

'''simple docstring'''




from typing import List

from .keymap import KEYMAP, get_character




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''

     def decorator(a			):
          __magic_name__						       =    getattr(a		,						'''handle_key'''		,						[]			)
          handle += [key]
          setattr(a		,						'''handle_key'''		,						a			)
          return func

     return decorator




def 						UpperCamelCase	(			*a			)				->     Optional[int]:






     '''simple docstring'''

     def decorator(a			):
          __magic_name__						       =    getattr(a		,						'''handle_key'''		,						[]			)
          handle += keys
          setattr(a		,						'''handle_key'''		,						a			)
          return func

     return decorator






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __new__(   cls		:   Optional[int]			,				a__		:   str			,				a__		:   int			,				a__		:   Union[str, Any]      ):
      __magic_name__						       =    super().__new__(cls			,				a__			,				a__			,				a__      )
      if not hasattr(a__			,				'''key_handler'''      ):
           setattr(a__			,				'''key_handler'''			,				{}      )
      setattr(a__			,				'''handle_input'''			,				KeyHandler.handle_input      )

      for value in attrs.values():
           __magic_name__						       =    getattr(a__			,				'''handle_key'''			,				[]      )
           for key in handled_keys:
                __magic_name__						       =    value
      return new_cls



 @staticmethod
 def 			snake_case__  (   cls		:   int      ):
      __magic_name__						       =    get_character()
      if char != KEYMAP["undefined"]:
           __magic_name__						       =    ord(a__      )
      __magic_name__						       =    cls.key_handler.get(a__      )
      if handler:
           __magic_name__						       =    char
           return handler(cls      )
      else:
           return None




def 						UpperCamelCase	(			cls			)				->     Dict:






     '''simple docstring'''
     return KeyHandler(cls.__name__		,						cls.__bases__		,						cls.__dict__.copy()			)









'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

      if self.config.trained_betas is not None:
           __magic_name__						       =    torch.tensor(self.config.trained_betas			,				dtype=torch.floataa      )
      else:
           __magic_name__						       =    torch.sin(steps * math.pi / 2      ) ** 2

      __magic_name__						       =    (1.0 - self.betas**2) ** 0.5

      __magic_name__						       =    (torch.atana(self.betas			,				self.alphas      ) / math.pi * 2)[:-1]
      __magic_name__						       =    timesteps.to(a__      )

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      if self.num_inference_steps is None:
           raise ValueError(
               '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''      )

      __magic_name__						       =    (self.timesteps == timestep).nonzero().item()
      __magic_name__						       =    timestep_index + 1

      __magic_name__						       =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(a__      )

      if len(self.ets      ) == 1:
           __magic_name__						       =    self.ets[-1]
      elif len(self.ets      ) == 2:
           __magic_name__						       =    (3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets      ) == 3:
           __magic_name__						       =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           __magic_name__						       =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

      __magic_name__						       =    self.alphas[prev_timestep_index]
      __magic_name__						       =    self.betas[prev_timestep_index]

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






