






"""simple docstring"""





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					int						=			(EulerAncestralDiscreteScheduler,)
    SCREAMING_SNAKE_CASE						:					Optional[Any]						=			1_0






    def        UpperCamelCase	(	self					:							Tuple	,						**__SCREAMING_SNAKE_CASE					:							Dict )					->		List[Any]:
      lowerCamelCase_   					=						{
          'num_train_timesteps': 1100,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**__SCREAMING_SNAKE_CASE )
      return config






    def        UpperCamelCase	(	self					:							int )					->		int:
      for timesteps in [10, 50, 100, 1000]:
        self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							List[str] )					->		List[str]:
      for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]	,						[0.0_002, 0.002, 0.02] ):
        self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE	,						beta_end=__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Optional[Any]:
      for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Dict )					->		Optional[int]:
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							List[str] )					->		List[str]:
      lowerCamelCase_   					=						self.scheduler_classes[0]
      lowerCamelCase_   					=						self.get_scheduler_config()
      lowerCamelCase_   					=						scheduler_class(**__SCREAMING_SNAKE_CASE )

      scheduler.set_timesteps(self.num_inference_steps )

      lowerCamelCase_   					=						torch.manual_seed(0 )

      lowerCamelCase_   					=						self.dummy_model()
      lowerCamelCase_   					=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      lowerCamelCase_   					=						sample.to(__SCREAMING_SNAKE_CASE )

      for i, t in enumerate(scheduler.timesteps ):
        lowerCamelCase_   					=						scheduler.scale_model_input(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        lowerCamelCase_   					=						model(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        lowerCamelCase_   					=						scheduler.step(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						generator=__SCREAMING_SNAKE_CASE )
        lowerCamelCase_   					=						output.prev_sample

      lowerCamelCase_   					=						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
      lowerCamelCase_   					=						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )

      assert abs(result_sum.item() - 152.3_192 ) < 1e-2
      assert abs(result_mean.item() - 0.1_983 ) < 1e-3






    def        UpperCamelCase	(	self					:							List[str] )					->		List[str]:
      lowerCamelCase_   					=						self.scheduler_classes[0]
      lowerCamelCase_   					=						self.get_scheduler_config(prediction_type='v_prediction' )
      lowerCamelCase_   					=						scheduler_class(**__SCREAMING_SNAKE_CASE )

      scheduler.set_timesteps(self.num_inference_steps )

      lowerCamelCase_   					=						torch.manual_seed(0 )

      lowerCamelCase_   					=						self.dummy_model()
      lowerCamelCase_   					=						self.dummy_sample_deter * scheduler.init_noise_sigma
      lowerCamelCase_   					=						sample.to(__SCREAMING_SNAKE_CASE )

      for i, t in enumerate(scheduler.timesteps ):
        lowerCamelCase_   					=						scheduler.scale_model_input(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        lowerCamelCase_   					=						model(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        lowerCamelCase_   					=						scheduler.step(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						generator=__SCREAMING_SNAKE_CASE )
        lowerCamelCase_   					=						output.prev_sample

      lowerCamelCase_   					=						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
      lowerCamelCase_   					=						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )

      assert abs(result_sum.item() - 108.4_439 ) < 1e-2
      assert abs(result_mean.item() - 0.1_412 ) < 1e-3






    def        UpperCamelCase	(	self					:							List[Any] )					->		Any:
      lowerCamelCase_   					=						self.scheduler_classes[0]
      lowerCamelCase_   					=						self.get_scheduler_config()
      lowerCamelCase_   					=						scheduler_class(**__SCREAMING_SNAKE_CASE )

      scheduler.set_timesteps(self.num_inference_steps	,						device=__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						torch.manual_seed(0 )

      lowerCamelCase_   					=						self.dummy_model()
      lowerCamelCase_   					=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      lowerCamelCase_   					=						sample.to(__SCREAMING_SNAKE_CASE )

      for t in scheduler.timesteps:
        lowerCamelCase_   					=						scheduler.scale_model_input(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        lowerCamelCase_   					=						model(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        lowerCamelCase_   					=						scheduler.step(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						generator=__SCREAMING_SNAKE_CASE )
        lowerCamelCase_   					=						output.prev_sample

      lowerCamelCase_   					=						torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
      lowerCamelCase_   					=						torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )

      assert abs(result_sum.item() - 152.3_192 ) < 1e-2
      assert abs(result_mean.item() - 0.1_983 ) < 1e-3












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # 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(_lowerCamelCase			)			)[::-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(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 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(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    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(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




