



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						(EulerDiscreteScheduler,)
       UpperCamelCase__  :    List[str]											=						10



       def 		_lowerCamelCase			(		self							:     Tuple     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_100,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           for timesteps in [10, 50, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           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=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

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

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 10.08_07) < 1E-2
           assert abs(result_mean.item() - 0.01_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config(prediction_type='''v_prediction''')
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

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

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 0.00_02) < 1E-2
           assert abs(result_mean.item() - 2.2676E-06) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

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

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
               __a						       =				scheduler.scale_model_input(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 10.08_07) < 1E-2
           assert abs(result_mean.item() - 0.01_31) < 1E-3





       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE     ,		use_karras_sigmas=__SCREAMING_SNAKE_CASE)

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

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
               __a						       =				scheduler.scale_model_input(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19) < 1E-2
           assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63) < 1E-3







"""simple docstring"""




def lowercase_  (   _snake_case						):
							if a < 0:
														raise ValueError("""Input value must be a positive integer"""						)
							elif isinstance(_snake_case   ,_snake_case						):
														raise TypeError("""Input value must be a 'int' type"""						)
							return bin(_snake_case						).count("""1"""						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




