





import numpy as np
def 			__UpperCamelCase				(			lowercase__	:       Any		,    lowercase__	:       int		,    lowercase__	:       Dict		)	->      Optional[int]:
    '''simple docstring'''





    lowerCAmelCase_					:	Any						=   n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    lowerCAmelCase_					:	Any						=   np.floor(lowercase__		)
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    lowerCAmelCase_					:	Union[str, Any]						=   int(n_draws - floored.sum()		)
    if need_to_add > 0:
        lowerCAmelCase_					:	str						=   continuous - floored
        lowerCAmelCase_					:	Union[str, Any]						=   np.sort(np.unique(lowercase__		)		)[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            (lowerCAmelCase_		)					:	str						=   np.where(remainder == value		)
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            lowerCAmelCase_					:	Any						=   min(len(lowercase__		)		,    lowercase__		)
            lowerCAmelCase_					:	Dict						=   rng.choice(lowercase__		,    size=lowercase__		,    replace=lowercase__		)
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa		)

def 			__UpperCamelCase				(			lowercase__	:       Any		,    lowercase__	:       Tuple		,    lowercase__	:       Optional[int]		,    lowercase__	:       Tuple		,    lowercase__	:       Union[str, Any]=10		)	->      str:
    '''simple docstring'''





    lowerCAmelCase_					:	List[Any]						=   np.unique(lowercase__		,    return_inverse=lowercase__		)
    lowerCAmelCase_					:	List[str]						=   classes.shape[0]
    lowerCAmelCase_					:	List[str]						=   np.bincount(lowercase__		)
    if np.min(lowercase__		) < 2:
        raise ValueError("""Minimum class count error"""		)
    if n_train < n_classes:
        raise ValueError(
            """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)		)
    if n_test < n_classes:
        raise ValueError(
            """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)		)
    lowerCAmelCase_					:	List[Any]						=   np.split(np.argsort(lowercase__		,    kind="""mergesort"""		)		,    np.cumsum(lowercase__		)[:-1]		)
    for _ in range(lowercase__		):
        lowerCAmelCase_					:	Any						=   approximate_mode(lowercase__		,    lowercase__		,    lowercase__		)
        lowerCAmelCase_					:	List[Any]						=   class_counts - n_i
        lowerCAmelCase_					:	Optional[int]						=   approximate_mode(lowercase__		,    lowercase__		,    lowercase__		)

        lowerCAmelCase_					:	List[Any]						=   []
        lowerCAmelCase_					:	Optional[Any]						=   []

        for i in range(lowercase__		):
            lowerCAmelCase_					:	Optional[int]						=   rng.permutation(class_counts[i]		)
            lowerCAmelCase_					:	Union[str, Any]						=   class_indices[i].take(lowercase__		,    mode="""clip"""		)
            train.extend(perm_indices_class_i[: n_i[i]]		)
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]		)
        lowerCAmelCase_					:	Dict						=   rng.permutation(lowercase__		)
        lowerCAmelCase_					:	List[str]						=   rng.permutation(lowercase__		)

        yield train, test









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


