



'''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		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  1






    @register_to_config
    def __init__(  self      :					Dict  ,						UpperCamelCase__      :					int = 1_0_0_0  ,						UpperCamelCase__      :					Optional[Union[np.ndarray, List[float]]] = None ):


       """simple docstring"""







       self.set_timesteps(UpperCamelCase__ )

       # 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  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """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(UpperCamelCase__ )

       UpperCamelCase					=  []






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					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' )

       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(UpperCamelCase__ )

       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(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       if not return_dict:
          return (prev_sample,)

       return SchedulerOutput(prev_sample=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					torch.FloatTensor  ,						*UpperCamelCase__      :					Any  ,						**UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					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(UpperCamelCase__  ,						1E-8 )
       UpperCamelCase					=  next_alpha * pred + ets * next_sigma

       return prev_sample






    def __len__(  self      :					List[str] ):


       """simple docstring"""







       return self.config.num_train_timesteps






'''simple docstring'''


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

