
from __future__ import annotations

from typing import Any


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				if not postfix_notation:
								return 0

				__A   			= {"+", "-", "*", "/"}
				__A   			= []

				for token in postfix_notation:
								if token in operations:
												__A			,							__A   			= stack.pop(), stack.pop()
												if token == "+":
																stack.append(a + b						)
												elif token == "-":
																stack.append(a - b						)
												elif token == "*":
																stack.append(a * b						)
												else:
																if a * b < 0 and a % b != 0:
																				stack.append(a // b + 1						)
																else:
																				stack.append(a // b						)
								else:
												stack.append(int(a_						)						)

				return stack.pop()


if __name__ == "__main__":
					import doctest

					doctest.testmod()


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
