
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class  __magic_name__						(  _UpperCamelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					(CMStochasticIterativeScheduler,)
				SCREAMING_SNAKE_CASE__					:							Any	      =					10


				def     _A							(							self:   Tuple		,					**_lowerCamelCase:   Any		):
											SCREAMING_SNAKE_CASE_   	=    {
											    "num_train_timesteps": 2_01,
											    "sigma_min": 0.0_02,
											    "sigma_max": 80.0,
											}

											config.update(**_lowerCamelCase		)
											return config


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    10

											SCREAMING_SNAKE_CASE_   	=    self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_   	=    self.scheduler_classes[0](**_lowerCamelCase		)

											scheduler.set_timesteps(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    scheduler.timesteps[0]
											SCREAMING_SNAKE_CASE_   	=    scheduler.timesteps[1]

											SCREAMING_SNAKE_CASE_   	=    self.dummy_sample
											SCREAMING_SNAKE_CASE_   	=    0.1 * sample

											SCREAMING_SNAKE_CASE_   	=    scheduler.step(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		).prev_sample
											SCREAMING_SNAKE_CASE_   	=    scheduler.step(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		).prev_sample

											self.assertEqual(output_a.shape		,					sample.shape		)
											self.assertEqual(output_a.shape		,					output_a.shape		)


				def     _A							(							self:   Dict		):
											for timesteps in [10, 50, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=_lowerCamelCase		)


				def     _A							(							self:   str		):
											for clip_denoised in [True, False]:
																		self.check_over_configs(clip_denoised=_lowerCamelCase		)


				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_   	=    self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_   	=    scheduler_class(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    1
											scheduler.set_timesteps(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    scheduler.timesteps

											SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(0		)

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

											for i, t in enumerate(_lowerCamelCase		):
																		# 1. scale model input
																		SCREAMING_SNAKE_CASE_   	=    scheduler.scale_model_input(_lowerCamelCase		,					_lowerCamelCase		)

																		# 2. predict noise residual
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					_lowerCamelCase		)

																		# 3. predict previous sample x_t-1
																		SCREAMING_SNAKE_CASE_   	=    scheduler.step(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					generator=_lowerCamelCase		).prev_sample

																		SCREAMING_SNAKE_CASE_   	=    pred_prev_sample

											SCREAMING_SNAKE_CASE_   	=    torch.sum(torch.abs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    torch.mean(torch.abs(_lowerCamelCase		)		)

											assert abs(result_sum.item() - 1_92.76_14		) < 1E-2
											assert abs(result_mean.item() - 0.25_10		) < 1E-3


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_   	=    self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_   	=    scheduler_class(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    [1_06, 0]
											scheduler.set_timesteps(timesteps=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    scheduler.timesteps

											SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(0		)

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

											for t in timesteps:
																		# 1. scale model input
																		SCREAMING_SNAKE_CASE_   	=    scheduler.scale_model_input(_lowerCamelCase		,					_lowerCamelCase		)

																		# 2. predict noise residual
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					_lowerCamelCase		)

																		# 3. predict previous sample x_t-1
																		SCREAMING_SNAKE_CASE_   	=    scheduler.step(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					generator=_lowerCamelCase		).prev_sample

																		SCREAMING_SNAKE_CASE_   	=    pred_prev_sample

											SCREAMING_SNAKE_CASE_   	=    torch.sum(torch.abs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    torch.mean(torch.abs(_lowerCamelCase		)		)

											assert abs(result_sum.item() - 3_47.63_57		) < 1E-2
											assert abs(result_mean.item() - 0.45_27		) < 1E-3


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_   	=    self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_   	=    scheduler_class(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    [39, 30, 12, 15, 0]

											with self.assertRaises(_lowerCamelCase		,					msg='''`timesteps` must be in descending order.'''		):
																		scheduler.set_timesteps(timesteps=_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_   	=    self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_   	=    scheduler_class(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    [39, 30, 12, 1, 0]
											SCREAMING_SNAKE_CASE_   	=    len(_lowerCamelCase		)

											with self.assertRaises(_lowerCamelCase		,					msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''		):
																		scheduler.set_timesteps(num_inference_steps=_lowerCamelCase		,					timesteps=_lowerCamelCase		)





				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE_   	=    self.get_scheduler_config()
											SCREAMING_SNAKE_CASE_   	=    scheduler_class(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    [scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    _lowerCamelCase		,					msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,					):
																		scheduler.set_timesteps(timesteps=_lowerCamelCase		)








from typing import Any

def 			a   (_lowerCAmelCase     ):
							if not input_list:
														return []
							SCREAMING_SNAKE_CASE_   	=    [input_list.count(_lowerCAmelCase     ) for value in input_list]
							SCREAMING_SNAKE_CASE_   	=    max(_lowerCAmelCase     )  # Gets the maximum count in the input list.
							# Gets values of modes
							return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase     ) if value == y}     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


