




'''simple docstring'''


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 			A    (		_a    ):
							lowercase_							=				(LMSDiscreteScheduler,)
							lowercase_							=				10







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			**lowerCAmelCase_       :  Dict					)			->							Dict:


														"""simple docstring"""


														_a	     =				{
														    '''num_train_timesteps''': 11_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														}

														config.update(**lowerCAmelCase_					)
														return config







							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Optional[int]:


														"""simple docstring"""


														for timesteps in [10, 50, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Dict:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]							,			[0.0_0_0_2, 0.0_0_2, 0.0_2]					):
																					self.check_over_configs(beta_start=lowerCAmelCase_							,			beta_end=lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  str					)			->							List[str]:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  int					)			->							Optional[int]:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  List[str]					)			->							Tuple:


														"""simple docstring"""


														for t in [0, 5_00, 8_00]:
																					self.check_over_forward(time_step=lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Dict:


														"""simple docstring"""


														_a	     =				self.scheduler_classes[0]
														_a	     =				self.get_scheduler_config()
														_a	     =				scheduler_class(**lowerCAmelCase_					)

														scheduler.set_timesteps(self.num_inference_steps					)

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

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

																					_a	     =				model(lowerCAmelCase_							,			lowerCAmelCase_					)

																					_a	     =				scheduler.step(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)
																					_a	     =				output.prev_sample

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

														assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
														assert abs(result_mean.item() - 1.3_1					) < 1e-3







							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Tuple:


														"""simple docstring"""


														_a	     =				self.scheduler_classes[0]
														_a	     =				self.get_scheduler_config(prediction_type='''v_prediction'''					)
														_a	     =				scheduler_class(**lowerCAmelCase_					)

														scheduler.set_timesteps(self.num_inference_steps					)

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

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

																					_a	     =				model(lowerCAmelCase_							,			lowerCAmelCase_					)

																					_a	     =				scheduler.step(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)
																					_a	     =				output.prev_sample

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

														assert abs(result_sum.item() - 0.0_0_1_7					) < 1e-2
														assert abs(result_mean.item() - 2.2676e-06					) < 1e-3







							def    __lowerCAmelCase			(  self       :  Any					)			->							List[str]:


														"""simple docstring"""


														_a	     =				self.scheduler_classes[0]
														_a	     =				self.get_scheduler_config()
														_a	     =				scheduler_class(**lowerCAmelCase_					)

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

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

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

																					_a	     =				model(lowerCAmelCase_							,			lowerCAmelCase_					)

																					_a	     =				scheduler.step(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)
																					_a	     =				output.prev_sample

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

														assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
														assert abs(result_mean.item() - 1.3_1					) < 1e-3







							def    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				self.scheduler_classes[0]
														_a	     =				self.get_scheduler_config()
														_a	     =				scheduler_class(**lowerCAmelCase_							,			use_karras_sigmas=lowerCAmelCase_					)

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

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

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

																					_a	     =				model(lowerCAmelCase_							,			lowerCAmelCase_					)

																					_a	     =				scheduler.step(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)
																					_a	     =				output.prev_sample

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

														assert abs(result_sum.item() - 3_8_1_2.9_9_2_7					) < 2e-2
														assert abs(result_mean.item() - 4.9_6_4_8					) < 1e-3







'''simple docstring'''


from __future__ import annotations
def 		snake_case_    (UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				[0] * max_one

							for i in range(0 ,						UpperCamelCase      ):
														_a	     =				i

							for i in range(2 ,						UpperCamelCase      ):
														if totients[i] == i:
																					for j in range(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      ):
																												totients[j] -= totients[j] // i

							return totients
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							return sorted(str(UpperCamelCase      )      ) == sorted(str(UpperCamelCase      )      )
def 		snake_case_    (UpperCamelCase       :  int = 1000_0000      ):





							'''simple docstring'''


							_a	     =				1  # i
							_a	     =				0  # φ(i)
							_a	     =				get_totients(max_n + 1      )

							for i in range(2 ,						max_n + 1      ):
														_a	     =				totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCamelCase ,						UpperCamelCase      ):
																					_a	     =				i
																					_a	     =				t

							return min_numerator


if __name__ == "__main__":
				print(F'''{solution() = }''')

