
import numpy as np
def lowerCamelCase_       (			A			:      str		,					A			:      Dict		,					A			:      Any						):




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




def lowerCamelCase_       (			A			:      List[str]		,					A			:      Dict		,					A			:      str		,					A			:      Optional[Any]		,					A			:      Any=10						):




      """simple docstring"""
      lowerCAmelCase_			,		lowerCAmelCase_						=      np.unique(A		,					return_inverse=A						)
      lowerCAmelCase_						=      classes.shape[0]
      lowerCAmelCase_						=      np.bincount(A						)
      if np.min(A						) < 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_						=      np.split(np.argsort(A		,					kind='''mergesort'''						)		,					np.cumsum(A						)[:-1]						)
      for _ in range(A						):
            lowerCAmelCase_						=      approximate_mode(A		,					A		,					A						)
            lowerCAmelCase_						=      class_counts - n_i
            lowerCAmelCase_						=      approximate_mode(A		,					A		,					A						)

            lowerCAmelCase_						=      []
            lowerCAmelCase_						=      []

            for i in range(A						):
                  lowerCAmelCase_						=      rng.permutation(class_counts[i]						)
                  lowerCAmelCase_						=      class_indices[i].take(A		,					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_						=      rng.permutation(A						)
            lowerCAmelCase_						=      rng.permutation(A						)

            yield train, test





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,					[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







      def lowercase__		( self):
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=_UpperCAmelCase)







      def lowercase__		( self):
            for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=_UpperCAmelCase)







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



