






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 					__lowercase					(		_UpperCAmelCase     ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE        = (LMSDiscreteScheduler,)
     SCREAMING_SNAKE_CASE        = 10




     def 						lowerCAmelCase_   ( self				:	int       ,		**UpperCamelCase_				:	List[Any] ):


         """simple docstring"""



         __A   			=		{
             "num_train_timesteps": 1_100,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**lowercase__ )
         return config




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


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Tuple ):


         """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=lowercase__       ,		beta_end=lowercase__ )




     def 						lowerCAmelCase_   ( self				:	int ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Dict ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	List[str] ):


         """simple docstring"""



         for t in [0, 500, 800]:
             self.check_over_forward(time_step=lowercase__ )




     def 						lowerCAmelCase_   ( self				:	int ):


         """simple docstring"""



         __A   			=		self.scheduler_classes[0]
         __A   			=		self.get_scheduler_config()
         __A   			=		scheduler_class(**lowercase__ )

         scheduler.set_timesteps(self.num_inference_steps )

         __A   			=		self.dummy_model()
         __A   			=		self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps ):
             __A   			=		scheduler.scale_model_input(lowercase__       ,		lowercase__ )

             __A   			=		model(lowercase__       ,		lowercase__ )

             __A   			=		scheduler.step(lowercase__       ,		lowercase__       ,		lowercase__ )
             __A   			=		output.prev_sample

         __A   			=		torch.sum(torch.abs(lowercase__ ) )
         __A   			=		torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1006.388 ) < 1e-2
         assert abs(result_mean.item() - 1.31 ) < 1e-3




     def 						lowerCAmelCase_   ( self				:	Tuple ):


         """simple docstring"""



         __A   			=		self.scheduler_classes[0]
         __A   			=		self.get_scheduler_config(prediction_type="""v_prediction""" )
         __A   			=		scheduler_class(**lowercase__ )

         scheduler.set_timesteps(self.num_inference_steps )

         __A   			=		self.dummy_model()
         __A   			=		self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps ):
             __A   			=		scheduler.scale_model_input(lowercase__       ,		lowercase__ )

             __A   			=		model(lowercase__       ,		lowercase__ )

             __A   			=		scheduler.step(lowercase__       ,		lowercase__       ,		lowercase__ )
             __A   			=		output.prev_sample

         __A   			=		torch.sum(torch.abs(lowercase__ ) )
         __A   			=		torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 0.0017 ) < 1e-2
         assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3




     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



         __A   			=		self.scheduler_classes[0]
         __A   			=		self.get_scheduler_config()
         __A   			=		scheduler_class(**lowercase__ )

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

         __A   			=		self.dummy_model()
         __A   			=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A   			=		sample.to(lowercase__ )

         for i, t in enumerate(scheduler.timesteps ):
             __A   			=		scheduler.scale_model_input(lowercase__       ,		lowercase__ )

             __A   			=		model(lowercase__       ,		lowercase__ )

             __A   			=		scheduler.step(lowercase__       ,		lowercase__       ,		lowercase__ )
             __A   			=		output.prev_sample

         __A   			=		torch.sum(torch.abs(lowercase__ ) )
         __A   			=		torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1006.388 ) < 1e-2
         assert abs(result_mean.item() - 1.31 ) < 1e-3





     def 						lowerCAmelCase_   ( self				:	Optional[Any] ):


         """simple docstring"""



         __A   			=		self.scheduler_classes[0]
         __A   			=		self.get_scheduler_config()
         __A   			=		scheduler_class(**lowercase__       ,		use_karras_sigmas=lowercase__ )

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

         __A   			=		self.dummy_model()
         __A   			=		self.dummy_sample_deter.to(lowercase__ ) * scheduler.init_noise_sigma
         __A   			=		sample.to(lowercase__ )

         for t in scheduler.timesteps:
             __A   			=		scheduler.scale_model_input(lowercase__       ,		lowercase__ )

             __A   			=		model(lowercase__       ,		lowercase__ )

             __A   			=		scheduler.step(lowercase__       ,		lowercase__       ,		lowercase__ )
             __A   			=		output.prev_sample

         __A   			=		torch.sum(torch.abs(lowercase__ ) )
         __A   			=		torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 3812.9927 ) < 2e-2
         assert abs(result_mean.item() - 4.9648 ) < 1e-3







'''simple docstring'''






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							(EulerAncestralDiscreteScheduler,)
    _A			    =							10




    def   __lowerCamelCase      (					self				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {
              "num_train_timesteps": 1100,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**lowercase__			)
          return config




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """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=lowercase__				,     beta_end=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			int     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[str]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Tuple     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.get_scheduler_config(prediction_type="v_prediction"			)
          SCREAMING_SNAKE_CASE_      :			str     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_sample_deter * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			List[str]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[Any]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 108.4439			) < 1e-2
          assert abs(result_mean.item() - 0.1412			) < 1e-3







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps				,     device=lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sample.to(lowercase__			)

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Dict     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[str]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			Any     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3

