import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 						a__							(   __snake_case		):
			A__				:   Union[str, Any]								=			(DDIMScheduler,)
			A__				:   Any								=			(('eta', 0.0), ('num_inference_steps', 50))



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    int:
								__a							=   {
								    'num_train_timesteps': 1_0_0_0,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'clip_sample': True,
								}

								config.update(**UpperCAmelCase    )
								return config



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config(**UpperCAmelCase    )
								__a							=   scheduler_class(**UpperCAmelCase    )

								__a					,      __a							=   1_0, 0.0

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter

								scheduler.set_timesteps(UpperCAmelCase    )

								for t in scheduler.timesteps:
													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ).prev_sample

								return sample



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=UpperCAmelCase    )

								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config(steps_offset=1    )
								__a							=   scheduler_class(**UpperCAmelCase    )
								scheduler.set_timesteps(5    )
								assert torch.equal(scheduler.timesteps      ,						torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]    )    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								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=UpperCAmelCase      ,						beta_end=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								self.check_over_configs(thresholding=UpperCAmelCase    )
								for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=UpperCAmelCase      ,						prediction_type=UpperCAmelCase      ,						sample_max_value=UpperCAmelCase      ,						)



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								for t in [1, 1_0, 4_9]:
													self.check_over_forward(time_step=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								for t, num_inference_steps in zip([1, 1_0, 5_0]      ,						[1_0, 5_0, 5_0_0]    ):
													self.check_over_forward(time_step=UpperCAmelCase      ,						num_inference_steps=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								for t, eta in zip([1, 1_0, 4_9]      ,						[0.0, 0.5, 1.0]    ):
													self.check_over_forward(time_step=UpperCAmelCase      ,						eta=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase    )

								assert torch.sum(torch.abs(scheduler._get_variance(0      ,						0    ) - 0.0    )    ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      ,						4_0_0    ) - 0.14_771    )    ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      ,						9_6_0    ) - 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(4_8_7      ,						4_8_6    ) - 0.00_979    )    ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ,						9_9_8    ) - 0.02    )    ) < 1e-5



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a							=   self.full_loop()

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

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



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a							=   self.full_loop(prediction_type='v_prediction'    )

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

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



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								# We specify different beta, so that the first alpha is 0.99
								__a							=   self.full_loop(set_alpha_to_one=UpperCAmelCase      ,						beta_start=0.01    )
								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

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







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								# We specify different beta, so that the first alpha is 0.99
								__a							=   self.full_loop(set_alpha_to_one=UpperCAmelCase      ,						beta_start=0.01    )
								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert abs(result_sum.item() - 149.0_784    ) < 1e-2
								assert abs(result_mean.item() - 0.1_941    ) < 1e-3





from dataclasses import dataclass
from typing import Callable, Optional

import datasets


@dataclass
class 						a__							(   datasets.BuilderConfig		):
			A__				:   Optional[Callable]								=			None
			A__				:   Optional[dict]								=			None
			A__				:   Optional[datasets.Features]								=			None



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								assert self.generator is not None, "generator must be specified"

								if self.gen_kwargs is None:
													__a							=   {}






class 						a__							(   datasets.GeneratorBasedBuilder		):
			A__				:   List[Any]								=			GeneratorConfig



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return datasets.DatasetInfo(features=self.config.features    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,						gen_kwargs=self.config.gen_kwargs    )]







			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								for idx, ex in enumerate(self.config.generator(**UpperCAmelCase    )    ):
													yield idx, ex




