



"""simple docstring"""




import numpy as np


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			List[Any]:






      '''simple docstring'''


      lowercase  :      str					=   n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      lowercase  :      int					=   np.floor(_UpperCAmelCase				)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      lowercase  :      Optional[Any]					=   int(n_draws - floored.sum()				)
      if need_to_add > 0:
            lowercase  :      List[str]					=   continuous - floored
            lowercase  :      List[Any]					=   np.sort(np.unique(_UpperCAmelCase				)				)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  ((lowercase)	,    )  :      Optional[int]					=   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
                  lowercase  :      Optional[int]					=   min(len(_UpperCAmelCase				)	,      _UpperCAmelCase				)
                  lowercase  :      List[str]					=   rng.choice(_UpperCAmelCase	,      size=_UpperCAmelCase	,      replace=_UpperCAmelCase				)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa				)





def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase=10				)     ->			Optional[int]:






      '''simple docstring'''


      lowercase	,    lowercase  :      List[str]					=   np.unique(_UpperCAmelCase	,      return_inverse=_UpperCAmelCase				)
      lowercase  :      Tuple					=   classes.shape[0]
      lowercase  :      int					=   np.bincount(_UpperCAmelCase				)
      if np.min(_UpperCAmelCase				) < 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)				)
      lowercase  :      Union[str, Any]					=   np.split(np.argsort(_UpperCAmelCase	,      kind='mergesort'				)	,      np.cumsum(_UpperCAmelCase				)[:-1]				)
      for _ in range(_UpperCAmelCase				):
            lowercase  :      Union[str, Any]					=   approximate_mode(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)
            lowercase  :      Optional[Any]					=   class_counts - n_i
            lowercase  :      Union[str, Any]					=   approximate_mode(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)

            lowercase  :      List[str]					=   []
            lowercase  :      List[Any]					=   []

            for i in range(_UpperCAmelCase				):
                  lowercase  :      str					=   rng.permutation(class_counts[i]				)
                  lowercase  :      Union[str, Any]					=   class_indices[i].take(_UpperCAmelCase	,      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]]				)
            lowercase  :      Optional[int]					=   rng.permutation(_UpperCAmelCase				)
            lowercase  :      Any					=   rng.permutation(_UpperCAmelCase				)

            yield train, test





"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				(HeunDiscreteScheduler,)
   _lowerCamelCase												=				10


   def        lowercase				(							self      :							List[Any],     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase			)
         return config


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],     [0.0002, 0.002, 0.02]			):
               self.check_over_configs(beta_start=lowerCAmelCase,     beta_end=lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Any:
         for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase,     clip_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Tuple:
         lowercase  :      Tuple					=   self.scheduler_classes[0]
         lowercase  :      int					=   self.get_scheduler_config()
         lowercase  :      str					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      Tuple					=   self.dummy_model()
         lowercase  :      Dict					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      Optional[Any]					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      Any					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Union[str, Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Tuple					=   output.prev_sample

         lowercase  :      Tuple					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Tuple			)    ->       List[str]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      Any					=   self.get_scheduler_config(prediction_type='v_prediction'			)
         lowercase  :      List[str]					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      List[Any]					=   self.dummy_model()
         lowercase  :      List[Any]					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      List[str]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Any					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      List[str]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      Optional[int]					=   self.scheduler_classes[0]
         lowercase  :      Optional[int]					=   self.get_scheduler_config()
         lowercase  :      Dict					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      int					=   self.dummy_model()
         lowercase  :      Tuple					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               lowercase  :      Optional[Any]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Optional[int]					=   output.prev_sample

         lowercase  :      str					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if str(lowerCAmelCase			).startswith('cpu'			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         elif str(lowerCAmelCase			).startswith('mps'			):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002			) < 1e-2
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      str					=   self.get_scheduler_config()
         lowercase  :      Tuple					=   scheduler_class(**lowerCAmelCase,     use_karras_sigmas=lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      Dict					=   self.dummy_model()
         lowercase  :      Any					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for t in scheduler.timesteps:
               lowercase  :      int					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      int					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      str					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Union[str, Any]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         assert abs(result_sum.item() - 0.0_0015			) < 1e-2
         assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07			) < 1e-2
