
"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class        _UpperCAmelCase							(						A__							):
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		(VQDiffusionScheduler,)




      def       a_ (							self				,    **lowercase_							)  ->   str:
          UpperCAmelCase       =   {
              "num_vec_classes": 4_0_9_7,
              "num_train_timesteps": 1_0_0,
          }

          config.update(**_A							)
          return config




      def       a_ (							self				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   4
          UpperCAmelCase       =   8
          UpperCAmelCase       =   8

          UpperCAmelCase       =   torch.randint(0				,    _A				,    (batch_size, height * width)							)

          return sample




      @property
      def       a_ (							self							)  ->   Dict:
          assert False




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:






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

          return model




      def       a_ (							self							)  ->   Optional[int]:






          for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=_A							)




      def       a_ (							self							)  ->   Dict:






          for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
              self.check_over_configs(num_vec_classes=_A							)




      def       a_ (							self							)  ->   Dict:






          for t in [0, 5_0, 9_9]:
              self.check_over_forward(time_step=_A							)




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





"""simple docstring"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_         =  '''Alexander Joslin'''




def       lowercase__  (   lowerCAmelCase			:   int	)	-> int:






    """simple docstring"""




    if number <= 0:
        UpperCAmelCase       =   F"n must be greater than 0. Got n = {number}"
        raise ValueError(lowerCAmelCase	)

    UpperCAmelCase       =   [number + 1] * (number + 1)

    # starting position
    UpperCAmelCase       =   0
    for i in range(1	,			lowerCAmelCase	):
        UpperCAmelCase       =   min(table[i + 1]	,			table[i] + 1	)
        # check if out of bounds
        if i * 2 <= number:
            UpperCAmelCase       =   min(table[i * 2]	,			table[i] + 1	)
        # check if out of bounds
        if i * 3 <= number:
            UpperCAmelCase       =   min(table[i * 3]	,			table[i] + 1	)
    return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



