


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Union[str, Any]	   =       (EulerDiscreteScheduler,)
     __lowercase				:							str	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		**_a:str   ):
       snake_case__						= {
           '''num_train_timesteps''': 11_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       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=_a     ,		beta_end=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__						= torch.manual_seed(0   )

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

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

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config(prediction_type='''v_prediction'''   )
       snake_case__						= scheduler_class(**_a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__						= torch.manual_seed(0   )

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

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

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

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

       assert abs(result_sum.item() - 0.0002   ) < 1e-2
       assert abs(result_mean.item() - 2.2_676e-06   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

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

       snake_case__						= torch.manual_seed(0   )

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

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

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

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

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







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a     ,		use_karras_sigmas=_a   )

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

       snake_case__						= torch.manual_seed(0   )

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

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

         snake_case__						= model(_a     ,		_a   )

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   )
         snake_case__						= output.prev_sample

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

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





lowerCamelCase__							:						List[str]							     =   """Alexander Joslin"""

import operator as op

from .stack import Stack







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				int:
  snake_case__						= {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

  snake_case__						= Stack()
  snake_case__						= Stack()

  for i in equation:
    if i.isdigit():
      # RULE 1
      operand_stack.push(int(__lowerCAmelCase				)				)
    elif i in operators:
      # RULE 2
      operator_stack.push(__lowerCAmelCase				)
    elif i == ")":
      # RULE 4
      snake_case__						= operator_stack.peek()
      operator_stack.pop()
      snake_case__						= operand_stack.peek()
      operand_stack.pop()
      snake_case__						= operand_stack.peek()
      operand_stack.pop()

      snake_case__						= operators[opr](__lowerCAmelCase							,   __lowerCAmelCase				)
      operand_stack.push(__lowerCAmelCase				)

    # RULE 5
  return operand_stack.peek()


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   """(5 + ((4 * 2) * (2 + 3)))"""
   # answer = 45
   print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")

