


'''simple docstring'''






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class  __magic_name__							(		__lowerCAmelCase    ):
       UpperCAmelCase										=(DDIMParallelScheduler,)
       UpperCAmelCase										=(('''eta''', 0.0), ('''num_inference_steps''', 5_0))






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   ={
              'num_train_timesteps': 1_0_0_0,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'clip_sample': True,
          }

          config.update(**lowerCAmelCase_)
          return config






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       List[Any]							   =self.get_scheduler_config(**lowerCAmelCase_)
          _UpperCAmelCase       :       Union[str, Any]							   =scheduler_class(**lowerCAmelCase_)

          _UpperCAmelCase   ,	_UpperCAmelCase       :       Tuple							   =1_0, 0.0

          _UpperCAmelCase       :       List[str]							   =self.dummy_model()
          _UpperCAmelCase       :       str							   =self.dummy_sample_deter

          scheduler.set_timesteps(lowerCAmelCase_)

          for t in scheduler.timesteps:
             _UpperCAmelCase       :       List[Any]							   =model(lowerCAmelCase_  ,							lowerCAmelCase_)
             _UpperCAmelCase       :       Optional[Any]							   =scheduler.step(lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_).prev_sample

          return sample






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=lowerCAmelCase_)

          _UpperCAmelCase       :       Optional[Any]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       Tuple							   =self.get_scheduler_config(steps_offset=1)
          _UpperCAmelCase       :       Optional[int]							   =scheduler_class(**lowerCAmelCase_)
          scheduler.set_timesteps(5)
          assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]))






       def       lowerCAmelCase		(      self)						->  str:




          '''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=lowerCAmelCase_  ,							beta_end=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=lowerCAmelCase_)






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




          '''simple docstring'''
          for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          self.check_over_configs(thresholding=lowerCAmelCase_)
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=lowerCAmelCase_  ,							prediction_type=lowerCAmelCase_  ,							sample_max_value=lowerCAmelCase_  ,							)






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          for t in [1, 1_0, 4_9]:
             self.check_over_forward(time_step=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          for t, num_inference_steps in zip([1, 1_0, 5_0]  ,							[1_0, 5_0, 5_0_0]):
             self.check_over_forward(time_step=lowerCAmelCase_  ,							num_inference_steps=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          for t, eta in zip([1, 1_0, 4_9]  ,							[0.0, 0.5, 1.0]):
             self.check_over_forward(time_step=lowerCAmelCase_  ,							eta=lowerCAmelCase_)






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       Tuple							   =self.get_scheduler_config()
          _UpperCAmelCase       :       int							   =scheduler_class(**lowerCAmelCase_)

          assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0) - 0.0)) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,							4_0_0) - 0.1_47_71)) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,							9_6_0) - 0.3_24_60)) < 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_8_7  ,							4_8_6) - 0.0_09_79)) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,							9_9_8) - 0.02)) < 1E-5






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       Dict							   =self.get_scheduler_config()
          _UpperCAmelCase       :       int							   =scheduler_class(**lowerCAmelCase_)

          _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =1_0, 0.0
          scheduler.set_timesteps(lowerCAmelCase_)

          _UpperCAmelCase       :       Optional[Any]							   =self.dummy_model()
          _UpperCAmelCase       :       Union[str, Any]							   =self.dummy_sample_deter
          _UpperCAmelCase       :       Dict							   =self.dummy_sample_deter + 0.1
          _UpperCAmelCase       :       List[str]							   =self.dummy_sample_deter - 0.1

          _UpperCAmelCase       :       Optional[int]							   =samplea.shape[0]
          _UpperCAmelCase       :       Tuple							   =torch.stack([samplea, samplea, samplea]  ,							dim=0)
          _UpperCAmelCase       :       Union[str, Any]							   =torch.arange(lowerCAmelCase_)[0:3, None].repeat(1  ,							lowerCAmelCase_)

          _UpperCAmelCase       :       Optional[Any]							   =model(samples.flatten(0  ,							1)  ,							timesteps.flatten(0  ,							1))
          _UpperCAmelCase       :       Dict							   =scheduler.batch_step_no_noise(lowerCAmelCase_  ,							timesteps.flatten(0  ,							1)  ,							samples.flatten(0  ,							1)  ,							lowerCAmelCase_)

          _UpperCAmelCase       :       Optional[int]							   =torch.sum(torch.abs(lowerCAmelCase_))
          _UpperCAmelCase       :       str							   =torch.mean(torch.abs(lowerCAmelCase_))

          assert abs(result_sum.item() - 11_47.79_04) < 1E-2
          assert abs(result_mean.item() - 0.49_82) < 1E-3






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =self.full_loop()

          _UpperCAmelCase       :       str							   =torch.sum(torch.abs(lowerCAmelCase_))
          _UpperCAmelCase       :       Optional[int]							   =torch.mean(torch.abs(lowerCAmelCase_))

          assert abs(result_sum.item() - 1_72.00_67) < 1E-2
          assert abs(result_mean.item() - 0.22_39_67) < 1E-3






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.full_loop(prediction_type='v_prediction')

          _UpperCAmelCase       :       List[str]							   =torch.sum(torch.abs(lowerCAmelCase_))
          _UpperCAmelCase       :       int							   =torch.mean(torch.abs(lowerCAmelCase_))

          assert abs(result_sum.item() - 52.53_02) < 1E-2
          assert abs(result_mean.item() - 0.06_84) < 1E-3






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          # We specify different beta, so that the first alpha is 0.99
          _UpperCAmelCase       :       List[str]							   =self.full_loop(set_alpha_to_one=lowerCAmelCase_  ,							beta_start=0.01)
          _UpperCAmelCase       :       int							   =torch.sum(torch.abs(lowerCAmelCase_))
          _UpperCAmelCase       :       int							   =torch.mean(torch.abs(lowerCAmelCase_))

          assert abs(result_sum.item() - 1_49.82_95) < 1E-2
          assert abs(result_mean.item() - 0.19_51) < 1E-3






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          # We specify different beta, so that the first alpha is 0.99
          _UpperCAmelCase       :       str							   =self.full_loop(set_alpha_to_one=lowerCAmelCase_  ,							beta_start=0.01)
          _UpperCAmelCase       :       Optional[int]							   =torch.sum(torch.abs(lowerCAmelCase_))
          _UpperCAmelCase       :       Tuple							   =torch.mean(torch.abs(lowerCAmelCase_))

          assert abs(result_sum.item() - 1_49.07_84) < 1E-2
          assert abs(result_mean.item() - 0.19_41) < 1E-3









'''simple docstring'''






from __future__ import annotations





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]							):







   '''simple docstring'''




   if not nums:
      return 0
   _UpperCAmelCase       :       Tuple							   =nums[0]
   _UpperCAmelCase       :       int							   =0
   for num in nums[1:]:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =(
          max_excluding + num,
          max(__lowerCamelCase	,   __lowerCamelCase							),
      )
   return max(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

