


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   __lowercase				(  __snake_case ,					__snake_case  ):
      UpperCamelCase								=       1





      @register_to_config
      def __init__(		self						:					Optional[Any]					,       __lowerCamelCase						:					int = 1_0_0_0					,       __lowerCamelCase						:					Optional[Union[np.ndarray, List[float]]] = None       )    ->   Union[str, Any]:






         """simple docstring"""







         self.set_timesteps(__lowerCamelCase       )

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

         # running values
         UpperCAmelCase							=     []





      def        _lowercase				(		self						:					int					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					Union[str, torch.device] = None       )    ->   int:






         """simple docstring"""







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

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

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

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

         UpperCAmelCase							=     []





      def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					torch.FloatTensor					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					torch.FloatTensor					,       __lowerCamelCase						:					bool = True					,       )    ->   Union[SchedulerOutput, Tuple]:






         """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"""       )

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

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

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

         UpperCAmelCase							=     self._get_prev_sample(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=__lowerCamelCase       )





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					torch.FloatTensor					,       *__lowerCamelCase						:					Union[str, Any]					,       **__lowerCamelCase						:					str       )    ->   torch.FloatTensor:






         """simple docstring"""







         return sample





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Optional[Any]					,       __lowerCamelCase						:					Any       )    ->   Union[str, Any]:






         """simple docstring"""







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

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

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

         return prev_sample





      def __len__(		self						:					Union[str, Any]       )    ->   List[str]:






         """simple docstring"""







         return self.config.num_train_timesteps








__a     =			[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   while place < len(lowerCAmelCase_   ):
      if (place + 1 < len(lowerCAmelCase_   )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     []
   for arabic, roman in ROMAN:
      ((UpperCAmelCase)				,  (UpperCAmelCase))							=     divmod(lowerCAmelCase_  ,						lowerCAmelCase_   )
      result.append(roman * factor   )
      if number == 0:
         break
   return "".join(lowerCAmelCase_   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



