
import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_A   =			logging.get_logger(__name__)

_A   =			{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}







class 					lowerCamelCase		(  _a  ):
					UpperCAmelCase__    :							Optional[Any]									=		"""conditional_detr"""
					UpperCAmelCase__    :							str									=		["""past_key_values"""]
					UpperCAmelCase__    :							List[Any]									=		{
					    """hidden_size""": """d_model""",
					    """num_attention_heads""": """encoder_attention_heads""",
					}





					def __init__(self				:   List[Any]    ,   _A				:   Tuple=True    ,   _A				:   List[str]=None    ,   _A				:   str=3    ,   _A				:   List[Any]=3_0_0    ,   _A				:   Union[str, Any]=6    ,   _A				:   Optional[int]=2_0_4_8    ,   _A				:   Tuple=8    ,   _A				:   Dict=6    ,   _A				:   Dict=2_0_4_8    ,   _A				:   Union[str, Any]=8    ,   _A				:   Dict=0.0    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[Any]=True    ,   _A				:   List[Any]="relu"    ,   _A				:   Dict=2_5_6    ,   _A				:   Tuple=0.1    ,   _A				:   Tuple=0.0    ,   _A				:   Any=0.0    ,   _A				:   List[Any]=0.02    ,   _A				:   Any=1.0    ,   _A				:   str=False    ,   _A				:   Dict="sine"    ,   _A				:   int="resnet50"    ,   _A				:   Any=True    ,   _A				:   Any=False    ,   _A				:   Dict=2    ,   _A				:   Tuple=5    ,   _A				:   Any=2    ,   _A				:   List[str]=1    ,   _A				:   Dict=1    ,   _A				:   Dict=2    ,   _A				:   Optional[int]=5    ,   _A				:   int=2    ,   _A				:   Optional[int]=0.25    ,   **_A				:   Tuple    ,   )						->						Any:
									if backbone_config is not None and use_timm_backbone:
													raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."       )

									if not use_timm_backbone:
													if backbone_config is None:
																	logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
																	snake_case								= CONFIG_MAPPING["""resnet"""](out_features=["stage4"]       )
													elif isinstance(__lowerCamelCase    ,   __lowerCamelCase       ):
																	snake_case								= backbone_config.get("model_type"       )
																	snake_case								= CONFIG_MAPPING[backbone_model_type]
																	snake_case								= config_class.from_dict(__lowerCamelCase       )

									snake_case								= use_timm_backbone
									snake_case								= backbone_config
									snake_case								= num_channels
									snake_case								= num_queries
									snake_case								= d_model
									snake_case								= encoder_ffn_dim
									snake_case								= encoder_layers
									snake_case								= encoder_attention_heads
									snake_case								= decoder_ffn_dim
									snake_case								= decoder_layers
									snake_case								= decoder_attention_heads
									snake_case								= dropout
									snake_case								= attention_dropout
									snake_case								= activation_dropout
									snake_case								= activation_function
									snake_case								= init_std
									snake_case								= init_xavier_std
									snake_case								= encoder_layerdrop
									snake_case								= decoder_layerdrop
									snake_case								= encoder_layers
									snake_case								= auxiliary_loss
									snake_case								= position_embedding_type
									snake_case								= backbone
									snake_case								= use_pretrained_backbone
									snake_case								= dilation
									# Hungarian matcher
									snake_case								= class_cost
									snake_case								= bbox_cost
									snake_case								= giou_cost
									# Loss coefficients
									snake_case								= mask_loss_coefficient
									snake_case								= dice_loss_coefficient
									snake_case								= cls_loss_coefficient
									snake_case								= bbox_loss_coefficient
									snake_case								= giou_loss_coefficient
									snake_case								= focal_alpha
									super().__init__(is_encoder_decoder=__lowerCamelCase    ,   **__lowerCamelCase       )





					@property
					def 		UpperCAmelCase(self				:   Optional[Any]       )						->						int:
									return self.encoder_attention_heads





					@property
					def 		UpperCAmelCase(self				:   Optional[int]       )						->						Optional[int]:
									return self.d_model





					def 		UpperCAmelCase(self				:   str       )						->						Any:
									snake_case								= copy.deepcopy(self.__dict__       )
									if self.backbone_config is not None:
													snake_case								= self.backbone_config.to_dict()
									snake_case								= self.__class__.model_type
									return output







class 					lowerCamelCase		(  _a  ):
					UpperCAmelCase__    :							Optional[int]									=		version.parse("1.11"  )





					@property
					def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Union[str, Any]:
									return OrderedDict(
									    [
									        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
									        ("pixel_mask", {0: "batch"}),
									    ]       )





					@property
					def 		UpperCAmelCase(self				:   Any       )						->						Tuple:
									return 1E-5





					@property
					def 		UpperCAmelCase(self				:   Any       )						->						str:
									return 1_2



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,
)

