

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest



class   __lowerCamelCase (_a	):
       _lowercase       							=    (VQDiffusionScheduler,)







       def 		snake_case_  ( self:   str,**A_:   Dict				):






            '''simple docstring'''




            __UpperCamelCase   = {
                'num_vec_classes': 4097,
                'num_train_timesteps': 100,
            }

            config.update(**A_				)
            return config







       def 		snake_case_  ( self:   Any,A_:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = 4
            __UpperCamelCase   = 8
            __UpperCamelCase   = 8

            __UpperCamelCase   = torch.randint(0,A_,(batch_size, height * width)				)

            return sample







       @property
       def 		snake_case_  ( self:   Any				):






            '''simple docstring'''




            assert False







       def 		snake_case_  ( self:   Tuple,A_:   Dict				):






            '''simple docstring'''




            def model(A_:   List[Any],A_:   Dict,*A_:   List[Any]				):
                 __UpperCamelCase,						__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 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




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







       def 		snake_case_  ( self:   Optional[Any]				):






            '''simple docstring'''




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







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




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







       def 		snake_case_  ( self:   str				):






            '''simple docstring'''




            pass






def 		_A		(		_lowercase   )				->   bool:


     """simple docstring"""


     if not isinstance(_lowercase				,     _lowercase   ):
          raise ValueError('Input series is not valid, valid series - [1, 2/3, 2]'   )
     if len(_lowercase   ) == 0:
          raise ValueError('Input list must be a non empty list'   )
     if len(_lowercase   ) == 1 and series[0] != 0:
          return True
     __UpperCamelCase   = []
     __UpperCamelCase   = len(_lowercase   )
     for i in range(0				,     _lowercase   ):
          if series[i] == 0:
               raise ValueError('Input series cannot have 0 as an element'   )
          rec_series.append(1 / series[i]   )
     __UpperCamelCase   = rec_series[1] - rec_series[0]
     for index in range(2				,     _lowercase   ):
          if rec_series[index] - rec_series[index - 1] != common_diff:
               return False
     return True






def 		_A		(		_lowercase   )				->   float:


     """simple docstring"""


     if not isinstance(_lowercase				,     _lowercase   ):
          raise ValueError('Input series is not valid, valid series - [2, 4, 6]'   )
     if len(_lowercase   ) == 0:
          raise ValueError('Input list must be a non empty list'   )
     __UpperCamelCase   = 0
     for val in series:
          answer += 1 / val
     return len(_lowercase   ) / answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()


