
import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__magic_name__				( __lowercase					):


  """simple docstring"""





  _UpperCamelCase						 = (VQDiffusionScheduler,)


  def     _UpperCAmelCase  (					self	,		**a__		):
    _lowerCamelCase      =						{
        '''num_vec_classes''': 40_97,
        '''num_train_timesteps''': 1_00,
    }

    config.update(**A__		)
    return config


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						4
    _lowerCamelCase      =						8
    _lowerCamelCase      =						8

    _lowerCamelCase      =						torch.randint(0	,		A__	,		(batch_size, height * width)		)

    return sample


  @property
  def     _UpperCAmelCase  (					self		):
    assert False


  def     _UpperCAmelCase  (					self	,		a__		):
    def model(a__	,		a__	,		*a__		):
      _lowerCamelCase    ,    _lowerCamelCase      =						sample.shape
      _lowerCamelCase      =						torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)		)
      _lowerCamelCase      =						F.log_softmax(logits.double()	,		dim=1		).float()
      return return_value

    return model


  def     _UpperCAmelCase  (					self		):
    for timesteps in [2, 5, 1_00, 10_00]:
      self.check_over_configs(num_train_timesteps=A__		)


  def     _UpperCAmelCase  (					self		):
    for num_vec_classes in [5, 1_00, 10_00, 40_00]:
      self.check_over_configs(num_vec_classes=A__		)


  def     _UpperCAmelCase  (					self		):
    for t in [0, 50, 99]:
      self.check_over_forward(time_step=A__		)


  def     _UpperCAmelCase  (					self		):
    pass







def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				index
  _snake_case		=				2 * index + 1
  _snake_case		=				2 * index + 2
  if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
    _snake_case		=				left_index

  if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
    _snake_case		=				right_index

  if largest != index:
    _snake_case,	_snake_case		=				unsorted[index], unsorted[largest]
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)



def  snake_case_(_UpperCamelCase	)				->      Any:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  for i in range(n // 2 - 1					,			-1					,			-1	):
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)
  for i in range(n - 1					,			0					,			-1	):
    _snake_case,	_snake_case		=				unsorted[i], unsorted[0]
    heapify(_UpperCamelCase					,			0					,			_UpperCamelCase	)
  return unsorted


if __name__ == "__main__":
 __A     						=      input('''Enter numbers separated by a comma:\n''').strip()
 __A     						=      [int(item) for item in user_input.split(''',''')]
 print(heap_sort(unsorted))

