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		(   UpperCamelCase_    ,     UpperCamelCase_    ):
    __a  =      1
    @register_to_config
    def __init__(  self , lowerCAmelCase = 1000 , lowerCAmelCase = None   )       ->					Optional[Any]:
     # set `betas`, `alphas`, `timesteps`
     self.set_timesteps(lowerCAmelCase   )

     # standard deviation of the initial noise distribution
     SCREAMING_SNAKE_CASE__:   Optional[int]=      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.
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      4

     # running values
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      []
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Tuple=      num_inference_steps
     SCREAMING_SNAKE_CASE__:   List[str]=      torch.linspace(1 , 0 , num_inference_steps + 1   )[:-1]
     SCREAMING_SNAKE_CASE__:   List[str]=      torch.cat([steps, torch.tensor([0.0]   )]   )

     if self.config.trained_betas is not None:
      SCREAMING_SNAKE_CASE__:   List[Any]=      torch.tensor(self.config.trained_betas , dtype=torch.floataa   )
     else:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.sin(steps * math.pi / 2   ) ** 2

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (1.0 - self.betas**2) ** 0.5

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      (torch.atana(self.betas , self.alphas   ) / math.pi * 2)[:-1]
     SCREAMING_SNAKE_CASE__:   str=      timesteps.to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      []
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = True , )       ->					Union[SchedulerOutput, Tuple]:
     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'''   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      (self.timesteps == timestep).nonzero().item()
     SCREAMING_SNAKE_CASE__:   Optional[int]=      timestep_index + 1

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
     self.ets.append(lowerCAmelCase   )

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

     SCREAMING_SNAKE_CASE__:   Dict=      self._get_prev_sample(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )

     if not return_dict:
      return (prev_sample,)

     return SchedulerOutput(prev_sample=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , *lowerCAmelCase , **lowerCAmelCase   )       ->					torch.FloatTensor:
     return sample
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.alphas[timestep_index]
     SCREAMING_SNAKE_CASE__:   List[str]=      self.betas[timestep_index]

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.alphas[prev_timestep_index]
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.betas[prev_timestep_index]

     SCREAMING_SNAKE_CASE__:   List[str]=      (sample - sigma * ets) / max(lowerCAmelCase , 1e-8   )
     SCREAMING_SNAKE_CASE__:   int=      next_alpha * pred + ets * next_sigma

     return prev_sample



    def __len__(  self   )       ->					Optional[Any]:
     return self.config.num_train_timesteps


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowercase_     :   str 				=				re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      None
    __a  =      None
    __a  =      None
    __a  =      None
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      _str_to_version_tuple(self.version_str   )
    def __repr__(  self   )       ->					Dict:
     return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     return self.major, self.minor, self.patch
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Dict:
     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return Version(lowerCAmelCase   )
     elif isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return other
     raise TypeError(f'{other} (type {type(lowerCAmelCase   )}) cannot be compared to version.'   )
    def __eq__(  self , lowerCAmelCase   )       ->					Optional[int]:
     try:
      SCREAMING_SNAKE_CASE__:   List[str]=      self._validate_operand(lowerCAmelCase   )
     except (TypeError, ValueError):
      return False
     else:
      return self.tuple == other.tuple
    def __lt__(  self , lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self._validate_operand(lowerCAmelCase   )
     return self.tuple < other.tuple
    def __hash__(  self   )       ->					List[Any]:
     return hash(_version_tuple_to_str(self.tuple   )   )
    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Dict=      {f.name for f in dataclasses.fields(cls   )}
     return cls(**{k: v for k, v in dic.items() if k in field_names}   )



    def 						UpperCamelCase_				(  self   )       ->					str:
     return self.version_str







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   List[Any]=      _VERSION_REG.match(snake_case_      )
 if not res:
  raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'      )
 return tuple(int(snake_case_      ) for v in [res.group('''major'''      ), res.group('''minor'''      ), res.group('''patch'''      )]      )







def 			A__      (	snake_case_ :		Union[str, Any]      ):
 return ".".join(str(snake_case_      ) for v in version_tuple      )

