


"""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    lowerCamelCase__						(		_a			,   _a    ):
      a :     int											=		1
      @register_to_config
      def __init__(		self	:	List[Any]      ,       A_	:	int = 1_0_0_0      ,       A_	:	Optional[Union[np.ndarray, List[float]]] = None							):






       '''simple docstring'''


       self.set_timesteps(A_							)

       # standard deviation of the initial noise distribution
       __lowercase									=	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.
       __lowercase									=	4

       # running values
       __lowercase									=	[]
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	int      ,       A_	:	Union[str, torch.device] = None							):






       '''simple docstring'''


       __lowercase									=	num_inference_steps
       __lowercase									=	torch.linspace(1      ,       0      ,       num_inference_steps + 1							)[:-1]
       __lowercase									=	torch.cat([steps, torch.tensor([0.0]							)]							)

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

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

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

       __lowercase									=	[]
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	torch.FloatTensor      ,       A_	:	int      ,       A_	:	torch.FloatTensor      ,       A_	:	bool = True      ,       ):






       '''simple docstring'''


       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"""							)

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

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

       if len(self.ets							) == 1:
        __lowercase									=	self.ets[-1]
       elif len(self.ets							) == 2:
        __lowercase									=	(3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets							) == 3:
        __lowercase									=	(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
       else:
        __lowercase									=	(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

       __lowercase									=	self._get_prev_sample(A_      ,       A_      ,       A_      ,       A_							)

       if not return_dict:
        return (prev_sample,)

       return SchedulerOutput(prev_sample=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	torch.FloatTensor      ,       *A_	:	List[str]      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


       return sample
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Optional[Any]      ,       A_	:	Any      ,       A_	:	List[Any]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.alphas[timestep_index]
       __lowercase									=	self.betas[timestep_index]

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

       __lowercase									=	(sample - sigma * ets) / max(A_      ,       1e-8							)
       __lowercase									=	next_alpha * pred + ets * next_sigma

       return prev_sample






      def __len__(		self	:	List[Any]							):






       '''simple docstring'''


       return self.config.num_train_timesteps




"""simple docstring"""

from typing import List

from .keymap import KEYMAP, get_character


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += [key]
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator


def lowerCAmelCase_      (				*UpperCamelCase__	: List[str] ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += keys
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator





class    lowerCamelCase__						(		_a    ):
      def __new__(		cls	:	str      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	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():
        __lowercase									=	getattr(A_      ,       """handle_key"""      ,       []							)
        for key in handled_keys:
         __lowercase									=	value
       return new_cls






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Dict							):






       '''simple docstring'''


       __lowercase									=	get_character()
       if char != KEYMAP["undefined"]:
        __lowercase									=	ord(A_							)
       __lowercase									=	cls.key_handler.get(A_							)
       if handler:
        __lowercase									=	char
        return handler(cls							)
       else:
        return None






def lowerCAmelCase_      (				cls	: int ):






 """simple docstring"""







 return KeyHandler(cls.__name__				,   cls.__bases__				,   cls.__dict__.copy() )
