) <= 2:
        # There is no convex hull
        raise ValueError("""graham_scan: argument must contain more than 3 points."""		)
    if len(lowercase__		) == 3:
        return points
    # find the lowest and the most left point
    lowerCAmelCase_					:	str						=   0
    lowerCAmelCase_					:	List[Any]						=   maxsize, maxsize
    for i, point in enumerate(lowercase__		):
        lowerCAmelCase_					:	Optional[int]						=   point[0]
        lowerCAmelCase_					:	Dict						=   point[1]
        if y < miny:
            lowerCAmelCase_					:	List[Any]						=   y
            lowerCAmelCase_					:	Tuple						=   x
            lowerCAmelCase_					:	Dict						=   i
        if y == miny and x < minx:
            lowerCAmelCase_					:	List[Any]						=   x
            lowerCAmelCase_					:	Dict						=   i

    # remove the lowest and the most left point from points for preparing for sort
    points.pop(lowercase__		)

    lowerCAmelCase_					:	List[str]						=   sorted(lowercase__		,    key=lambda lowercase__		: angle_comparer(lowercase__		,    lowercase__		,    lowercase__		)		)
    # This insert actually costs complexity,
    # and you should instead add (minx, miny) into stack later.
    # I'm using insert just for easy understanding.
    sorted_points.insert(0		,    (minx, miny)		)

    lowerCAmelCase_					:	deque[tuple[int, int]]						=   deque()
    stack.append(sorted_points[0]		)
    stack.append(sorted_points[1]		)
    stack.append(sorted_points[2]		)
    # In any ways, the first 3 points line are towards left.
    # Because we sort them the angle from minx, miny.
    lowerCAmelCase_					:	int						=   Direction.left

    for i in range(3		,    len(lowercase__		)		):
        while True:
            lowerCAmelCase_					:	Optional[int]						=   stack[-2]
            lowerCAmelCase_					:	List[Any]						=   stack[-1]
            lowerCAmelCase_					:	Any						=   sorted_points[i]
            lowerCAmelCase_					:	List[Any]						=   check_direction(lowercase__		,    lowercase__		,    lowercase__		)

            if next_direction == Direction.left:
                lowerCAmelCase_					:	List[Any]						=   Direction.left
                break
            if next_direction == Direction.straight:
                if current_direction == Direction.left:
                    # We keep current_direction as left.
                    # Because if the straight line keeps as straight,
                    # we want to know if this straight line is towards left.
                    break
                elif current_direction == Direction.right:
                    # If the straight line is towards right,
                    # every previous points on those straigh line is not convex hull.
                    stack.pop()
            if next_direction == Direction.right:
                stack.pop()
        stack.append(sorted_points[i]		)
    return list(lowercase__		)









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


