import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class        lowercase_   (					__snake_case		):
       _lowerCamelCase						=		(DDIMParallelScheduler,)
       _lowerCamelCase						=		(('eta', 0.0), ('num_inference_steps', 50))



       def 	UpperCamelCase	(						self				,    **lowercase_    ):
             _snake_case						:					str    =   {
                 "num_train_timesteps": 1_000,
                 "beta_start": 0.0_001,
                 "beta_end": 0.02,
                 "beta_schedule": "linear",
                 "clip_sample": True,
             }

             config.update(**lowercase_    )
             return config



       def 	UpperCamelCase	(						self				,    **lowercase_    ):
             _snake_case						:					Any    =   self.scheduler_classes[0]
             _snake_case						:					Dict    =   self.get_scheduler_config(**lowercase_    )
             _snake_case						:					Dict    =   scheduler_class(**lowercase_    )

             _snake_case						:					int    =   10, 0.0

             _snake_case						:					Union[str, Any]    =   self.dummy_model()
             _snake_case						:					int    =   self.dummy_sample_deter

             scheduler.set_timesteps(lowercase_    )

             for t in scheduler.timesteps:
                   _snake_case						:					Any    =   model(lowercase_				,    lowercase_    )
                   _snake_case						:					Optional[int]    =   scheduler.step(lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ).prev_sample

             return sample



       def 	UpperCamelCase	(						self    ):
             for timesteps in [100, 500, 1_000]:
                   self.check_over_configs(num_train_timesteps=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             for steps_offset in [0, 1]:
                   self.check_over_configs(steps_offset=lowercase_    )

             _snake_case						:					str    =   self.scheduler_classes[0]
             _snake_case						:					Optional[int]    =   self.get_scheduler_config(steps_offset=1    )
             _snake_case						:					Any    =   scheduler_class(**lowercase_    )
             scheduler.set_timesteps(5    )
             assert torch.equal(scheduler.timesteps				,    torch.LongTensor([801, 601, 401, 201, 1]    )    )



       def 	UpperCamelCase	(						self    ):
             for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,    [0.002, 0.02, 0.2, 2]    ):
                   self.check_over_configs(beta_start=lowercase_				,    beta_end=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             for schedule in ["linear", "squaredcos_cap_v2"]:
                   self.check_over_configs(beta_schedule=lowercase_    )



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



       def 	UpperCamelCase	(						self    ):
             for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             for timestep_spacing in ["trailing", "leading"]:
                   self.check_over_configs(timestep_spacing=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             for rescale_betas_zero_snr in [True, False]:
                   self.check_over_configs(rescale_betas_zero_snr=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             self.check_over_configs(thresholding=lowercase_    )
             for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "v_prediction"]:
                         self.check_over_configs(
                             thresholding=lowercase_				,    prediction_type=lowercase_				,    sample_max_value=lowercase_				,    )



       def 	UpperCamelCase	(						self    ):
             for t in [1, 10, 49]:
                   self.check_over_forward(time_step=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             for t, num_inference_steps in zip([1, 10, 50]				,    [10, 50, 500]    ):
                   self.check_over_forward(time_step=lowercase_				,    num_inference_steps=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             for t, eta in zip([1, 10, 49]				,    [0.0, 0.5, 1.0]    ):
                   self.check_over_forward(time_step=lowercase_				,    eta=lowercase_    )



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Tuple    =   self.scheduler_classes[0]
             _snake_case						:					int    =   self.get_scheduler_config()
             _snake_case						:					str    =   scheduler_class(**lowercase_    )

             assert torch.sum(torch.abs(scheduler._get_variance(0				,    0    ) - 0.0    )    ) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(420				,    400    ) - 0.14_771    )    ) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(980				,    960    ) - 0.32_460    )    ) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(0				,    0    ) - 0.0    )    ) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(487				,    486    ) - 0.00_979    )    ) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(999				,    998    ) - 0.02    )    ) < 1e-5



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					int    =   self.scheduler_classes[0]
             _snake_case						:					Optional[Any]    =   self.get_scheduler_config()
             _snake_case						:					Any    =   scheduler_class(**lowercase_    )

             _snake_case						:					Optional[Any]    =   10, 0.0
             scheduler.set_timesteps(lowercase_    )

             _snake_case						:					Optional[int]    =   self.dummy_model()
             _snake_case						:					Tuple    =   self.dummy_sample_deter
             _snake_case						:					str    =   self.dummy_sample_deter + 0.1
             _snake_case						:					int    =   self.dummy_sample_deter - 0.1

             _snake_case						:					List[Any]    =   samplea.shape[0]
             _snake_case						:					Dict    =   torch.stack([samplea, samplea, samplea]				,    dim=0    )
             _snake_case						:					List[Any]    =   torch.arange(lowercase_    )[0:3, None].repeat(1				,    lowercase_    )

             _snake_case						:					Dict    =   model(samples.flatten(0				,    1    )				,    timesteps.flatten(0				,    1    )    )
             _snake_case						:					int    =   scheduler.batch_step_no_noise(lowercase_				,    timesteps.flatten(0				,    1    )				,    samples.flatten(0				,    1    )				,    lowercase_    )

             _snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
             _snake_case						:					Optional[int]    =   torch.mean(torch.abs(lowercase_    )    )

             assert abs(result_sum.item() - 1_147.7_904    ) < 1e-2
             assert abs(result_mean.item() - 0.4_982    ) < 1e-3



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Optional[Any]    =   self.full_loop()

             _snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
             _snake_case						:					Optional[Any]    =   torch.mean(torch.abs(lowercase_    )    )

             assert abs(result_sum.item() - 172.0_067    ) < 1e-2
             assert abs(result_mean.item() - 0.223_967    ) < 1e-3



       def 	UpperCamelCase	(						self    ):
             _snake_case						:					Optional[Any]    =   self.full_loop(prediction_type="v_prediction"    )

             _snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
             _snake_case						:					int    =   torch.mean(torch.abs(lowercase_    )    )

             assert abs(result_sum.item() - 52.5_302    ) < 1e-2
             assert abs(result_mean.item() - 0.0_684    ) < 1e-3



       def 	UpperCamelCase	(						self    ):
             # We specify different beta, so that the first alpha is 0.99
             _snake_case						:					str    =   self.full_loop(set_alpha_to_one=lowercase_				,    beta_start=0.01    )
             _snake_case						:					List[str]    =   torch.sum(torch.abs(lowercase_    )    )
             _snake_case						:					Tuple    =   torch.mean(torch.abs(lowercase_    )    )

             assert abs(result_sum.item() - 149.8_295    ) < 1e-2
             assert abs(result_mean.item() - 0.1_951    ) < 1e-3



       def 	UpperCamelCase	(						self    ):
             # We specify different beta, so that the first alpha is 0.99
             _snake_case						:					Any    =   self.full_loop(set_alpha_to_one=lowercase_				,    beta_start=0.01    )
             _snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
             _snake_case						:					Optional[int]    =   torch.mean(torch.abs(lowercase_    )    )

             assert abs(result_sum.item() - 149.0_784    ) < 1e-2
             assert abs(result_mean.item() - 0.1_941    ) < 1e-3
from __future__ import annotations
def  snake_case						(__lowercase ,      __lowercase						)	->	float:

						'''simple docstring'''



						_snake_case						:					Any    =   sorted(numsa + numsa						)
						_snake_case    ,_snake_case						:					Any    =   divmod(len(__lowercase						) ,      2						)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[float(x) for x in input('Enter the elements of first array: ').split()]
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			[float(x) for x in input('Enter the elements of second array: ').split()]
				print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')