




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,
)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[str] =				"xlm"
    __lowerCamelCase      :			Tuple =				{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }






    def __init__(	self,		lowerCamelCase__=3_0145,		lowerCamelCase__=2048,		lowerCamelCase__=12,		lowerCamelCase__=16,		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=1,		lowerCamelCase__=True,		lowerCamelCase__=512,		lowerCamelCase__=2048**-0.5,		lowerCamelCase__=1e-12,		lowerCamelCase__=0.02,		lowerCamelCase__=0,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=5,		lowerCamelCase__=True,		lowerCamelCase__="first",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=0.1,		lowerCamelCase__=5,		lowerCamelCase__=5,		lowerCamelCase__=0,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=0,		**lowerCamelCase__,		):
        A			:	Dict										=       vocab_size
        A			:	int										=       emb_dim
        A			:	str										=       n_layers
        A			:	Union[str, Any]										=       n_heads
        A			:	Optional[int]										=       dropout
        A			:	Union[str, Any]										=       attention_dropout
        A			:	Optional[Any]										=       gelu_activation
        A			:	Dict										=       sinusoidal_embeddings
        A			:	int										=       causal
        A			:	Optional[Any]										=       asm
        A			:	Any										=       n_langs
        A			:	List[str]										=       use_lang_emb
        A			:	Union[str, Any]										=       layer_norm_eps
        A			:	str										=       bos_index
        A			:	int										=       eos_index
        A			:	Tuple										=       pad_index
        A			:	str										=       unk_index
        A			:	Optional[Any]										=       mask_index
        A			:	Union[str, Any]										=       is_encoder
        A			:	Tuple										=       max_position_embeddings
        A			:	List[str]										=       embed_init_std
        A			:	Tuple										=       init_std
        A			:	Tuple										=       summary_type
        A			:	int										=       summary_use_proj
        A			:	List[Any]										=       summary_activation
        A			:	Optional[Any]										=       summary_proj_to_labels
        A			:	Optional[Any]										=       summary_first_dropout
        A			:	Optional[int]										=       start_n_top
        A			:	Optional[Any]										=       end_n_top
        A			:	List[str]										=       mask_token_id
        A			:	Tuple										=       lang_id

        if "n_words" in kwargs:
            A			:	List[str]										=       kwargs["""n_words"""]

        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		**lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Any										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	Union[str, Any]										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]		)


