





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest




class 				UpperCamelCase (    _SCREAMING_SNAKE_CASE    ):



      """simple docstring"""






      UpperCAmelCase_						:      List[str]       =(VQDiffusionScheduler,)







      def 						UpperCAmelCase		( self				,						**UpperCAmelCase   )       ->   Tuple:






        '''simple docstring'''






        __snake_case   :				Any        =       {
            "num_vec_classes": 4097,
            "num_train_timesteps": 100,
        }

        config.update(**__A   )
        return config







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				Dict        =       4
        __snake_case   :				int        =       8
        __snake_case   :				Dict        =       8

        __snake_case   :				str        =       torch.randint(0				,						__A				,						(batch_size, height * width)   )

        return sample







      @property
      def 						UpperCAmelCase		( self   )       ->   Optional[Any]:






        '''simple docstring'''






        assert False







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Tuple:






        '''simple docstring'''






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

        return model







      def 						UpperCAmelCase		( self   )       ->   Dict:






        '''simple docstring'''






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







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






        '''simple docstring'''






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







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






        '''simple docstring'''






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







      def 						UpperCAmelCase		( self   )       ->   str:






        '''simple docstring'''






        pass








import torch
from torch import autograd




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Dict        =       inputs.numel()
        __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
        if sigmoid:
          __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
        else:
          __snake_case   :				Optional[int]        =       (inputs > threshold).type(inputs.type()   )
        if mask.sum() < nb_min:
          # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
          __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
          __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        return gradOutput, None, None




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        __snake_case   :				Optional[int]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[int]        =       mask.flatten()
        __snake_case   :				Union[str, Any]        =       0
        __snake_case   :				Any        =       1
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:






        '''simple docstring'''






        return gradOutput, None




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[Any]        =       mask.flatten()
        __snake_case   :				Tuple        =       0
        __snake_case   :				Any        =       1
        return mask

