





"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:int		   =    (EulerAncestralDiscreteScheduler,)
  SCREAMING_SNAKE_CASE__							:Tuple		   =    10

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      **__a      :				str		)						-> int:
        _UpperCamelCase					:		List[str]  =				{
            "num_train_timesteps": 1100,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__a		)
        return config

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

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Optional[int]:
        for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,      [0.00_02, 0.0_02, 0.02]		):
              self.check_over_configs(beta_start=__a    ,      beta_end=__a		)

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

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

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Dict:
        _UpperCamelCase					:		int  =				self.scheduler_classes[0]
        _UpperCamelCase					:		Tuple  =				self.get_scheduler_config()
        _UpperCamelCase					:		Dict  =				scheduler_class(**__a		)

        scheduler.set_timesteps(self.num_inference_steps		)

        _UpperCamelCase					:		Any  =				torch.manual_seed(0		)

        _UpperCamelCase					:		Union[str, Any]  =				self.dummy_model()
        _UpperCamelCase					:		Dict  =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _UpperCamelCase					:		Dict  =				sample.to(__a		)

        for i, t in enumerate(scheduler.timesteps		):
              _UpperCamelCase					:		Optional[int]  =				scheduler.scale_model_input(__a    ,      __a		)

              _UpperCamelCase					:		Union[str, Any]  =				model(__a    ,      __a		)

              _UpperCamelCase					:		Dict  =				scheduler.step(__a    ,      __a    ,      __a    ,      generator=__a		)
              _UpperCamelCase					:		str  =				output.prev_sample

        _UpperCamelCase					:		List[Any]  =				torch.sum(torch.abs(__a		)		)
        _UpperCamelCase					:		List[str]  =				torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 1_52.31_92		) < 1e-2
        assert abs(result_mean.item() - 0.19_83		) < 1e-3

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Tuple:
        _UpperCamelCase					:		Union[str, Any]  =				self.scheduler_classes[0]
        _UpperCamelCase					:		int  =				self.get_scheduler_config(prediction_type="v_prediction"		)
        _UpperCamelCase					:		Any  =				scheduler_class(**__a		)

        scheduler.set_timesteps(self.num_inference_steps		)

        _UpperCamelCase					:		str  =				torch.manual_seed(0		)

        _UpperCamelCase					:		Tuple  =				self.dummy_model()
        _UpperCamelCase					:		Tuple  =				self.dummy_sample_deter * scheduler.init_noise_sigma
        _UpperCamelCase					:		Tuple  =				sample.to(__a		)

        for i, t in enumerate(scheduler.timesteps		):
              _UpperCamelCase					:		str  =				scheduler.scale_model_input(__a    ,      __a		)

              _UpperCamelCase					:		Union[str, Any]  =				model(__a    ,      __a		)

              _UpperCamelCase					:		Union[str, Any]  =				scheduler.step(__a    ,      __a    ,      __a    ,      generator=__a		)
              _UpperCamelCase					:		Optional[int]  =				output.prev_sample

        _UpperCamelCase					:		Optional[int]  =				torch.sum(torch.abs(__a		)		)
        _UpperCamelCase					:		List[Any]  =				torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 1_08.44_39		) < 1e-2
        assert abs(result_mean.item() - 0.14_12		) < 1e-3


  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> Optional[Any]:
        _UpperCamelCase					:		List[Any]  =				self.scheduler_classes[0]
        _UpperCamelCase					:		Tuple  =				self.get_scheduler_config()
        _UpperCamelCase					:		Optional[int]  =				scheduler_class(**__a		)

        scheduler.set_timesteps(self.num_inference_steps    ,      device=__a		)
        _UpperCamelCase					:		Any  =				torch.manual_seed(0		)

        _UpperCamelCase					:		List[str]  =				self.dummy_model()
        _UpperCamelCase					:		int  =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _UpperCamelCase					:		List[str]  =				sample.to(__a		)

        for t in scheduler.timesteps:
              _UpperCamelCase					:		Optional[int]  =				scheduler.scale_model_input(__a    ,      __a		)

              _UpperCamelCase					:		Union[str, Any]  =				model(__a    ,      __a		)

              _UpperCamelCase					:		Optional[Any]  =				scheduler.step(__a    ,      __a    ,      __a    ,      generator=__a		)
              _UpperCamelCase					:		List[Any]  =				output.prev_sample

        _UpperCamelCase					:		int  =				torch.sum(torch.abs(__a		)		)
        _UpperCamelCase					:		List[Any]  =				torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 1_52.31_92		) < 1e-2
        assert abs(result_mean.item() - 0.19_83		) < 1e-3







"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
