


'''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 torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    (DDIMParallelScheduler,)
		_a   			=    (('eta', 0.0), ('num_inference_steps', 50))




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Optional[Any]							 =			self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			10, 0.0

							UpperCamelCase__       :List[str]							 =			self.dummy_model()
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCamelCase_							)

							for t in scheduler.timesteps:
												UpperCamelCase__       :Optional[int]							 =			model(UpperCamelCase_   ,       UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							).prev_sample

							return sample




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timesteps in [100, 500, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config(steps_offset=1							)
							UpperCamelCase__       :str							 =			scheduler_class(**UpperCamelCase_							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps   ,       torch.LongTensor([801, 601, 401, 201, 1]							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]							):
												self.check_over_configs(beta_start=UpperCamelCase_   ,       beta_end=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.check_over_configs(thresholding=UpperCamelCase_							)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=UpperCamelCase_   ,       prediction_type=UpperCamelCase_   ,       sample_max_value=UpperCamelCase_   ,       )




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t in [1, 10, 49]:
												self.check_over_forward(time_step=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 10, 50]   ,       [10, 50, 500]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       num_inference_steps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, eta in zip([1, 10, 49]   ,       [0.0, 0.5, 1.0]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       eta=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config()
							UpperCamelCase__       :int							 =			scheduler_class(**UpperCamelCase_							)

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400							) - 0.14771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960							) - 0.32460							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ,       486							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998							) - 0.02							)							) < 1e-5




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Dict							 =			self.get_scheduler_config()
							UpperCamelCase__       :Any							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			10, 0.0
							scheduler.set_timesteps(UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			self.dummy_model()
							UpperCamelCase__       :List[Any]							 =			self.dummy_sample_deter
							UpperCamelCase__       :int							 =			self.dummy_sample_deter + 0.1
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter - 0.1

							UpperCamelCase__       :str							 =			samplea.shape[0]
							UpperCamelCase__       :Optional[Any]							 =			torch.stack([samplea, samplea, samplea]   ,       dim=0							)
							UpperCamelCase__       :List[Any]							 =			torch.arange(UpperCamelCase_							)[0:3, None].repeat(1   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(samples.flatten(0   ,       1							)   ,       timesteps.flatten(0   ,       1							)							)
							UpperCamelCase__       :Any							 =			scheduler.batch_step_no_noise(UpperCamelCase_   ,       timesteps.flatten(0   ,       1							)   ,       samples.flatten(0   ,       1							)   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 1147.7904							) < 1e-2
							assert abs(result_mean.item() - 0.4982							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.full_loop()

							UpperCamelCase__       :List[str]							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 172.0067							) < 1e-2
							assert abs(result_mean.item() - 0.223967							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.full_loop(prediction_type='''v_prediction'''							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :str							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 52.5302							) < 1e-2
							assert abs(result_mean.item() - 0.0684							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :str							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.8295							) < 1e-2
							assert abs(result_mean.item() - 0.1951							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :List[str]							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.0784							) < 1e-2
							assert abs(result_mean.item() - 0.1941							) < 1e-3