





"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			(DDPMScheduler,)


						def     snake_case_   (self   ,							**__a		) ->   str:
								UpperCamelCase   =	{
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.0001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "variance_type": "fixed_small",
								    "clip_sample": True,
								}

								config.update(**__a		)
								return config


						def     snake_case_   (self		) ->   int:
								for timesteps in [1, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__a		)


						def     snake_case_   (self		) ->   List[Any]:
								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,							[0.002, 0.02, 0.2, 2]		):
										self.check_over_configs(beta_start=__a   ,							beta_end=__a		)


						def     snake_case_   (self		) ->   int:
								for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=__a		)


						def     snake_case_   (self		) ->   Any:
								for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=__a		)


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


						def     snake_case_   (self		) ->   Tuple:
								self.check_over_configs(thresholding=__a		)
								for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(
												    thresholding=__a   ,							prediction_type=__a   ,							sample_max_value=__a   ,							)


						def     snake_case_   (self		) ->   List[str]:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=__a		)


						def     snake_case_   (self		) ->   str:
								for t in [0, 5_00, 9_99]:
										self.check_over_forward(time_step=__a		)


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	self.scheduler_classes[0]
								UpperCamelCase   =	self.get_scheduler_config()
								UpperCamelCase   =	scheduler_class(**__a		)

								assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00979		)		) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1e-5


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	self.scheduler_classes[0]
								UpperCamelCase   =	self.get_scheduler_config()
								UpperCamelCase   =	scheduler_class(**__a		)

								UpperCamelCase   =	len(__a		)

								UpperCamelCase   =	self.dummy_model()
								UpperCamelCase   =	self.dummy_sample_deter
								UpperCamelCase   =	torch.manual_seed(0		)

								for t in reversed(range(__a		)		):
										# 1. predict noise residual
										UpperCamelCase   =	model(__a   ,							__a		)

										# 2. predict previous mean of sample x_t-1
										UpperCamelCase   =	scheduler.step(__a   ,							__a   ,							__a   ,							generator=__a		).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										UpperCamelCase   =	pred_prev_sample

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

								assert abs(result_sum.item() - 258.9606		) < 1e-2
								assert abs(result_mean.item() - 0.3372		) < 1e-3


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	self.scheduler_classes[0]
								UpperCamelCase   =	self.get_scheduler_config(prediction_type="v_prediction"		)
								UpperCamelCase   =	scheduler_class(**__a		)

								UpperCamelCase   =	len(__a		)

								UpperCamelCase   =	self.dummy_model()
								UpperCamelCase   =	self.dummy_sample_deter
								UpperCamelCase   =	torch.manual_seed(0		)

								for t in reversed(range(__a		)		):
										# 1. predict noise residual
										UpperCamelCase   =	model(__a   ,							__a		)

										# 2. predict previous mean of sample x_t-1
										UpperCamelCase   =	scheduler.step(__a   ,							__a   ,							__a   ,							generator=__a		).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										UpperCamelCase   =	pred_prev_sample

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

								assert abs(result_sum.item() - 202.0296		) < 1e-2
								assert abs(result_mean.item() - 0.2631		) < 1e-3


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	self.scheduler_classes[0]
								UpperCamelCase   =	self.get_scheduler_config()
								UpperCamelCase   =	scheduler_class(**__a		)

								UpperCamelCase   =	[1_00, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=__a		)

								UpperCamelCase   =	scheduler.timesteps

								for i, timestep in enumerate(__a		):
										if i == len(__a		) - 1:
												UpperCamelCase   =	-1
										else:
												UpperCamelCase   =	timesteps[i + 1]

										UpperCamelCase   =	scheduler.previous_timestep(__a		)
										UpperCamelCase   =	prev_t.item()

										self.assertEqual(__a   ,							__a		)


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	self.scheduler_classes[0]
								UpperCamelCase   =	self.get_scheduler_config()
								UpperCamelCase   =	scheduler_class(**__a		)

								UpperCamelCase   =	[1_00, 87, 50, 51, 0]

								with self.assertRaises(__a   ,							msg="`custom_timesteps` must be in descending order."		):
										scheduler.set_timesteps(timesteps=__a		)


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	self.scheduler_classes[0]
								UpperCamelCase   =	self.get_scheduler_config()
								UpperCamelCase   =	scheduler_class(**__a		)

								UpperCamelCase   =	[1_00, 87, 50, 1, 0]
								UpperCamelCase   =	len(__a		)

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




						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.scheduler_classes[0]
								UpperCamelCase   =	self.get_scheduler_config()
								UpperCamelCase   =	scheduler_class(**__a		)

								UpperCamelCase   =	[scheduler.config.num_train_timesteps]

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













"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	0
		for ch in input_str:
				UpperCamelCase   =	ord(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	pow(2  ,	_SCREAMING_SNAKE_CASE    )

				# If we already turned on bit for current character's unicode
				if bitmap >> ch_unicode & 1 == 1:
						return False
				bitmap |= ch_bit_index_on
		return True


if __name__ == "__main__":
						import doctest

						doctest.testmod()






