'''simple docstring'''




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 		__A       (__magic_name__   ):
       snake_case       :int				    =					(DDIMParallelScheduler,)
       snake_case       :Tuple				    =					(("eta", 0.0), ("num_inference_steps", 50))






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		{
                  "num_train_timesteps": 10_00,
                  "beta_start": 0.0_0_0_1,
                  "beta_end": 0.0_2,
                  "beta_schedule": "linear",
                  "clip_sample": True,
              }

              config.update(**UpperCamelCase_      )
              return config






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Optional[Any]       				=		self.get_scheduler_config(**UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		scheduler_class(**UpperCamelCase_      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		10, 0.0

              __UpperCAmelCase  :				List[Any]       				=		self.dummy_model()
              __UpperCAmelCase  :				List[Any]       				=		self.dummy_sample_deter

              scheduler.set_timesteps(UpperCamelCase_      )

              for t in scheduler.timesteps:
                     __UpperCAmelCase  :				Tuple       				=		model(UpperCamelCase_				,     UpperCamelCase_      )
                     __UpperCAmelCase  :				Optional[Any]       				=		scheduler.step(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample

              return sample






       def 			_snake_case						(      self      ):
              for timesteps in [1_00, 5_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=UpperCamelCase_      )

              __UpperCAmelCase  :				List[str]       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Optional[Any]       				=		self.get_scheduler_config(steps_offset=1      )
              __UpperCAmelCase  :				List[Any]       				=		scheduler_class(**UpperCamelCase_      )
              scheduler.set_timesteps(5      )
              assert torch.equal(scheduler.timesteps				,     torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )






       def 			_snake_case						(      self      ):
              for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]      ):
                     self.check_over_configs(beta_start=UpperCamelCase_				,     beta_end=UpperCamelCase_      )






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






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






       def 			_snake_case						(      self      ):
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_      )






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






       def 			_snake_case						(      self      ):
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for t, num_inference_steps in zip([1, 10, 50]				,     [10, 50, 5_00]      ):
                     self.check_over_forward(time_step=UpperCamelCase_				,     num_inference_steps=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for t, eta in zip([1, 10, 49]				,     [0.0, 0.5, 1.0]      ):
                     self.check_over_forward(time_step=UpperCamelCase_				,     eta=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Dict       				=		self.get_scheduler_config()
              __UpperCAmelCase  :				Optional[Any]       				=		scheduler_class(**UpperCamelCase_      )

              assert torch.sum(torch.abs(scheduler._get_variance(0				,     0      ) - 0.0      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_20				,     4_00      ) - 0.1_4_7_7_1      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_80				,     9_60      ) - 0.3_2_4_6_0      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(0				,     0      ) - 0.0      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_87				,     4_86      ) - 0.0_0_9_7_9      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_99				,     9_98      ) - 0.0_2      )      ) < 1E-5






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Optional[int]       				=		self.get_scheduler_config()
              __UpperCAmelCase  :				List[str]       				=		scheduler_class(**UpperCamelCase_      )

              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		10, 0.0
              scheduler.set_timesteps(UpperCamelCase_      )

              __UpperCAmelCase  :				Any       				=		self.dummy_model()
              __UpperCAmelCase  :				str       				=		self.dummy_sample_deter
              __UpperCAmelCase  :				Optional[Any]       				=		self.dummy_sample_deter + 0.1
              __UpperCAmelCase  :				List[str]       				=		self.dummy_sample_deter - 0.1

              __UpperCAmelCase  :				str       				=		samplea.shape[0]
              __UpperCAmelCase  :				Tuple       				=		torch.stack([samplea, samplea, samplea]				,     dim=0      )
              __UpperCAmelCase  :				List[Any]       				=		torch.arange(UpperCamelCase_      )[0:3, None].repeat(1				,     UpperCamelCase_      )

              __UpperCAmelCase  :				List[str]       				=		model(samples.flatten(0				,     1      )				,     timesteps.flatten(0				,     1      )      )
              __UpperCAmelCase  :				Tuple       				=		scheduler.batch_step_no_noise(UpperCamelCase_				,     timesteps.flatten(0				,     1      )				,     samples.flatten(0				,     1      )				,     UpperCamelCase_      )

              __UpperCAmelCase  :				int       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_1_4_7.7_9_0_4      ) < 1E-2
              assert abs(result_mean.item() - 0.4_9_8_2      ) < 1E-3






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		self.full_loop()

              __UpperCAmelCase  :				Optional[Any]       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				List[Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_7_2.0_0_6_7      ) < 1E-2
              assert abs(result_mean.item() - 0.2_2_3_9_6_7      ) < 1E-3






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		self.full_loop(prediction_type="v_prediction"      )

              __UpperCAmelCase  :				Any       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				List[Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 5_2.5_3_0_2      ) < 1E-2
              assert abs(result_mean.item() - 0.0_6_8_4      ) < 1E-3






       def 			_snake_case						(      self      ):
              # We specify different beta, so that the first alpha is 0.99
              __UpperCAmelCase  :				List[Any]       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
              __UpperCAmelCase  :				List[str]       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				int       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_4_9.8_2_9_5      ) < 1E-2
              assert abs(result_mean.item() - 0.1_9_5_1      ) < 1E-3







       def 			_snake_case						(      self      ):
              # We specify different beta, so that the first alpha is 0.99
              __UpperCAmelCase  :				Optional[int]       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
              __UpperCAmelCase  :				Tuple       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Dict       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_4_9.0_7_8_4      ) < 1E-2
              assert abs(result_mean.item() - 0.1_9_4_1      ) < 1E-3







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		(boundary[1] - boundary[0]) / steps
       __UpperCAmelCase  :				Tuple       				=		boundary[0]
       __UpperCAmelCase  :				List[str]       				=		boundary[1]
       __UpperCAmelCase  :				List[Any]       				=		make_points(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)
       __UpperCAmelCase  :				int       				=		0.0
       y += (h / 2.0) * f(lowerCamelCase__						)
       for i in x_i:
              # print(i)
              y += h * f(lowerCamelCase__						)
       y += (h / 2.0) * f(lowerCamelCase__						)
       return y

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		a + h
       while x < (b - h):
              yield x
              __UpperCAmelCase  :				List[str]       				=		x + h

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:  # enter your function here





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		(x - 0) * (x - 0)
       return y

def _lowercase	(      )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		0.0  # Lower bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		1.0  # Upper bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		10.0  # define number of steps or resolution
       __UpperCAmelCase  :				Dict       				=		[a, b]  # define boundary of integration
       __UpperCAmelCase  :				Optional[int]       				=		method_a(lowerCamelCase__							,    lowerCamelCase__						)
       print(f"""y = {y}"""						)


if __name__ == "__main__":
   main()






