






"""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 						snake_case  (  __a       ,			__a	):
				__magic_name__   					=					1





				@register_to_config
				def __init__(							self     : Optional[int]    ,		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
						a					:      str    =       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.
						a					:      Optional[int]    =       4

						# running values
						a					:      Dict    =       []





				def     lowerCamelCase__				(							self     : Dict    ,		A     : int    ,		A     : Union[str, torch.device] = None      ):


						'''simple docstring'''




						a					:      List[str]    =       num_inference_steps
						a					:      List[str]    =       torch.linspace(1    ,		0    ,		num_inference_steps + 1      )[:-1]
						a					:      Optional[int]    =       torch.cat([steps, torch.tensor([0.0]      )]      )

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

						a					:      int    =       (1.0 - self.betas**2) ** 0.5

						a					:      Optional[int]    =       (torch.atana(self.betas    ,		self.alphas      ) / math.pi * 2)[:-1]
						a					:      List[Any]    =       timesteps.to(a__      )

						a					:      str    =       []





				def     lowerCamelCase__				(							self     : Union[str, Any]    ,		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'      )

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

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

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

						a					:      Optional[int]    =       self._get_prev_sample(a__    ,		a__    ,		a__    ,		a__      )

						if not return_dict:
								return (prev_sample,)

						return SchedulerOutput(prev_sample=a__      )





				def     lowerCamelCase__				(							self     : Any    ,		A     : torch.FloatTensor    ,		*A     : Optional[int]    ,		**A     : Union[str, Any]      ):


						'''simple docstring'''




						return sample





				def     lowerCamelCase__				(							self     : str    ,		A     : Optional[int]    ,		A     : int    ,		A     : List[Any]    ,		A     : List[str]      ):


						'''simple docstring'''




						a					:      List[Any]    =       self.alphas[timestep_index]
						a					:      Dict    =       self.betas[timestep_index]

						a					:      Dict    =       self.alphas[prev_timestep_index]
						a					:      List[str]    =       self.betas[prev_timestep_index]

						a					:      List[Any]    =       (sample - sigma * ets) / max(a__    ,		1E-8      )
						a					:      Optional[int]    =       next_alpha * pred + ets * next_sigma

						return prev_sample





				def __len__(							self     : int      ):


						'''simple docstring'''




						return self.config.num_train_timesteps












"""simple docstring"""


from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




