






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(EulerDiscreteScheduler,)
				lowercase__											=			10




				def 							lowercase__  (	self		:       Optional[Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {
								    '''num_train_timesteps''': 1_100,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Union[str, Any]    ):

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




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001],    [0.0002, 0.002, 0.02]    ):
												self.check_over_configs(beta_start=lowerCamelCase,    beta_end=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[Any]    ):

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




				def 							lowercase__  (	self		:       str    ):

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




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config()
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowercase__  					=     torch.manual_seed(0    )

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

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

												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )

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

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

								assert abs(result_sum.item() - 10.0807    ) < 1E-2
								assert abs(result_mean.item() - 0.0131    ) < 1E-3




				def 							lowercase__  (	self		:       str    ):

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

								scheduler.set_timesteps(self.num_inference_steps    )

								lowercase__  					=     torch.manual_seed(0    )

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

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

												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )

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

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

								assert abs(result_sum.item() - 0.0002    ) < 1E-2
								assert abs(result_mean.item() - 2.2676E-06    ) < 1E-3




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config()
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

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

								lowercase__  					=     torch.manual_seed(0    )

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

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

												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )

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

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

								assert abs(result_sum.item() - 10.0807    ) < 1E-2
								assert abs(result_mean.item() - 0.0131    ) < 1E-3




				def 							lowercase__  (	self		:       Tuple    ):

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

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

								lowercase__  					=     torch.manual_seed(0    )

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

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

												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )

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

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

								assert abs(result_sum.item() - 124.52299499511719    ) < 1E-2
								assert abs(result_mean.item() - 0.16213932633399963    ) < 1E-3












from __future__ import annotations

from typing import Any






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if not postfix_notation:
								return 0

				lowercase__  					=     {'''+''', '''-''', '''*''', '''/'''}
				lowercase__  					=     []

				for token in postfix_notation:
								if token in operations:
												lowercase__					,		lowercase__  					=     stack.pop(), stack.pop()
												if token == "+":
																stack.append(a + b							)
												elif token == "-":
																stack.append(a - b							)
												elif token == "*":
																stack.append(a * b							)
												else:
																if a * b < 0 and a % b != 0:
																				stack.append(a // b + 1							)
																else:
																				stack.append(a // b							)
								else:
												stack.append(int(lowerCamelCase_							)							)

				return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




