





'''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,
)









'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 				a_       (    _lowerCAmelCase							):
     __A								=      (UniPCMultistepScheduler,)
     __A								=      (("num_inference_steps", 25),)





     def 							lowercase__				(			self		: Tuple     ,				**lowercase		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Any         =			{
            "num_train_timesteps": 1_000,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "solver_order": 2,
            "solver_type": "bh2",
        }

        config.update(**lowercase						)
        return config





     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: str=0     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			dict(self.forward_default_kwargs						)
        lowercase_      :Tuple         =			kwargs.pop("num_inference_steps"     ,				lowercase						)
        lowercase_      :Union[str, Any]         =			self.dummy_sample
        lowercase_      :Union[str, Any]         =			0.1 * sample
        lowercase_      :Any         =			[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Tuple         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Tuple         =			scheduler_class(**lowercase						)
           scheduler.set_timesteps(lowercase						)
           # copy over dummy past residuals
           lowercase_      :str         =			dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(lowercase						)
              lowercase_      :str         =			scheduler_class.from_pretrained(lowercase						)
              new_scheduler.set_timesteps(lowercase						)
              # copy over dummy past residuals
              lowercase_      :List[Any]         =			dummy_past_residuals[: new_scheduler.config.solver_order]

           lowercase_      ,							lowercase_      :Tuple         =			sample, sample
           for t in range(lowercase     ,				time_step + scheduler.config.solver_order + 1						):
              lowercase_      :Optional[int]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
              lowercase_      :Union[str, Any]         =			new_scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

              assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"





     def 							lowercase__				(			self		: str     ,				lowercase		: Any=0     ,				**lowercase		: str						):





        """simple docstring"""




        lowercase_      :Tuple         =			dict(self.forward_default_kwargs						)
        lowercase_      :Dict         =			kwargs.pop("num_inference_steps"     ,				lowercase						)
        lowercase_      :str         =			self.dummy_sample
        lowercase_      :Optional[int]         =			0.1 * sample
        lowercase_      :Optional[int]         =			[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Any         =			self.get_scheduler_config()
           lowercase_      :Tuple         =			scheduler_class(**lowercase						)
           scheduler.set_timesteps(lowercase						)

           # copy over dummy past residuals (must be after setting timesteps)
           lowercase_      :str         =			dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(lowercase						)
              lowercase_      :int         =			scheduler_class.from_pretrained(lowercase						)
              # copy over dummy past residuals
              new_scheduler.set_timesteps(lowercase						)

              # copy over dummy past residual (must be after setting timesteps)
              lowercase_      :Optional[Any]         =			dummy_past_residuals[: new_scheduler.config.solver_order]

           lowercase_      :Dict         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
           lowercase_      :str         =			new_scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: Optional[int]=None     ,				**lowercase		: int						):





        """simple docstring"""




        if scheduler is None:
           lowercase_      :int         =			self.scheduler_classes[0]
           lowercase_      :Optional[Any]         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Optional[Any]         =			scheduler_class(**lowercase						)

        lowercase_      :str         =			self.scheduler_classes[0]
        lowercase_      :Optional[int]         =			self.get_scheduler_config(**lowercase						)
        lowercase_      :List[Any]         =			scheduler_class(**lowercase						)

        lowercase_      :str         =			10
        lowercase_      :List[str]         =			self.dummy_model()
        lowercase_      :Optional[int]         =			self.dummy_sample_deter
        scheduler.set_timesteps(lowercase						)

        for i, t in enumerate(scheduler.timesteps						):
           lowercase_      :int         =			model(lowercase     ,				lowercase						)
           lowercase_      :Dict         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase						).prev_sample

        return sample





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :str         =			dict(self.forward_default_kwargs						)

        lowercase_      :Union[str, Any]         =			kwargs.pop("num_inference_steps"     ,				lowercase						)

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Optional[Any]         =			self.get_scheduler_config()
           lowercase_      :Optional[int]         =			scheduler_class(**lowercase						)

           lowercase_      :Union[str, Any]         =			self.dummy_sample
           lowercase_      :str         =			0.1 * sample

           if num_inference_steps is not None and hasattr(lowercase     ,				"set_timesteps"						):
              scheduler.set_timesteps(lowercase						)
           elif num_inference_steps is not None and not hasattr(lowercase     ,				"set_timesteps"						):
              lowercase_      :str         =			num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowercase_      :Dict         =			[residual + 0.2, residual + 0.15, residual + 0.10]
           lowercase_      :Any         =			dummy_past_residuals[: scheduler.config.solver_order]

           lowercase_      :Optional[int]         =			scheduler.timesteps[5]
           lowercase_      :List[Any]         =			scheduler.timesteps[6]

           lowercase_      :Optional[Any]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
           lowercase_      :List[str]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

           self.assertEqual(output_a.shape     ,				sample.shape						)
           self.assertEqual(output_a.shape     ,				output_a.shape						)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			UniPCMultistepScheduler(**self.get_scheduler_config()						)
        lowercase_      :Dict         =			self.full_loop(scheduler=lowercase						)
        lowercase_      :List[Any]         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3

        lowercase_      :List[str]         =			DPMSolverSinglestepScheduler.from_config(scheduler.config						)
        lowercase_      :Tuple         =			DEISMultistepScheduler.from_config(scheduler.config						)
        lowercase_      :Optional[int]         =			DPMSolverMultistepScheduler.from_config(scheduler.config						)
        lowercase_      :Tuple         =			UniPCMultistepScheduler.from_config(scheduler.config						)

        lowercase_      :Tuple         =			self.full_loop(scheduler=lowercase						)
        lowercase_      :Tuple         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=lowercase						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        self.check_over_configs(thresholding=lowercase						)
        for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        thresholding=lowercase     ,				prediction_type=lowercase     ,				sample_max_value=lowercase     ,				solver_order=lowercase     ,				solver_type=lowercase     ,				)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=lowercase						)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=lowercase     ,				solver_type=lowercase     ,				prediction_type=lowercase     ,				)
                 lowercase_      :Tuple         =			self.full_loop(
                     solver_order=lowercase     ,				solver_type=lowercase     ,				prediction_type=lowercase     ,				)
                 assert not torch.isnan(lowercase						).any(), "Samples have nan numbers"





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        self.check_over_configs(lower_order_final=lowercase						)
        self.check_over_configs(lower_order_final=lowercase						)





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=lowercase     ,				time_step=0						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :List[str]         =			self.full_loop()
        lowercase_      :List[str]         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :Dict         =			self.full_loop(prediction_type="v_prediction"						)
        lowercase_      :Any         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.10_14						) < 1e-3





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.scheduler_classes[0]
        lowercase_      :str         =			self.get_scheduler_config(thresholding=lowercase     ,				dynamic_thresholding_ratio=0						)
        lowercase_      :Tuple         =			scheduler_class(**lowercase						)

        lowercase_      :Optional[int]         =			10
        lowercase_      :Optional[int]         =			self.dummy_model()
        lowercase_      :Any         =			self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase						)

        for i, t in enumerate(scheduler.timesteps						):
           lowercase_      :Tuple         =			model(lowercase     ,				lowercase						)
           lowercase_      :Any         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase						).prev_sample

        assert sample.dtype == torch.floataa







     def 							lowercase__				(			self		: List[Any]     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        for scheduler_class in self.scheduler_classes:
           lowercase_      :Optional[int]         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Dict         =			scheduler_class(**lowercase						)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
           assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps


