





"""simple docstring"""




from __future__ import annotations

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

class 					_UpperCAmelCase    (  __A						):



      '''simple docstring'''
      lowercase_ :					int             =  1
      lowercase_ :					Dict             =  2
      lowercase_ :					List[str]             =  3

      def __repr__(	self    ):

        """simple docstring"""

        return F"""{self.__class__.__name__}.{self.name}"""





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Dict									=    point
  return degrees(atana(y - miny					,   x - minx   )   )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    starting
  A_    :			Optional[int]									=    via
  A_    :			Dict									=    target
  A_    :			int									=    degrees(atana(ya - ya					,   xa - xa   )   )
  via_angle %= 360
  A_    :			Any									=    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__						(	_UpperCAmelCase   ):






  """simple docstring"""





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

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

  A_    :			Dict									=    sorted(a_					,   key=lambda _UpperCAmelCase   : angle_comparer(a_					,   a_					,   a_   )   )
  # 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)   )

  A_    :			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.
  A_    :			Union[str, Any]									=    Direction.left

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

      if next_direction == Direction.left:
        A_    :			Union[str, 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(a_   )




'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
