




'''simple docstring'''







import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    List[str]									=				(HeunDiscreteScheduler,)
     lowerCAmelCase_     :    Dict									=				10







     def  snake_case_			(							self       ,**__a       )			->							List[Any]:
          snake_case :			int					   = {
              """num_train_timesteps""": 1_100,
              """beta_start""": 0.0_001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
          }

          config.update(**__a       )
          return config







     def  snake_case_			(							self       )			->							Union[str, Any]:
          for timesteps in [10, 50, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__a       )







     def  snake_case_			(							self       )			->							List[Any]:
          for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,[0.0_002, 0.002, 0.02]       ):
               self.check_over_configs(beta_start=__a       ,beta_end=__a       )







     def  snake_case_			(							self       )			->							Dict:
          for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=__a       )







     def  snake_case_			(							self       )			->							str:
          for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=__a       ,clip_sample=__a       )







     def  snake_case_			(							self       )			->							int:
          for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=__a       )







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			Optional[Any]					   = self.scheduler_classes[0]
          snake_case :			Tuple					   = self.get_scheduler_config()
          snake_case :			Optional[int]					   = scheduler_class(**__a       )

          scheduler.set_timesteps(self.num_inference_steps       )

          snake_case :			List[Any]					   = self.dummy_model()
          snake_case :			Union[str, Any]					   = self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case :			Optional[Any]					   = sample.to(__a       )

          for i, t in enumerate(scheduler.timesteps       ):
               snake_case :			Optional[Any]					   = scheduler.scale_model_input(__a       ,__a       )

               snake_case :			str					   = model(__a       ,__a       )

               snake_case :			str					   = scheduler.step(__a       ,__a       ,__a       )
               snake_case :			str					   = output.prev_sample

          snake_case :			int					   = torch.sum(torch.abs(__a       )       )
          snake_case :			Union[str, Any]					   = torch.mean(torch.abs(__a       )       )

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1_233       ) < 1E-2
               assert abs(result_mean.item() - 0.0_002       ) < 1E-3
          else:
               # CUDA
               assert abs(result_sum.item() - 0.1_233       ) < 1E-2
               assert abs(result_mean.item() - 0.0_002       ) < 1E-3







     def  snake_case_			(							self       )			->							Tuple:
          snake_case :			Any					   = self.scheduler_classes[0]
          snake_case :			Optional[Any]					   = self.get_scheduler_config(prediction_type="""v_prediction"""       )
          snake_case :			Optional[Any]					   = scheduler_class(**__a       )

          scheduler.set_timesteps(self.num_inference_steps       )

          snake_case :			Optional[Any]					   = self.dummy_model()
          snake_case :			List[str]					   = self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case :			List[Any]					   = sample.to(__a       )

          for i, t in enumerate(scheduler.timesteps       ):
               snake_case :			List[Any]					   = scheduler.scale_model_input(__a       ,__a       )

               snake_case :			Any					   = model(__a       ,__a       )

               snake_case :			int					   = scheduler.step(__a       ,__a       ,__a       )
               snake_case :			List[str]					   = output.prev_sample

          snake_case :			str					   = torch.sum(torch.abs(__a       )       )
          snake_case :			List[Any]					   = torch.mean(torch.abs(__a       )       )

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934E-07       ) < 1E-2
               assert abs(result_mean.item() - 6.1112E-10       ) < 1E-3
          else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972E-07       ) < 1E-2
               assert abs(result_mean.item() - 0.0_002       ) < 1E-3







     def  snake_case_			(							self       )			->							List[Any]:
          snake_case :			str					   = self.scheduler_classes[0]
          snake_case :			Optional[Any]					   = self.get_scheduler_config()
          snake_case :			Optional[Any]					   = scheduler_class(**__a       )

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

          snake_case :			List[str]					   = self.dummy_model()
          snake_case :			List[str]					   = self.dummy_sample_deter.to(__a       ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
               snake_case :			Optional[Any]					   = scheduler.scale_model_input(__a       ,__a       )

               snake_case :			Tuple					   = model(__a       ,__a       )

               snake_case :			str					   = scheduler.step(__a       ,__a       ,__a       )
               snake_case :			Dict					   = output.prev_sample

          snake_case :			Optional[int]					   = torch.sum(torch.abs(__a       )       )
          snake_case :			Optional[int]					   = torch.mean(torch.abs(__a       )       )

          if str(__a       ).startswith("""cpu"""       ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1_233       ) < 1E-2
               assert abs(result_mean.item() - 0.0_002       ) < 1E-3
          elif str(__a       ).startswith("""mps"""       ):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0_002       ) < 1E-2
          else:
               # CUDA
               assert abs(result_sum.item() - 0.1_233       ) < 1E-2
               assert abs(result_mean.item() - 0.0_002       ) < 1E-3







     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case :			Dict					   = self.scheduler_classes[0]
          snake_case :			Any					   = self.get_scheduler_config()
          snake_case :			Dict					   = scheduler_class(**__a       ,use_karras_sigmas=__a       )

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

          snake_case :			Any					   = self.dummy_model()
          snake_case :			Optional[Any]					   = self.dummy_sample_deter.to(__a       ) * scheduler.init_noise_sigma
          snake_case :			Dict					   = sample.to(__a       )

          for t in scheduler.timesteps:
               snake_case :			List[Any]					   = scheduler.scale_model_input(__a       ,__a       )

               snake_case :			Dict					   = model(__a       ,__a       )

               snake_case :			int					   = scheduler.step(__a       ,__a       ,__a       )
               snake_case :			Optional[int]					   = output.prev_sample

          snake_case :			Tuple					   = torch.sum(torch.abs(__a       )       )
          snake_case :			Union[str, Any]					   = torch.mean(torch.abs(__a       )       )

          assert abs(result_sum.item() - 0.00_015       ) < 1E-2
          assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07       ) < 1E-2









'''simple docstring'''







from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def 					lowerCamelCase__							(			__lowercase							):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__lowercase    ,      -1							)



def 					lowerCamelCase__							(			__lowercase							):
     if not balanced_parentheses(__lowercase							):
          raise ValueError("""Mismatched parentheses"""							)
     snake_case :			Stack[str]					   = Stack()
     snake_case :			List[Any]					   = []
     for char in expression_str:
          if char.isalpha() or char.isdigit():
               postfix.append(__lowercase							)
          elif char == "(":
               stack.push(__lowercase							)
          elif char == ")":
               while not stack.is_empty() and stack.peek() != "(":
                    postfix.append(stack.pop()							)
               stack.pop()
          else:
               while not stack.is_empty() and precedence(__lowercase							) <= precedence(stack.peek()							):
                    postfix.append(stack.pop()							)
               stack.push(__lowercase							)
     while not stack.is_empty():
          postfix.append(stack.pop()							)
     return " ".join(__lowercase							)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      lowercase			:      Tuple			=					"""a+b*(c^d-e)^(f+g*h)-i"""

      print("""Infix to Postfix Notation demonstration:\n""")
      print("""Infix notation: """ + expression)
      print("""Postfix notation: """ + infix_to_postfix(expression))



