




"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					__magic_name__							( _UpperCamelCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		(UnCLIPScheduler,)





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      {
							    "num_train_timesteps": 1_000,
							    "variance_type": "fixed_small_log",
							    "clip_sample": True,
							    "clip_sample_range": 1.0,
							    "prediction_type": "epsilon",
							}

							config.update(**__a				)
							return config





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for timesteps in [1, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=__a				)





				def      _lowerCAmelCase       ( self				):





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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=__a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for clip_sample_range in [1, 5, 10, 20]:
										self.check_over_configs(clip_sample_range=__a				)





				def      _lowerCAmelCase       ( self				):





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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							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=__a	,				prev_timestep=__a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.scheduler_classes[0]
							lowerCamelCase       =      self.get_scheduler_config(variance_type="""fixed_small_log"""				)
							lowerCamelCase       =      scheduler_class(**__a				)

							assert torch.sum(torch.abs(scheduler._get_variance(0				) - 1.0_0_0_0e-1_0				)				) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_549_625				)				) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.9_994_987				)				) < 1e-5





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.scheduler_classes[0]
							lowerCamelCase       =      self.get_scheduler_config(variance_type="""learned_range"""				)
							lowerCamelCase       =      scheduler_class(**__a				)

							lowerCamelCase       =      0.5

							assert scheduler._get_variance(1	,				predicted_variance=__a				) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(487	,				predicted_variance=__a				) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(999	,				predicted_variance=__a				) - -0.0_010_011 < 1e-5





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.scheduler_classes[0]
							lowerCamelCase       =      self.get_scheduler_config()
							lowerCamelCase       =      scheduler_class(**__a				)

							lowerCamelCase       =      scheduler.timesteps

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

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

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

										lowerCamelCase       =      pred_prev_sample

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

							assert abs(result_sum.item() - 252.2_682_495				) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743				) < 1e-3





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.scheduler_classes[0]
							lowerCamelCase       =      self.get_scheduler_config()
							lowerCamelCase       =      scheduler_class(**__a				)

							scheduler.set_timesteps(25				)

							lowerCamelCase       =      scheduler.timesteps

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

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

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

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

										lowerCamelCase       =      pred_prev_sample

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

							assert abs(result_sum.item() - 258.2_044_983				) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038				) < 1e-3





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass









"""simple docstring"""




from functools import lru_cache



def 					a__				(       snake_case__       )		->      set:
			lowerCamelCase       =      2
			lowerCamelCase       =      set()
			while i * i <= n:
						if n % i:
									i += 1
						else:
									n //= i
									factors.add(snake_case__       )
			if n > 1:
						factors.add(snake_case__       )
			return factors



@lru_cache
def 					a__				(       snake_case__       )		->      int:
			return len(unique_prime_factors(snake_case__       )       )



def 					a__				(       snake_case__       )		->      bool:
			return len(set(snake_case__       )       ) in (0, 1)



def 					a__				(       snake_case__       )		->      list:
			lowerCamelCase       =      2

			while True:
						# Increment each value of a generated range
						lowerCamelCase       =      [base + i for i in range(snake_case__       )]

						# Run elements through out unique_prime_factors function
						# Append our target number to the end.
						lowerCamelCase       =      [upf_len(snake_case__       ) for x in group]
						checker.append(snake_case__       )

						# If all numbers in the list are equal, return the group variable.
						if equality(snake_case__       ):
									return group

						# Increment our base variable by 1
						base += 1



def 					a__				(       snake_case__ = 4       )		->      int:
			lowerCamelCase       =      run(snake_case__       )
			return results[0] if len(snake_case__       ) else None


if __name__ == "__main__":
					print(solution())



