




"""simple docstring"""




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		(EulerDiscreteScheduler,)
				__UpperCamelCase    						=		10





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      {
							    """num_train_timesteps""": 1_100,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							}

							config.update(**_a				)
							return config





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for timesteps in [10, 50, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]	,				[0.0_002, 0.002, 0.02]				):
										self.check_over_configs(beta_start=_a	,				beta_end=_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=_a				)





				def      _lowerCAmelCase       ( self				):





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





				def      _lowerCAmelCase       ( self				):





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

							scheduler.set_timesteps(self.num_inference_steps				)

							lowerCamelCase       =      torch.manual_seed(0				)

							lowerCamelCase       =      self.dummy_model()
							lowerCamelCase       =      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase       =      sample.to(_a				)

							for i, t in enumerate(scheduler.timesteps				):
										lowerCamelCase       =      scheduler.scale_model_input(_a	,				_a				)

										lowerCamelCase       =      model(_a	,				_a				)

										lowerCamelCase       =      scheduler.step(_a	,				_a	,				_a	,				generator=_a				)
										lowerCamelCase       =      output.prev_sample

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

							assert abs(result_sum.item() - 10.0_807				) < 1e-2
							assert abs(result_mean.item() - 0.0_131				) < 1e-3





				def      _lowerCAmelCase       ( self				):





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

							scheduler.set_timesteps(self.num_inference_steps				)

							lowerCamelCase       =      torch.manual_seed(0				)

							lowerCamelCase       =      self.dummy_model()
							lowerCamelCase       =      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase       =      sample.to(_a				)

							for i, t in enumerate(scheduler.timesteps				):
										lowerCamelCase       =      scheduler.scale_model_input(_a	,				_a				)

										lowerCamelCase       =      model(_a	,				_a				)

										lowerCamelCase       =      scheduler.step(_a	,				_a	,				_a	,				generator=_a				)
										lowerCamelCase       =      output.prev_sample

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

							assert abs(result_sum.item() - 0.0_002				) < 1e-2
							assert abs(result_mean.item() - 2.2_6_7_6e-0_6				) < 1e-3





				def      _lowerCAmelCase       ( self				):





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

							scheduler.set_timesteps(self.num_inference_steps	,				device=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)

							lowerCamelCase       =      self.dummy_model()
							lowerCamelCase       =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							lowerCamelCase       =      sample.to(_a				)

							for t in scheduler.timesteps:
										lowerCamelCase       =      scheduler.scale_model_input(_a	,				_a				)

										lowerCamelCase       =      model(_a	,				_a				)

										lowerCamelCase       =      scheduler.step(_a	,				_a	,				_a	,				generator=_a				)
										lowerCamelCase       =      output.prev_sample

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

							assert abs(result_sum.item() - 10.0_807				) < 1e-2
							assert abs(result_mean.item() - 0.0_131				) < 1e-3





				def      _lowerCAmelCase       ( self				):





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

							scheduler.set_timesteps(self.num_inference_steps	,				device=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)

							lowerCamelCase       =      self.dummy_model()
							lowerCamelCase       =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							lowerCamelCase       =      sample.to(_a				)

							for t in scheduler.timesteps:
										lowerCamelCase       =      scheduler.scale_model_input(_a	,				_a				)

										lowerCamelCase       =      model(_a	,				_a				)

										lowerCamelCase       =      scheduler.step(_a	,				_a	,				_a	,				generator=_a				)
										lowerCamelCase       =      output.prev_sample

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

							assert abs(result_sum.item() - 124.52_299_499_511_719				) < 1e-2
							assert abs(result_mean.item() - 0.16_213_932_633_399_963				) < 1e-3









"""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())



