


'''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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]      )
										__lowerCAmelCase							       =     get_activation("gelu"      )
										self.assertTrue(torch.allclose(gelu_python(__a      )       ,  torch_builtin(__a      )      )      )
										self.assertFalse(torch.allclose(gelu_python(__a      )       ,  gelu_new(__a      )      )      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]      )
										__lowerCAmelCase							       =     get_activation("gelu"      )
										__lowerCAmelCase							       =     get_activation("gelu_10"      )

										__lowerCAmelCase							       =     torch_builtin(__a      )
										__lowerCAmelCase							       =     geluaa(__a      )

										__lowerCAmelCase							       =     torch.where(y_gelu_aa < 1_0.0       ,  1       ,  0      )

										self.assertTrue(torch.max(__a      ).item() == 1_0.0      )
										self.assertTrue(torch.allclose(y_gelu * clipped_mask       ,  y_gelu_aa * clipped_mask      )      )

							def      snake_case							(	self      ):
										get_activation("gelu"      )
										get_activation("gelu_10"      )
										get_activation("gelu_fast"      )
										get_activation("gelu_new"      )
										get_activation("gelu_python"      )
										get_activation("gelu_pytorch_tanh"      )
										get_activation("linear"      )
										get_activation("mish"      )
										get_activation("quick_gelu"      )
										get_activation("relu"      )
										get_activation("sigmoid"      )
										get_activation("silu"      )
										get_activation("swish"      )
										get_activation("tanh"      )
										with self.assertRaises(__a      ):
													get_activation("bogus"      )
										with self.assertRaises(__a      ):
													get_activation(__a      )



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     get_activation("gelu"      )
										__lowerCAmelCase							       =     1
										__lowerCAmelCase							       =     get_activation("gelu"      )
										self.assertEqual(acta.a       ,  1      )
										with self.assertRaises(__a      ):
													__lowerCAmelCase							       =     acta.a




