






'''simple docstring'''


import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 				lowerCAmelCase_						(						A__						):
 __lowerCamelCase  :							Union[str, Any]    					=   (VQDiffusionScheduler,)




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = {
         "num_vec_classes": 4097,
         "num_train_timesteps": 100,
     }

     config.update(**_A						)
     return config




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Optional[int]:
     _lowerCAmelCase		     = 4
     _lowerCAmelCase		     = 8
     _lowerCAmelCase		     = 8

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

     return sample




 @property
 def 							_snake_case				(		self						)      ->							List[Any]:
     assert False




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Union[str, Any]:






     def model(_lowerCAmelCase     ,       _lowerCAmelCase     ,       *_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 							_snake_case				(		self						)      ->							int:






     for timesteps in [2, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_A						)




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






     for num_vec_classes in [5, 100, 1000, 4000]:
         self.check_over_configs(num_vec_classes=_A						)




 def 							_snake_case				(		self						)      ->							List[str]:






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





 def 							_snake_case				(		self						)      ->							Dict:
     pass









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    _lowerCAmelCase		     = [0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    _lowerCAmelCase		     = 1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(SCREAMING_SNAKE_CASE_						,    n + 1      ):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


