
"""simple docstring"""





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						(KDPMaAncestralDiscreteScheduler,)
  SCREAMING_SNAKE_CASE__     :		str    	=						10







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       {
         "num_train_timesteps": 1100,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_    )
     return config







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for timesteps in [10, 50, 100, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,  [0.00_02, 0.0_02, 0.02]    ):
        self.check_over_configs(beta_start=lowercase_							,  beta_end=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if torch_device == "mps":
        return
     UpperCAmelCase_   :   List[str]     	=       self.scheduler_classes[0]
     UpperCAmelCase_   :   str     	=       self.get_scheduler_config()
     UpperCAmelCase_   :   List[Any]     	=       scheduler_class(**lowercase_    )

     scheduler.set_timesteps(self.num_inference_steps    )

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

     UpperCAmelCase_   :   Union[str, Any]     	=       self.dummy_model()
     UpperCAmelCase_   :   Tuple     	=       self.dummy_sample_deter * scheduler.init_noise_sigma
     UpperCAmelCase_   :   int     	=       sample.to(lowercase_    )

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

        UpperCAmelCase_   :   List[Any]     	=       model(lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Tuple     	=       scheduler.step(lowercase_							,  lowercase_							,  lowercase_							,  generator=lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       output.prev_sample

     UpperCAmelCase_   :   List[Any]     	=       torch.sum(torch.abs(lowercase_    )    )
     UpperCAmelCase_   :   List[str]     	=       torch.mean(torch.abs(lowercase_    )    )

     assert abs(result_sum.item() - 1_38_49.38_77    ) < 1E-2
     assert abs(result_mean.item() - 18.03_31    ) < 5E-3







  def 			UpperCamelCase__   (						self    ):

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if torch_device == "mps":
        return
     UpperCAmelCase_   :   Union[str, Any]     	=       self.scheduler_classes[0]
     UpperCAmelCase_   :   Tuple     	=       self.get_scheduler_config(prediction_type="v_prediction"    )
     UpperCAmelCase_   :   Union[str, Any]     	=       scheduler_class(**lowercase_    )

     scheduler.set_timesteps(self.num_inference_steps    )

     UpperCAmelCase_   :   Optional[int]     	=       self.dummy_model()
     UpperCAmelCase_   :   Union[str, Any]     	=       self.dummy_sample_deter * scheduler.init_noise_sigma
     UpperCAmelCase_   :   Tuple     	=       sample.to(lowercase_    )

     UpperCAmelCase_   :   Optional[Any]     	=       torch.manual_seed(0    )

     for i, t in enumerate(scheduler.timesteps    ):
        UpperCAmelCase_   :   Union[str, Any]     	=       scheduler.scale_model_input(lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Optional[int]     	=       model(lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Dict     	=       scheduler.step(lowercase_							,  lowercase_							,  lowercase_							,  generator=lowercase_    )
        UpperCAmelCase_   :   int     	=       output.prev_sample

     UpperCAmelCase_   :   List[Any]     	=       torch.sum(torch.abs(lowercase_    )    )
     UpperCAmelCase_   :   Tuple     	=       torch.mean(torch.abs(lowercase_    )    )

     assert abs(result_sum.item() - 3_28.99_70    ) < 1E-2
     assert abs(result_mean.item() - 0.42_84    ) < 1E-3







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if torch_device == "mps":
        return
     UpperCAmelCase_   :   Any     	=       self.scheduler_classes[0]
     UpperCAmelCase_   :   Optional[int]     	=       self.get_scheduler_config()
     UpperCAmelCase_   :   str     	=       scheduler_class(**lowercase_    )

     scheduler.set_timesteps(self.num_inference_steps							,  device=lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       torch.manual_seed(0    )

     UpperCAmelCase_   :   List[Any]     	=       self.dummy_model()
     UpperCAmelCase_   :   Dict     	=       self.dummy_sample_deter.to(lowercase_    ) * scheduler.init_noise_sigma

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

        UpperCAmelCase_   :   Union[str, Any]     	=       model(lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Optional[Any]     	=       scheduler.step(lowercase_							,  lowercase_							,  lowercase_							,  generator=lowercase_    )
        UpperCAmelCase_   :   Union[str, Any]     	=       output.prev_sample

     UpperCAmelCase_   :   Dict     	=       torch.sum(torch.abs(lowercase_    )    )
     UpperCAmelCase_   :   int     	=       torch.mean(torch.abs(lowercase_    )    )

     assert abs(result_sum.item() - 1_38_49.38_18    ) < 1E-1
     assert abs(result_mean.item() - 18.03_31    ) < 1E-3



'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







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


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

      testmod()
      _lowerCamelCase :				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))