



'''simple docstring'''




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				(DDPMParallelScheduler,)





	def 				_lowercase(						self	,	**A					)    ->    Optional[Any]:
								UpperCAmelCase  :	List[Any]							  =	{
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """beta_schedule""": """linear""",
								    """variance_type""": """fixed_small""",
								    """clip_sample""": True,
								}

								config.update(**A					)
								return config





	def 				_lowercase(						self					)    ->    Optional[int]:
								for timesteps in [1, 5, 100, 1000]:
															self.check_over_configs(num_train_timesteps=A					)





	def 				_lowercase(						self					)    ->    List[str]:
								for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]	,	[0.0_0_2, 0.0_2, 0.2, 2]					):
															self.check_over_configs(beta_start=A	,	beta_end=A					)





	def 				_lowercase(						self					)    ->    List[str]:
								for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=A					)





	def 				_lowercase(						self					)    ->    Any:
								for variance in ["fixed_small", "fixed_large", "other"]:
															self.check_over_configs(variance_type=A					)





	def 				_lowercase(						self					)    ->    int:
								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=A					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								self.check_over_configs(thresholding=A					)
								for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=A	,	prediction_type=A	,	sample_max_value=A	,	)





	def 				_lowercase(						self					)    ->    Tuple:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(prediction_type=A					)





	def 				_lowercase(						self					)    ->    Any:
								for t in [0, 500, 999]:
															self.check_over_forward(time_step=A					)





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Any							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Union[str, Any]							  =	self.get_scheduler_config()
								UpperCAmelCase  :	List[Any]							  =	scheduler_class(**A					)

								assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_0_9_7_9					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.0_2					)					) < 1e-5





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	str							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Any							  =	self.get_scheduler_config()
								UpperCAmelCase  :	Dict							  =	scheduler_class(**A					)

								UpperCAmelCase  :	List[Any]							  =	len(A					)

								UpperCAmelCase  :	Union[str, Any]							  =	self.dummy_model()
								UpperCAmelCase  :	Tuple							  =	self.dummy_sample_deter
								UpperCAmelCase  :	Optional[Any]							  =	self.dummy_sample_deter + 0.1
								UpperCAmelCase  :	str							  =	self.dummy_sample_deter - 0.1

								UpperCAmelCase  :	int							  =	samplea.shape[0]
								UpperCAmelCase  :	Optional[Any]							  =	torch.stack([samplea, samplea, samplea]	,	dim=0					)
								UpperCAmelCase  :	Union[str, Any]							  =	torch.arange(A					)[0:3, None].repeat(1	,	A					)

								UpperCAmelCase  :	Any							  =	model(samples.flatten(0	,	1					)	,	timesteps.flatten(0	,	1					)					)
								UpperCAmelCase  :	List[str]							  =	scheduler.batch_step_no_noise(A	,	timesteps.flatten(0	,	1					)	,	samples.flatten(0	,	1					)					)

								UpperCAmelCase  :	Optional[Any]							  =	torch.sum(torch.abs(A					)					)
								UpperCAmelCase  :	str							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_sum.item() - 1_1_5_3.1_8_3_3					) < 1e-2
								assert abs(result_mean.item() - 0.5_0_0_5					) < 1e-3





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Any							  =	self.get_scheduler_config()
								UpperCAmelCase  :	Optional[int]							  =	scheduler_class(**A					)

								UpperCAmelCase  :	int							  =	len(A					)

								UpperCAmelCase  :	Optional[Any]							  =	self.dummy_model()
								UpperCAmelCase  :	Optional[int]							  =	self.dummy_sample_deter
								UpperCAmelCase  :	str							  =	torch.manual_seed(0					)

								for t in reversed(range(A					)					):
															# 1. predict noise residual
															UpperCAmelCase  :	Optional[Any]							  =	model(A	,	A					)

															# 2. predict previous mean of sample x_t-1
															UpperCAmelCase  :	Dict							  =	scheduler.step(A	,	A	,	A	,	generator=A					).prev_sample

															UpperCAmelCase  :	Union[str, Any]							  =	pred_prev_sample

								UpperCAmelCase  :	Tuple							  =	torch.sum(torch.abs(A					)					)
								UpperCAmelCase  :	int							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_sum.item() - 2_5_8.9_6_0_6					) < 1e-2
								assert abs(result_mean.item() - 0.3_3_7_2					) < 1e-3





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	int							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Tuple							  =	self.get_scheduler_config(prediction_type="""v_prediction"""					)
								UpperCAmelCase  :	int							  =	scheduler_class(**A					)

								UpperCAmelCase  :	Dict							  =	len(A					)

								UpperCAmelCase  :	Optional[Any]							  =	self.dummy_model()
								UpperCAmelCase  :	List[str]							  =	self.dummy_sample_deter
								UpperCAmelCase  :	Any							  =	torch.manual_seed(0					)

								for t in reversed(range(A					)					):
															# 1. predict noise residual
															UpperCAmelCase  :	Tuple							  =	model(A	,	A					)

															# 2. predict previous mean of sample x_t-1
															UpperCAmelCase  :	Dict							  =	scheduler.step(A	,	A	,	A	,	generator=A					).prev_sample

															UpperCAmelCase  :	Union[str, Any]							  =	pred_prev_sample

								UpperCAmelCase  :	str							  =	torch.sum(torch.abs(A					)					)
								UpperCAmelCase  :	Any							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_sum.item() - 2_0_2.0_2_9_6					) < 1e-2
								assert abs(result_mean.item() - 0.2_6_3_1					) < 1e-3





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	str							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	int							  =	self.get_scheduler_config()
								UpperCAmelCase  :	int							  =	scheduler_class(**A					)

								UpperCAmelCase  :	Optional[int]							  =	[100, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=A					)

								UpperCAmelCase  :	Dict							  =	scheduler.timesteps

								for i, timestep in enumerate(A					):
															if i == len(A					) - 1:
																						UpperCAmelCase  :	int							  =	-1
															else:
																						UpperCAmelCase  :	int							  =	timesteps[i + 1]

															UpperCAmelCase  :	str							  =	scheduler.previous_timestep(A					)
															UpperCAmelCase  :	Union[str, Any]							  =	prev_t.item()

															self.assertEqual(A	,	A					)





	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Union[str, Any]							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	int							  =	self.get_scheduler_config()
								UpperCAmelCase  :	str							  =	scheduler_class(**A					)

								UpperCAmelCase  :	str							  =	[100, 87, 50, 51, 0]

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





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Union[str, Any]							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Any							  =	self.get_scheduler_config()
								UpperCAmelCase  :	List[Any]							  =	scheduler_class(**A					)

								UpperCAmelCase  :	Dict							  =	[100, 87, 50, 1, 0]
								UpperCAmelCase  :	Dict							  =	len(A					)

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






	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	List[Any]							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Optional[Any]							  =	self.get_scheduler_config()
								UpperCAmelCase  :	List[str]							  =	scheduler_class(**A					)

								UpperCAmelCase  :	int							  =	[scheduler.config.num_train_timesteps]

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






'''simple docstring'''




a     :    List[Any]      						=					"""Alexander Joslin"""

import operator as op

from .stack import Stack



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	Dict							  =	{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

							UpperCAmelCase  :	Stack[int]							  =	Stack()
							UpperCAmelCase  :	Stack[str]							  =	Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(_lowercase	)	)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(_lowercase	)
														elif i == ")":
																					# RULE 4
																					UpperCAmelCase  :	List[Any]							  =	operator_stack.peek()
																					operator_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()

																					UpperCAmelCase  :	List[Any]							  =	operators[opr](_lowercase			,       _lowercase	)
																					operand_stack.push(_lowercase	)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
							a     :    Tuple      						=					"""(5 + ((4 * 2) * (2 + 3)))"""
							# answer = 45
							print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')

