
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 							SCREAMING_SNAKE_CASE__     (				__snake_case     ):

							"""simple docstring"""






							_snake_case  		=	(HeunDiscreteScheduler,)
							_snake_case  		=	10



							def      A__		(	self  ,						**SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      {
													    """num_train_timesteps""": 1100,
													    """beta_start""": 0.0_0_0_1,
													    """beta_end""": 0.0_2,
													    """beta_schedule""": """linear""",
													}

													config.update(**lowerCAmelCase__      )
													return config



							def      A__		(	self      )-> int:





													'''simple docstring'''






													for timesteps in [10, 50, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=lowerCAmelCase__      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																			self.check_over_configs(beta_start=lowerCAmelCase__  ,						beta_end=lowerCAmelCase__      )



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													for schedule in ["linear", "scaled_linear", "exp"]:
																			self.check_over_configs(beta_schedule=lowerCAmelCase__      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													for clip_sample_range in [1.0, 2.0, 3.0]:
																			self.check_over_configs(clip_sample_range=lowerCAmelCase__  ,						clip_sample=lowerCAmelCase__      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													for prediction_type in ["epsilon", "v_prediction", "sample"]:
																			self.check_over_configs(prediction_type=lowerCAmelCase__      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.scheduler_classes[0]
													__UpperCamelCase  	=      self.get_scheduler_config()
													__UpperCamelCase  	=      scheduler_class(**lowerCAmelCase__      )

													scheduler.set_timesteps(self.num_inference_steps      )

													__UpperCamelCase  	=      self.dummy_model()
													__UpperCamelCase  	=      self.dummy_sample_deter * scheduler.init_noise_sigma
													__UpperCamelCase  	=      sample.to(lowerCAmelCase__      )

													for i, t in enumerate(scheduler.timesteps      ):
																			__UpperCamelCase  	=      scheduler.scale_model_input(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      model(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      scheduler.step(lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__      )
																			__UpperCamelCase  	=      output.prev_sample

													__UpperCamelCase  	=      torch.sum(torch.abs(lowerCAmelCase__      )      )
													__UpperCamelCase  	=      torch.mean(torch.abs(lowerCAmelCase__      )      )

													if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 0.1_2_3_3      ) < 1E-2
																			assert abs(result_mean.item() - 0.0_0_0_2      ) < 1E-3
													else:
																			# CUDA
																			assert abs(result_sum.item() - 0.1_2_3_3      ) < 1E-2
																			assert abs(result_mean.item() - 0.0_0_0_2      ) < 1E-3



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.scheduler_classes[0]
													__UpperCamelCase  	=      self.get_scheduler_config(prediction_type='''v_prediction'''      )
													__UpperCamelCase  	=      scheduler_class(**lowerCAmelCase__      )

													scheduler.set_timesteps(self.num_inference_steps      )

													__UpperCamelCase  	=      self.dummy_model()
													__UpperCamelCase  	=      self.dummy_sample_deter * scheduler.init_noise_sigma
													__UpperCamelCase  	=      sample.to(lowerCAmelCase__      )

													for i, t in enumerate(scheduler.timesteps      ):
																			__UpperCamelCase  	=      scheduler.scale_model_input(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      model(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      scheduler.step(lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__      )
																			__UpperCamelCase  	=      output.prev_sample

													__UpperCamelCase  	=      torch.sum(torch.abs(lowerCAmelCase__      )      )
													__UpperCamelCase  	=      torch.mean(torch.abs(lowerCAmelCase__      )      )

													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_0_0_2      ) < 1E-3



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.scheduler_classes[0]
													__UpperCamelCase  	=      self.get_scheduler_config()
													__UpperCamelCase  	=      scheduler_class(**lowerCAmelCase__      )

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

													__UpperCamelCase  	=      self.dummy_model()
													__UpperCamelCase  	=      self.dummy_sample_deter.to(lowerCAmelCase__      ) * scheduler.init_noise_sigma

													for t in scheduler.timesteps:
																			__UpperCamelCase  	=      scheduler.scale_model_input(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      model(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      scheduler.step(lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__      )
																			__UpperCamelCase  	=      output.prev_sample

													__UpperCamelCase  	=      torch.sum(torch.abs(lowerCAmelCase__      )      )
													__UpperCamelCase  	=      torch.mean(torch.abs(lowerCAmelCase__      )      )

													if str(lowerCAmelCase__      ).startswith('''cpu'''      ):
																			# The following sum varies between 148 and 156 on mps. Why?
																			assert abs(result_sum.item() - 0.1_2_3_3      ) < 1E-2
																			assert abs(result_mean.item() - 0.0_0_0_2      ) < 1E-3
													elif str(lowerCAmelCase__      ).startswith('''mps'''      ):
																			# Larger tolerance on mps
																			assert abs(result_mean.item() - 0.0_0_0_2      ) < 1E-2
													else:
																			# CUDA
																			assert abs(result_sum.item() - 0.1_2_3_3      ) < 1E-2
																			assert abs(result_mean.item() - 0.0_0_0_2      ) < 1E-3



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      self.scheduler_classes[0]
													__UpperCamelCase  	=      self.get_scheduler_config()
													__UpperCamelCase  	=      scheduler_class(**lowerCAmelCase__  ,						use_karras_sigmas=lowerCAmelCase__      )

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

													__UpperCamelCase  	=      self.dummy_model()
													__UpperCamelCase  	=      self.dummy_sample_deter.to(lowerCAmelCase__      ) * scheduler.init_noise_sigma
													__UpperCamelCase  	=      sample.to(lowerCAmelCase__      )

													for t in scheduler.timesteps:
																			__UpperCamelCase  	=      scheduler.scale_model_input(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      model(lowerCAmelCase__  ,						lowerCAmelCase__      )

																			__UpperCamelCase  	=      scheduler.step(lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__      )
																			__UpperCamelCase  	=      output.prev_sample

													__UpperCamelCase  	=      torch.sum(torch.abs(lowerCAmelCase__      )      )
													__UpperCamelCase  	=      torch.mean(torch.abs(lowerCAmelCase__      )      )

													assert abs(result_sum.item() - 0.0_0_0_1_5      ) < 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				(				__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))