



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	(UnCLIPScheduler,)






     def    lowercase_   (       self  :  Optional[Any], **_snake_case  :  Dict					)   ->Optional[Any]:
            snake_case__							:	Any   	=	{
                'num_train_timesteps': 1_0_0_0,
                'variance_type': 'fixed_small_log',
                'clip_sample': True,
                'clip_sample_range': 1.0,
                'prediction_type': 'epsilon',
            }

            config.update(**__lowerCAmelCase					)
            return config






     def    lowercase_   (       self  :  List[Any]					)   ->Tuple:







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






     def    lowercase_   (       self  :  List[str]					)   ->Any:







            for variance in ["fixed_small_log", "learned_range"]:
                   self.check_over_configs(variance_type=__lowerCAmelCase					)






     def    lowercase_   (       self  :  Tuple					)   ->Dict:







            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=__lowerCAmelCase					)






     def    lowercase_   (       self  :  Dict					)   ->Optional[Any]:







            for clip_sample_range in [1, 5, 1_0, 2_0]:
                   self.check_over_configs(clip_sample_range=__lowerCAmelCase					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->Optional[Any]:







            for prediction_type in ["epsilon", "sample"]:
                   self.check_over_configs(prediction_type=__lowerCAmelCase					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->List[str]:







            for time_step in [0, 5_0_0, 9_9_9]:
                   for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                          if prev_timestep is not None and prev_timestep >= time_step:
                                 continue

                          self.check_over_forward(time_step=__lowerCAmelCase, prev_timestep=__lowerCAmelCase					)






     def    lowercase_   (       self  :  int					)   ->Optional[int]:
            snake_case__							:	Any   	=	self.scheduler_classes[0]
            snake_case__							:	Optional[int]   	=	self.get_scheduler_config(variance_type='fixed_small_log'					)
            snake_case__							:	List[Any]   	=	scheduler_class(**__lowerCAmelCase					)

            assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0000e-10					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_8_7					) - 0.0_5_4_9_6_2_5					)					) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_9_9					) - 0.9_9_9_4_9_8_7					)					) < 1e-5






     def    lowercase_   (       self  :  Any					)   ->Dict:
            snake_case__							:	str   	=	self.scheduler_classes[0]
            snake_case__							:	Any   	=	self.get_scheduler_config(variance_type='learned_range'					)
            snake_case__							:	List[Any]   	=	scheduler_class(**__lowerCAmelCase					)

            snake_case__							:	Any   	=	0.5

            assert scheduler._get_variance(1, predicted_variance=__lowerCAmelCase					) - -1_0.1_7_1_2_7_9_0 < 1e-5
            assert scheduler._get_variance(4_8_7, predicted_variance=__lowerCAmelCase					) - -5.7_9_9_8_0_5_2 < 1e-5
            assert scheduler._get_variance(9_9_9, predicted_variance=__lowerCAmelCase					) - -0.0_0_1_0_0_1_1 < 1e-5






     def    lowercase_   (       self  :  Any					)   ->Tuple:
            snake_case__							:	Tuple   	=	self.scheduler_classes[0]
            snake_case__							:	List[Any]   	=	self.get_scheduler_config()
            snake_case__							:	str   	=	scheduler_class(**__lowerCAmelCase					)

            snake_case__							:	List[Any]   	=	scheduler.timesteps

            snake_case__							:	List[Any]   	=	self.dummy_model()
            snake_case__							:	str   	=	self.dummy_sample_deter
            snake_case__							:	Optional[int]   	=	torch.manual_seed(0					)

            for i, t in enumerate(__lowerCAmelCase					):
                   # 1. predict noise residual
                   snake_case__							:	List[str]   	=	model(__lowerCAmelCase, __lowerCAmelCase					)

                   # 2. predict previous mean of sample x_t-1
                   snake_case__							:	List[str]   	=	scheduler.step(__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, generator=__lowerCAmelCase					).prev_sample

                   snake_case__							:	Tuple   	=	pred_prev_sample

            snake_case__							:	Optional[int]   	=	torch.sum(torch.abs(__lowerCAmelCase					)					)
            snake_case__							:	Optional[int]   	=	torch.mean(torch.abs(__lowerCAmelCase					)					)

            assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5					) < 1e-2
            assert abs(result_mean.item() - 0.3_2_8_4_7_4_3					) < 1e-3






     def    lowercase_   (       self  :  Optional[Any]					)   ->Optional[Any]:
            snake_case__							:	List[str]   	=	self.scheduler_classes[0]
            snake_case__							:	List[str]   	=	self.get_scheduler_config()
            snake_case__							:	Tuple   	=	scheduler_class(**__lowerCAmelCase					)

            scheduler.set_timesteps(2_5					)

            snake_case__							:	List[str]   	=	scheduler.timesteps

            snake_case__							:	List[str]   	=	self.dummy_model()
            snake_case__							:	str   	=	self.dummy_sample_deter
            snake_case__							:	Any   	=	torch.manual_seed(0					)

            for i, t in enumerate(__lowerCAmelCase					):
                   # 1. predict noise residual
                   snake_case__							:	Optional[int]   	=	model(__lowerCAmelCase, __lowerCAmelCase					)

                   if i + 1 == timesteps.shape[0]:
                          snake_case__							:	Union[str, Any]   	=	None
                   else:
                          snake_case__							:	str   	=	timesteps[i + 1]

                   # 2. predict previous mean of sample x_t-1
                   snake_case__							:	List[str]   	=	scheduler.step(
                       __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, prev_timestep=__lowerCAmelCase, generator=__lowerCAmelCase					).prev_sample

                   snake_case__							:	Optional[Any]   	=	pred_prev_sample

            snake_case__							:	Dict   	=	torch.sum(torch.abs(__lowerCAmelCase					)					)
            snake_case__							:	List[Any]   	=	torch.mean(torch.abs(__lowerCAmelCase					)					)

            assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3					) < 1e-2
            assert abs(result_mean.item() - 0.3_3_6_2_0_3_8					) < 1e-3






     def    lowercase_   (       self  :  List[str]					)   ->str:
            pass






     def    lowercase_   (       self  :  Any					)   ->List[Any]:
            pass













def lowerCAmelCase__(__snake_case							)				->      list:







	'''simple docstring'''







	lowerCamelCase__								=		len(__snake_case							)
	for _ in range(__snake_case							):
		for i in range(_ % 2       ,arr_size - 1       ,2							):
			if arr[i + 1] < arr[i]:
				lowerCamelCase__				,	lowerCamelCase__								=		arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
			_a 						=      list(range(10, 0, -1))
			print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")



