





"""simple docstring"""





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   (EulerDiscreteScheduler,)
       __magic_name__	:Any						    =   10






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				{
             'num_train_timesteps': 1_1_0_0,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**__UpperCAmelCase )
         return config






       def 	snake_case		(       self ):



         '''simple docstring'''

         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''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=__UpperCAmelCase       ,					beta_end=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.scheduler_classes[0]
         lowerCAmelCase__     :int											=				self.get_scheduler_config()
         lowerCAmelCase__     :Optional[Any]											=				scheduler_class(**__UpperCAmelCase )

         scheduler.set_timesteps(self.num_inference_steps )

         lowerCAmelCase__     :str											=				torch.manual_seed(0 )

         lowerCAmelCase__     :Tuple											=				self.dummy_model()
         lowerCAmelCase__     :Optional[Any]											=				self.dummy_sample_deter * scheduler.init_noise_sigma
         lowerCAmelCase__     :List[Any]											=				sample.to(__UpperCAmelCase )

         for i, t in enumerate(scheduler.timesteps ):
           lowerCAmelCase__     :Tuple											=				scheduler.scale_model_input(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :Dict											=				model(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :Any											=				scheduler.step(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					generator=__UpperCAmelCase )
           lowerCAmelCase__     :Any											=				output.prev_sample

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

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.scheduler_classes[0]
         lowerCAmelCase__     :Any											=				self.get_scheduler_config(prediction_type='v_prediction' )
         lowerCAmelCase__     :int											=				scheduler_class(**__UpperCAmelCase )

         scheduler.set_timesteps(self.num_inference_steps )

         lowerCAmelCase__     :int											=				torch.manual_seed(0 )

         lowerCAmelCase__     :int											=				self.dummy_model()
         lowerCAmelCase__     :Optional[int]											=				self.dummy_sample_deter * scheduler.init_noise_sigma
         lowerCAmelCase__     :Any											=				sample.to(__UpperCAmelCase )

         for i, t in enumerate(scheduler.timesteps ):
           lowerCAmelCase__     :List[Any]											=				scheduler.scale_model_input(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :List[Any]											=				model(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :List[Any]											=				scheduler.step(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					generator=__UpperCAmelCase )
           lowerCAmelCase__     :Any											=				output.prev_sample

         lowerCAmelCase__     :Tuple											=				torch.sum(torch.abs(__UpperCAmelCase ) )
         lowerCAmelCase__     :Tuple											=				torch.mean(torch.abs(__UpperCAmelCase ) )

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				self.scheduler_classes[0]
         lowerCAmelCase__     :Tuple											=				self.get_scheduler_config()
         lowerCAmelCase__     :Dict											=				scheduler_class(**__UpperCAmelCase )

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

         lowerCAmelCase__     :int											=				torch.manual_seed(0 )

         lowerCAmelCase__     :List[Any]											=				self.dummy_model()
         lowerCAmelCase__     :int											=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         lowerCAmelCase__     :Dict											=				sample.to(__UpperCAmelCase )

         for t in scheduler.timesteps:
           lowerCAmelCase__     :Optional[int]											=				scheduler.scale_model_input(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :List[str]											=				model(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :Optional[int]											=				scheduler.step(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					generator=__UpperCAmelCase )
           lowerCAmelCase__     :List[str]											=				output.prev_sample

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

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self.scheduler_classes[0]
         lowerCAmelCase__     :str											=				self.get_scheduler_config()
         lowerCAmelCase__     :Optional[Any]											=				scheduler_class(**__UpperCAmelCase       ,					use_karras_sigmas=__UpperCAmelCase )

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

         lowerCAmelCase__     :List[Any]											=				torch.manual_seed(0 )

         lowerCAmelCase__     :str											=				self.dummy_model()
         lowerCAmelCase__     :List[Any]											=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         lowerCAmelCase__     :Tuple											=				sample.to(__UpperCAmelCase )

         for t in scheduler.timesteps:
           lowerCAmelCase__     :Any											=				scheduler.scale_model_input(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :int											=				model(__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :int											=				scheduler.step(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					generator=__UpperCAmelCase )
           lowerCAmelCase__     :Dict											=				output.prev_sample

         lowerCAmelCase__     :str											=				torch.sum(torch.abs(__UpperCAmelCase ) )
         lowerCAmelCase__     :str											=				torch.mean(torch.abs(__UpperCAmelCase ) )

         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'''



from itertools import product


def A (__lowerCamelCase :int					,      __lowerCamelCase :int						):
						_lowerCAmelCase    		=					sides_number
						_lowerCAmelCase    		=					max_face_number * dice_number
						_lowerCAmelCase    		=					[0] * (max_total + 1)

						_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					range(__lowerCamelCase					,      max_face_number + 1						)
						for dice_numbers in product(__lowerCamelCase					,      repeat=__lowerCamelCase						):
												_lowerCAmelCase    		=					sum(__lowerCamelCase						)
												totals_frequencies[total] += 1

						return totals_frequencies


def A ():
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=4					,      dice_number=9						)
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=6					,      dice_number=6						)

						_lowerCAmelCase    		=					0
						_lowerCAmelCase    		=					9
						_lowerCAmelCase    		=					4 * 9
						_lowerCAmelCase    		=					6
						for peter_total in range(__lowerCamelCase					,      max_peter_total + 1						):
												peter_wins_count += peter_totals_frequencies[peter_total] * sum(
												    colin_totals_frequencies[min_colin_total:peter_total]						)

						_lowerCAmelCase    		=					(4**9) * (6**6)
						_lowerCAmelCase    		=					peter_wins_count / total_games_number

						_lowerCAmelCase    		=					round(__lowerCamelCase					,      ndigits=7						)

						return rounded_peter_win_probability


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