






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(DDPMScheduler,)




				def 							lowercase__  (	self		:       List[str],    **lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any    ):

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




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],    [0.002, 0.02, 0.2, 2]    ):
												self.check_over_configs(beta_start=lowerCamelCase,    beta_end=lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

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




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

								'''simple docstring'''
								self.check_over_configs(thresholding=lowerCamelCase    )
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase,    prediction_type=lowerCamelCase,    sample_max_value=lowerCamelCase,    )




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

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




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								for t in [0, 500, 999]:
												self.check_over_forward(time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       int    ):

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

								assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.00979    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1E-5




				def 							lowercase__  (	self		:       str    ):

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

								lowercase__  					=     len(lowerCamelCase    )

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

								for t in reversed(range(lowerCamelCase    )    ):
												# 1. predict noise residual
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )

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

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												lowercase__  					=     pred_prev_sample

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

								assert abs(result_sum.item() - 258.9606    ) < 1E-2
								assert abs(result_mean.item() - 0.3372    ) < 1E-3




				def 							lowercase__  (	self		:       Optional[int]    ):

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

								lowercase__  					=     len(lowerCamelCase    )

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

								for t in reversed(range(lowerCamelCase    )    ):
												# 1. predict noise residual
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )

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

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												lowercase__  					=     pred_prev_sample

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

								assert abs(result_sum.item() - 202.0296    ) < 1E-2
								assert abs(result_mean.item() - 0.2631    ) < 1E-3




				def 							lowercase__  (	self		:       str    ):

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

								lowercase__  					=     [100, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=lowerCamelCase    )

								lowercase__  					=     scheduler.timesteps

								for i, timestep in enumerate(lowerCamelCase    ):
												if i == len(lowerCamelCase    ) - 1:
																lowercase__  					=     -1
												else:
																lowercase__  					=     timesteps[i + 1]

												lowercase__  					=     scheduler.previous_timestep(lowerCamelCase    )
												lowercase__  					=     prev_t.item()

												self.assertEqual(lowerCamelCase,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Dict    ):

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

								lowercase__  					=     [100, 87, 50, 51, 0]

								with self.assertRaises(lowerCamelCase,    msg='''`custom_timesteps` must be in descending order.'''    ):
												scheduler.set_timesteps(timesteps=lowerCamelCase    )




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

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

								lowercase__  					=     [100, 87, 50, 1, 0]
								lowercase__  					=     len(lowerCamelCase    )

								with self.assertRaises(lowerCamelCase,    msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''    ):
												scheduler.set_timesteps(num_inference_steps=lowerCamelCase,    timesteps=lowerCamelCase    )




				def 							lowercase__  (	self		:       int    ):

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

								lowercase__  					=     [scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    lowerCamelCase,    msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''',    ):
												scheduler.set_timesteps(timesteps=lowerCamelCase    )












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()




