






"""simple docstring"""


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  (DDPMParallelScheduler,)





    def        lowerCamelCase__   (							self			:      Optional[Any]       ,	**A			:      Dict    ):


      '''simple docstring'''




      a  :    Optional[int]       =     {
          'num_train_timesteps': 1_0_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**A    )
      return config





    def        lowerCamelCase__   (							self			:      Tuple    ):


      '''simple docstring'''




      for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A    )





    def        lowerCamelCase__   (							self			:      Dict    ):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,	[0.0_02, 0.02, 0.2, 2]    ):
        self.check_over_configs(beta_start=A       ,	beta_end=A    )





    def        lowerCamelCase__   (							self			:      int    ):


      '''simple docstring'''




      for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=A    )





    def        lowerCamelCase__   (							self			:      Any    ):


      '''simple docstring'''




      for variance in ["fixed_small", "fixed_large", "other"]:
        self.check_over_configs(variance_type=A    )





    def        lowerCamelCase__   (							self			:      int    ):


      '''simple docstring'''




      for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=A    )





    def        lowerCamelCase__   (							self			:      List[str]    ):


      '''simple docstring'''




      self.check_over_configs(thresholding=A    )
      for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(
              thresholding=A       ,	prediction_type=A       ,	sample_max_value=A       ,	)





    def        lowerCamelCase__   (							self			:      str    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Dict    ):


      '''simple docstring'''




      for t in [0, 5_0_0, 9_9_9]:
        self.check_over_forward(time_step=A    )





    def        lowerCamelCase__   (							self			:      int    ):


      '''simple docstring'''




      a  :    Any       =     self.scheduler_classes[0]
      a  :    Optional[int]       =     self.get_scheduler_config()
      a  :    str       =     scheduler_class(**A    )

      assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_8_7    ) - 0.0_09_79    )    ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ) - 0.02    )    ) < 1E-5





    def        lowerCamelCase__   (							self			:      List[Any]    ):


      '''simple docstring'''




      a  :    int       =     self.scheduler_classes[0]
      a  :    Union[str, Any]       =     self.get_scheduler_config()
      a  :    List[Any]       =     scheduler_class(**A    )

      a  :    str       =     len(A    )

      a  :    Union[str, Any]       =     self.dummy_model()
      a  :    int       =     self.dummy_sample_deter
      a  :    Optional[int]       =     self.dummy_sample_deter + 0.1
      a  :    Union[str, Any]       =     self.dummy_sample_deter - 0.1

      a  :    Tuple       =     samplea.shape[0]
      a  :    List[Any]       =     torch.stack([samplea, samplea, samplea]       ,	dim=0    )
      a  :    Dict       =     torch.arange(A    )[0:3, None].repeat(1       ,	A    )

      a  :    Optional[int]       =     model(samples.flatten(0       ,	1    )       ,	timesteps.flatten(0       ,	1    )    )
      a  :    Dict       =     scheduler.batch_step_no_noise(A       ,	timesteps.flatten(0       ,	1    )       ,	samples.flatten(0       ,	1    )    )

      a  :    str       =     torch.sum(torch.abs(A    )    )
      a  :    Union[str, Any]       =     torch.mean(torch.abs(A    )    )

      assert abs(result_sum.item() - 11_53.18_33    ) < 1E-2
      assert abs(result_mean.item() - 0.50_05    ) < 1E-3





    def        lowerCamelCase__   (							self			:      Tuple    ):


      '''simple docstring'''




      a  :    int       =     self.scheduler_classes[0]
      a  :    Optional[Any]       =     self.get_scheduler_config()
      a  :    int       =     scheduler_class(**A    )

      a  :    Dict       =     len(A    )

      a  :    List[Any]       =     self.dummy_model()
      a  :    Union[str, Any]       =     self.dummy_sample_deter
      a  :    str       =     torch.manual_seed(0    )

      for t in reversed(range(A    )    ):
        # 1. predict noise residual
        a  :    Any       =     model(A       ,	A    )

        # 2. predict previous mean of sample x_t-1
        a  :    int       =     scheduler.step(A       ,	A       ,	A       ,	generator=A    ).prev_sample

        a  :    int       =     pred_prev_sample

      a  :    Union[str, Any]       =     torch.sum(torch.abs(A    )    )
      a  :    str       =     torch.mean(torch.abs(A    )    )

      assert abs(result_sum.item() - 2_58.96_06    ) < 1E-2
      assert abs(result_mean.item() - 0.33_72    ) < 1E-3





    def        lowerCamelCase__   (							self			:      Dict    ):


      '''simple docstring'''




      a  :    List[str]       =     self.scheduler_classes[0]
      a  :    str       =     self.get_scheduler_config(prediction_type='v_prediction'    )
      a  :    str       =     scheduler_class(**A    )

      a  :    str       =     len(A    )

      a  :    Union[str, Any]       =     self.dummy_model()
      a  :    Any       =     self.dummy_sample_deter
      a  :    Dict       =     torch.manual_seed(0    )

      for t in reversed(range(A    )    ):
        # 1. predict noise residual
        a  :    Optional[Any]       =     model(A       ,	A    )

        # 2. predict previous mean of sample x_t-1
        a  :    Optional[Any]       =     scheduler.step(A       ,	A       ,	A       ,	generator=A    ).prev_sample

        a  :    str       =     pred_prev_sample

      a  :    str       =     torch.sum(torch.abs(A    )    )
      a  :    str       =     torch.mean(torch.abs(A    )    )

      assert abs(result_sum.item() - 2_02.02_96    ) < 1E-2
      assert abs(result_mean.item() - 0.26_31    ) < 1E-3





    def        lowerCamelCase__   (							self			:      Any    ):


      '''simple docstring'''




      a  :    List[Any]       =     self.scheduler_classes[0]
      a  :    str       =     self.get_scheduler_config()
      a  :    Optional[int]       =     scheduler_class(**A    )

      a  :    Any       =     [1_0_0, 8_7, 5_0, 1, 0]

      scheduler.set_timesteps(timesteps=A    )

      a  :    Optional[Any]       =     scheduler.timesteps

      for i, timestep in enumerate(A    ):
        if i == len(A    ) - 1:
          a  :    Tuple       =     -1
        else:
          a  :    int       =     timesteps[i + 1]

        a  :    int       =     scheduler.previous_timestep(A    )
        a  :    Any       =     prev_t.item()

        self.assertEqual(A       ,	A    )





    def        lowerCamelCase__   (							self			:      List[Any]    ):


      '''simple docstring'''




      a  :    Tuple       =     self.scheduler_classes[0]
      a  :    List[str]       =     self.get_scheduler_config()
      a  :    Tuple       =     scheduler_class(**A    )

      a  :    Dict       =     [1_0_0, 8_7, 5_0, 5_1, 0]

      with self.assertRaises(A       ,	msg='`custom_timesteps` must be in descending order.'    ):
        scheduler.set_timesteps(timesteps=A    )





    def        lowerCamelCase__   (							self			:      Optional[int]    ):


      '''simple docstring'''




      a  :    List[str]       =     self.scheduler_classes[0]
      a  :    Union[str, Any]       =     self.get_scheduler_config()
      a  :    Dict       =     scheduler_class(**A    )

      a  :    Dict       =     [1_0_0, 8_7, 5_0, 1, 0]
      a  :    Tuple       =     len(A    )

      with self.assertRaises(A       ,	msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'    ):
        scheduler.set_timesteps(num_inference_steps=A       ,	timesteps=A    )





    def        lowerCamelCase__   (							self			:      Dict    ):


      '''simple docstring'''




      a  :    Union[str, Any]       =     self.scheduler_classes[0]
      a  :    str       =     self.get_scheduler_config()
      a  :    str       =     scheduler_class(**A    )

      a  :    Union[str, Any]       =     [scheduler.config.num_train_timesteps]

      with self.assertRaises(
          A       ,	msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,	):
        scheduler.set_timesteps(timesteps=A    )












"""simple docstring"""


import math

from numpy import inf
from scipy.integrate import quad




def     snake_case						(A_   :float   ):







  '''simple docstring'''

  if num <= 0:
    raise ValueError('math domain error'   )

  return quad(A_			,    0			,    A_			,    args=(A_)   )[0]




def     snake_case						(A_   :float			,    A_   :float   ):







  '''simple docstring'''

  return math.pow(A_			,    z - 1   ) * math.exp(-x   )


if __name__ == "__main__":
   from doctest import testmod

   testmod()




