





"""simple docstring"""


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest



@require_torchsde
class      __SCREAMING_SNAKE_CASE    (						UpperCamelCase_   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					(DPMSolverSDEScheduler,)
  SCREAMING_SNAKE_CASE__							:Dict	     =					10

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   **__a   :       List[Any]	)		->				int:
        _UpperCamelCase      :	Optional[int]						=       {
            'num_train_timesteps': 1100,
            'beta_start': 0.00_01,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'noise_sampler_seed': 0,
        }

        config.update(**__a	)
        return config

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				int:






        for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Dict:






        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   :       List[str]	)		->				Optional[Any]:






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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Optional[Any]:






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

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

        scheduler.set_timesteps(self.num_inference_steps	)

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

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

              _UpperCamelCase      :	Optional[int]						=       model(__a					,   __a	)

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

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

        if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75	) < 1e-2
              assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77	) < 1e-3
        elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06	) < 1e-2
              assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52	) < 1e-3
        else:
              assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62	) < 1e-2
              assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26	) < 1e-3

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

        scheduler.set_timesteps(self.num_inference_steps	)

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

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

              _UpperCamelCase      :	Optional[Any]						=       model(__a					,   __a	)

              _UpperCamelCase      :	Tuple						=       scheduler.step(__a					,   __a					,   __a	)
              _UpperCamelCase      :	Optional[int]						=       output.prev_sample

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

        if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53	) < 1e-2
              assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84	) < 1e-3
        elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03	) < 1e-2
              assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97	) < 1e-3
        else:
              assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25	) < 1e-2
              assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21	) < 1e-3

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

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

        _UpperCamelCase      :	Dict						=       self.dummy_model()
        _UpperCamelCase      :	str						=       self.dummy_sample_deter.to(__a	) * scheduler.init_noise_sigma

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

              _UpperCamelCase      :	Tuple						=       model(__a					,   __a	)

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

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

        if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38	) < 1e-2
              assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35	) < 1e-3
        elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12	) < 1e-2
              assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71	) < 1e-3
        else:
              assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62	) < 1e-2
              assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26	) < 1e-3


  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        _UpperCamelCase      :	Union[str, Any]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	str						=       self.get_scheduler_config()
        _UpperCamelCase      :	Optional[int]						=       scheduler_class(**__a					,   use_karras_sigmas=__a	)

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

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

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

              _UpperCamelCase      :	Optional[Any]						=       model(__a					,   __a	)

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

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

        if torch_device in ["mps"]:
              assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88	) < 1e-2
              assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11	) < 1e-2
        elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25	) < 1e-2
              assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11	) < 1e-2
        else:
              assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72	) < 1e-2
              assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11	) < 1e-2







"""simple docstring"""


class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       Optional[Any]					,   __a   :       list[int]	)		->				None:
        _UpperCamelCase      :	Tuple						=       len(__a	)
        _UpperCamelCase      :	Dict						=       [0] * len_array

        if len_array > 0:
              _UpperCamelCase      :	Optional[Any]						=       array[0]

        for i in range(1					,   __a	):
              _UpperCamelCase      :	Tuple						=       self.prefix_sum[i - 1] + array[i]

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       int					,   __a   :       int	)		->				int:
        if start == 0:
              return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int	)		->				bool:
        _UpperCamelCase      :	int						=       {0}
        for sum_item in self.prefix_sum:
              if sum_item - target_sum in sums:
                    return True

              sums.add(__a	)

        return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()
