






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_				(      lowerCamelCase ):
    a__					       =  (CMStochasticIterativeScheduler,)
    a__					       =  10







    def 			A	(						self       ,					**__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:List[str]        =      {
            '''num_train_timesteps''': 2_0_1,
            '''sigma_min''': 0.002,
            '''sigma_max''': 80.0,
        }

        config.update(**UpperCAmelCase__     )
        return config







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[int]        =      1_0

        __magic_name__		:Dict        =      self.get_scheduler_config()
        __magic_name__		:Dict        =      self.scheduler_classes[0](**UpperCAmelCase__     )

        scheduler.set_timesteps(UpperCAmelCase__     )

        __magic_name__		:List[str]        =      scheduler.timesteps[0]
        __magic_name__		:Tuple        =      scheduler.timesteps[1]

        __magic_name__		:str        =      self.dummy_sample
        __magic_name__		:Union[str, Any]        =      0.1 * sample

        __magic_name__		:str        =      scheduler.step(UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__     ).prev_sample
        __magic_name__		:int        =      scheduler.step(UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__     ).prev_sample

        self.assertEqual(output_a.shape       ,					sample.shape     )
        self.assertEqual(output_a.shape       ,					output_a.shape     )







    def 			A	(						self     ):
        """simple docstring"""
        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=UpperCAmelCase__     )







    def 			A	(						self     ):
        """simple docstring"""
        for clip_denoised in [True, False]:
            self.check_over_configs(clip_denoised=UpperCAmelCase__     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:str        =      self.scheduler_classes[0]
        __magic_name__		:Optional[int]        =      self.get_scheduler_config()
        __magic_name__		:Union[str, Any]        =      scheduler_class(**UpperCAmelCase__     )

        __magic_name__		:List[str]        =      1
        scheduler.set_timesteps(UpperCAmelCase__     )
        __magic_name__		:Optional[Any]        =      scheduler.timesteps

        __magic_name__		:Union[str, Any]        =      torch.manual_seed(0     )

        __magic_name__		:Union[str, Any]        =      self.dummy_model()
        __magic_name__		:Optional[Any]        =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(UpperCAmelCase__     ):
            # 1. scale model input
            __magic_name__		:List[str]        =      scheduler.scale_model_input(UpperCAmelCase__       ,					UpperCAmelCase__     )

            # 2. predict noise residual
            __magic_name__		:int        =      model(UpperCAmelCase__       ,					UpperCAmelCase__     )

            # 3. predict previous sample x_t-1
            __magic_name__		:Union[str, Any]        =      scheduler.step(UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					generator=UpperCAmelCase__     ).prev_sample

            __magic_name__		:Union[str, Any]        =      pred_prev_sample

        __magic_name__		:Any        =      torch.sum(torch.abs(UpperCAmelCase__     )     )
        __magic_name__		:Optional[int]        =      torch.mean(torch.abs(UpperCAmelCase__     )     )

        assert abs(result_sum.item() - 192.7614     ) < 1E-2
        assert abs(result_mean.item() - 0.2510     ) < 1E-3







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Dict        =      self.scheduler_classes[0]
        __magic_name__		:Union[str, Any]        =      self.get_scheduler_config()
        __magic_name__		:Optional[Any]        =      scheduler_class(**UpperCAmelCase__     )

        __magic_name__		:List[Any]        =      [1_0_6, 0]
        scheduler.set_timesteps(timesteps=UpperCAmelCase__     )
        __magic_name__		:int        =      scheduler.timesteps

        __magic_name__		:int        =      torch.manual_seed(0     )

        __magic_name__		:Optional[Any]        =      self.dummy_model()
        __magic_name__		:List[str]        =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for t in timesteps:
            # 1. scale model input
            __magic_name__		:Any        =      scheduler.scale_model_input(UpperCAmelCase__       ,					UpperCAmelCase__     )

            # 2. predict noise residual
            __magic_name__		:List[Any]        =      model(UpperCAmelCase__       ,					UpperCAmelCase__     )

            # 3. predict previous sample x_t-1
            __magic_name__		:List[str]        =      scheduler.step(UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					generator=UpperCAmelCase__     ).prev_sample

            __magic_name__		:Any        =      pred_prev_sample

        __magic_name__		:int        =      torch.sum(torch.abs(UpperCAmelCase__     )     )
        __magic_name__		:List[str]        =      torch.mean(torch.abs(UpperCAmelCase__     )     )

        assert abs(result_sum.item() - 347.6357     ) < 1E-2
        assert abs(result_mean.item() - 0.4527     ) < 1E-3







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      self.scheduler_classes[0]
        __magic_name__		:Optional[Any]        =      self.get_scheduler_config()
        __magic_name__		:str        =      scheduler_class(**UpperCAmelCase__     )

        __magic_name__		:str        =      [3_9, 3_0, 1_2, 1_5, 0]

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







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[int]        =      self.scheduler_classes[0]
        __magic_name__		:Tuple        =      self.get_scheduler_config()
        __magic_name__		:Dict        =      scheduler_class(**UpperCAmelCase__     )

        __magic_name__		:Tuple        =      [3_9, 3_0, 1_2, 1, 0]
        __magic_name__		:List[Any]        =      len(UpperCAmelCase__     )

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







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Tuple        =      self.scheduler_classes[0]
        __magic_name__		:Tuple        =      self.get_scheduler_config()
        __magic_name__		:int        =      scheduler_class(**UpperCAmelCase__     )

        __magic_name__		:List[Any]        =      [scheduler.config.num_train_timesteps]

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














SCREAMING_SNAKE_CASE__      :			str							=		"""Alexander Joslin"""

import operator as op

from .stack import Stack

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

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

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

												__magic_name__		:Optional[int]        =      operators[opr](snake_case,  snake_case )
												operand_stack.push(snake_case )

    # RULE 5
				return operand_stack.peek()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Any							=		"""(5 + ((4 * 2) * (2 + 3)))"""
					# answer = 45
					print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")






