




'''simple docstring'''


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class     SCREAMING_SNAKE_CASE						(	A__							):
      __magic_name__     :					List[Any]             =				(KDPMaAncestralDiscreteScheduler,)
      __magic_name__     :					List[Any]             =				10






      def        lowercase_	(   self      :     Any	,	**lowercase__      :     List[Any]					):


           '''simple docstring'''

           a_    :       int    	=     {
               """num_train_timesteps""": 1100,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**_lowerCAmelCase					)
           return config






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]	,	[0.0002, 0.002, 0.02]					):
                self.check_over_configs(beta_start=_lowerCAmelCase	,	beta_end=_lowerCAmelCase					)






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           if torch_device == "mps":
                return
           a_    :       str    	=     self.scheduler_classes[0]
           a_    :       str    	=     self.get_scheduler_config()
           a_    :       str    	=     scheduler_class(**_lowerCAmelCase					)

           scheduler.set_timesteps(self.num_inference_steps					)

           a_    :       int    	=     torch.manual_seed(0					)

           a_    :       Any    	=     self.dummy_model()
           a_    :       Dict    	=     self.dummy_sample_deter * scheduler.init_noise_sigma
           a_    :       Tuple    	=     sample.to(_lowerCAmelCase					)

           for i, t in enumerate(scheduler.timesteps					):
                a_    :       Optional[int]    	=     scheduler.scale_model_input(_lowerCAmelCase	,	_lowerCAmelCase					)

                a_    :       Union[str, Any]    	=     model(_lowerCAmelCase	,	_lowerCAmelCase					)

                a_    :       List[str]    	=     scheduler.step(_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	generator=_lowerCAmelCase					)
                a_    :       List[str]    	=     output.prev_sample

           a_    :       Union[str, Any]    	=     torch.sum(torch.abs(_lowerCAmelCase					)					)
           a_    :       Any    	=     torch.mean(torch.abs(_lowerCAmelCase					)					)

           assert abs(result_sum.item() - 1_3849.3877					) < 1e-2
           assert abs(result_mean.item() - 18.0331					) < 5e-3






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           if torch_device == "mps":
                return
           a_    :       Dict    	=     self.scheduler_classes[0]
           a_    :       List[Any]    	=     self.get_scheduler_config(prediction_type="""v_prediction"""					)
           a_    :       List[str]    	=     scheduler_class(**_lowerCAmelCase					)

           scheduler.set_timesteps(self.num_inference_steps					)

           a_    :       Optional[int]    	=     self.dummy_model()
           a_    :       Dict    	=     self.dummy_sample_deter * scheduler.init_noise_sigma
           a_    :       str    	=     sample.to(_lowerCAmelCase					)

           a_    :       Optional[Any]    	=     torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
                a_    :       int    	=     scheduler.scale_model_input(_lowerCAmelCase	,	_lowerCAmelCase					)

                a_    :       List[str]    	=     model(_lowerCAmelCase	,	_lowerCAmelCase					)

                a_    :       List[str]    	=     scheduler.step(_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	generator=_lowerCAmelCase					)
                a_    :       Any    	=     output.prev_sample

           a_    :       List[str]    	=     torch.sum(torch.abs(_lowerCAmelCase					)					)
           a_    :       Union[str, Any]    	=     torch.mean(torch.abs(_lowerCAmelCase					)					)

           assert abs(result_sum.item() - 328.9970					) < 1e-2
           assert abs(result_mean.item() - 0.4284					) < 1e-3






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           if torch_device == "mps":
                return
           a_    :       str    	=     self.scheduler_classes[0]
           a_    :       List[Any]    	=     self.get_scheduler_config()
           a_    :       Tuple    	=     scheduler_class(**_lowerCAmelCase					)

           scheduler.set_timesteps(self.num_inference_steps	,	device=_lowerCAmelCase					)
           a_    :       int    	=     torch.manual_seed(0					)

           a_    :       int    	=     self.dummy_model()
           a_    :       int    	=     self.dummy_sample_deter.to(_lowerCAmelCase					) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                a_    :       Dict    	=     scheduler.scale_model_input(_lowerCAmelCase	,	_lowerCAmelCase					)

                a_    :       List[Any]    	=     model(_lowerCAmelCase	,	_lowerCAmelCase					)

                a_    :       str    	=     scheduler.step(_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	generator=_lowerCAmelCase					)
                a_    :       int    	=     output.prev_sample

           a_    :       Any    	=     torch.sum(torch.abs(_lowerCAmelCase					)					)
           a_    :       Optional[Any]    	=     torch.mean(torch.abs(_lowerCAmelCase					)					)

           assert abs(result_sum.item() - 1_3849.3818					) < 1e-1
           assert abs(result_mean.item() - 18.0331					) < 1e-3






'''simple docstring'''


def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int = 1000							):




     """simple docstring"""


     return sum(i for i in range(UpperCamelCase__							) if i % 3 == 0 or i % 5 == 0							)


if __name__ == "__main__":
 print(f"{solution() = }")
