




'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		(UnCLIPScheduler,)


				def   A_ (		self    :					Union[str, Any]       ,				**lowercase_    :					Union[str, Any]					):
									snake_case_									=				{
									    '''num_train_timesteps''': 1000,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**lowercase_					)
									return config


				def   A_ (		self    :					str					):
									for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=lowercase_					)


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


				def   A_ (		self    :					List[str]					):
									for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=lowercase_					)


				def   A_ (		self    :					Optional[int]					):
									for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=lowercase_					)


				def   A_ (		self    :					Any					):
									for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																			if prev_timestep is not None and prev_timestep >= time_step:
																								continue

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


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(variance_type='''fixed_small_log'''					)
									snake_case_									=				scheduler_class(**lowercase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000e-10					)					) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.054_9625					)					) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.999_4987					)					) < 1e-5


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(variance_type='''learned_range'''					)
									snake_case_									=				scheduler_class(**lowercase_					)

									snake_case_									=				0.5

									assert scheduler._get_variance(1       ,				predicted_variance=lowercase_					) - -10.171_2790 < 1e-5
									assert scheduler._get_variance(487       ,				predicted_variance=lowercase_					) - -5.799_8052 < 1e-5
									assert scheduler._get_variance(999       ,				predicted_variance=lowercase_					) - -0.001_0011 < 1e-5


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config()
									snake_case_									=				scheduler_class(**lowercase_					)

									snake_case_									=				scheduler.timesteps

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

									for i, t in enumerate(lowercase_					):
														# 1. predict noise residual
														snake_case_									=				model(lowercase_       ,				lowercase_					)

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

														snake_case_									=				pred_prev_sample

									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 252.268_2495					) < 1e-2
									assert abs(result_mean.item() - 0.328_4743					) < 1e-3


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config()
									snake_case_									=				scheduler_class(**lowercase_					)

									scheduler.set_timesteps(25					)

									snake_case_									=				scheduler.timesteps

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

									for i, t in enumerate(lowercase_					):
														# 1. predict noise residual
														snake_case_									=				model(lowercase_       ,				lowercase_					)

														if i + 1 == timesteps.shape[0]:
																			snake_case_									=				None
														else:
																			snake_case_									=				timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														snake_case_									=				scheduler.step(
														    lowercase_       ,				lowercase_       ,				lowercase_       ,				prev_timestep=lowercase_       ,				generator=lowercase_					).prev_sample

														snake_case_									=				pred_prev_sample

									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 258.204_4983					) < 1e-2
									assert abs(result_mean.item() - 0.336_2038					) < 1e-3


				def   A_ (		self    :					str					):
									pass


				def   A_ (		self    :					int					):
									pass








'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       int:


     '''simple docstring'''



     if not isinstance(__lowercase			,     __lowercase       ) or number < 0:
          raise ValueError("Input must be a non-negative integer"       )

     _UpperCAmelCase     							=   0
     while number:
          # This way we arrive at next set bit (next 1) instead of looping
          # through each bit and checking for 1s hence the
          # loop won't run 32 times it will only run the number of `1` times
          number &= number - 1
          count += 1
     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()




