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 ddlmZ dd	lmZ d
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      ZddgZy)zDETR model configuration    OrderedDict)Mapping)version   )PretrainedConfig)
OnnxConfig)logging) verify_backbone_config_arguments   )CONFIG_MAPPINGc                        e Zd ZdZdZdgZdddZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Zede	fd	       Z
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DetrConfigax  
    This is the configuration class to store the configuration of a [`DetrModel`]. It is used to instantiate a DETR
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the DETR
    [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        use_timm_backbone (`bool`, *optional*, defaults to `True`):
            Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
            API.
        backbone_config (`PretrainedConfig` or `dict`, *optional*):
            The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
            case it will default to `ResNetConfig()`.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        num_queries (`int`, *optional*, defaults to 100):
            Number of object queries, i.e. detection slots. This is the maximal number of objects [`DetrModel`] can
            detect in a single image. For COCO, we recommend 100 queries.
        d_model (`int`, *optional*, defaults to 256):
            Dimension of the layers.
        encoder_layers (`int`, *optional*, defaults to 6):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 6):
            Number of decoder layers.
        encoder_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 2048):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 2048):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        init_xavier_std (`float`, *optional*, defaults to 1):
            The scaling factor used for the Xavier initialization gain in the HM Attention map module.
        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        auxiliary_loss (`bool`, *optional*, defaults to `False`):
            Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
        position_embedding_type (`str`, *optional*, defaults to `"sine"`):
            Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
        backbone (`str`, *optional*, defaults to `"resnet50"`):
            Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
            will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
            is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
        use_pretrained_backbone (`bool`, *optional*, `True`):
            Whether to use pretrained weights for the backbone.
        backbone_kwargs (`dict`, *optional*):
            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
        dilation (`bool`, *optional*, defaults to `False`):
            Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
            `use_timm_backbone` = `True`.
        class_cost (`float`, *optional*, defaults to 1):
            Relative weight of the classification error in the Hungarian matching cost.
        bbox_cost (`float`, *optional*, defaults to 5):
            Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
        giou_cost (`float`, *optional*, defaults to 2):
            Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
        mask_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the Focal loss in the panoptic segmentation loss.
        dice_loss_coefficient (`float`, *optional*, defaults to 1):
            Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
        bbox_loss_coefficient (`float`, *optional*, defaults to 5):
            Relative weight of the L1 bounding box loss in the object detection loss.
        giou_loss_coefficient (`float`, *optional*, defaults to 2):
            Relative weight of the generalized IoU loss in the object detection loss.
        eos_coefficient (`float`, *optional*, defaults to 0.1):
            Relative classification weight of the 'no-object' class in the object detection loss.

    Examples:

    ```python
    >>> from transformers import DetrConfig, DetrModel

    >>> # Initializing a DETR facebook/detr-resnet-50 style configuration
    >>> configuration = DetrConfig()

    >>> # Initializing a model (with random weights) from the facebook/detr-resnet-50 style configuration
    >>> model = DetrModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```detrpast_key_valuesd_modelencoder_attention_heads)hidden_sizenum_attention_headsc#                 2   |r|i }|rd|d<   g d|d<   ||d<   nm|sk|dv rg|&t         j                  d       t        d   d	g
      }n;t        |t              r+|j                  d      }$t        |$   }%|%j                  |      }d }d }t        |||||       || _        || _	        || _
        || _        || _        || _        || _        || _        |	| _        || _        |
| _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _         || _!        || _"        || _#        || _$        || _%        || _&        | | _'        |!| _(        |"| _)        tU        &|   dd|i|# y )N   output_stride)   r   r      out_indicesin_chans)Nresnet50zX`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.resnetstage4)out_features
model_type)use_timm_backboneuse_pretrained_backbonebackbonebackbone_configbackbone_kwargsis_encoder_decoder ),loggerinfor   
isinstancedictget	from_dictr   r"   r%   num_channelsnum_queriesr   encoder_ffn_dimencoder_layersr   decoder_ffn_dimdecoder_layersdecoder_attention_headsdropoutattention_dropoutactivation_dropoutactivation_functioninit_stdinit_xavier_stdencoder_layerdropdecoder_layerdropnum_hidden_layersauxiliary_lossposition_embedding_typer$   r#   r&   dilation
class_cost	bbox_cost	giou_costmask_loss_coefficientdice_loss_coefficientbbox_loss_coefficientgiou_loss_coefficienteos_coefficientsuper__init__)'selfr"   r%   r/   r0   r2   r1   r   r4   r3   r5   r<   r=   r'   r9   r   r6   r7   r8   r:   r;   r?   r@   r$   r#   r&   rA   rB   rC   rD   rE   rF   rG   rH   rI   kwargsbackbone_model_typeconfig_class	__class__s'                                         ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/detr/configuration_detr.pyrK   zDetrConfig.__init__   s   P !8 O350-9OM**6OJ'"x3E'E&vw"0":
"SOT2&5&9&9,&G#-.AB"."8"8"IHH(/$;++	
 "3.(&.,'>$.,'>$!2"4#6  .!2!2!/,'>$ '>$. $""%:"%:"%:"%:".I,>I&I    returnc                     | j                   S N)r   rL   s    rQ   r   zDetrConfig.num_attention_heads   s    +++rR   c                     | j                   S rU   )r   rV   s    rQ   r   zDetrConfig.hidden_size   s    ||rR   r%   c                      | dd|i|S )a-  Instantiate a [`DetrConfig`] (or a derived class) from a pre-trained backbone model configuration.

        Args:
            backbone_config ([`PretrainedConfig`]):
                The backbone configuration.
        Returns:
            [`DetrConfig`]: An instance of a configuration object
        r%   r(   r(   )clsr%   rM   s      rQ   from_backbone_configzDetrConfig.from_backbone_config   s     =?=f==rR   )"TNr   d            r\   r]   r^           r_   Trelu   皙?r_   r_   g{Gz?g      ?Fsiner   TNFr      r   r   r   rd   r   rb   )__name__
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edefd       Zede	fd       Zy)DetrOnnxConfigz1.11rS   c                 2    t        ddddddfdddifg      S )	Npixel_valuesbatchr/   heightwidth)r   r   r   r   
pixel_maskr   r   rV   s    rQ   inputszDetrOnnxConfig.inputs  s2    WHQX!YZ7|,
 	
rR   c                      y)Ngh㈵>r(   rV   s    rQ   atol_for_validationz"DetrOnnxConfig.atol_for_validation  s    rR   c                      y)N   r(   rV   s    rQ   default_onnx_opsetz!DetrOnnxConfig.default_onnx_opset  s    rR   N)re   rf   rg   r   parsetorch_onnx_minimum_versionrk   r   strrl   rw   floatry   r|   r(   rR   rQ   rp   rp     su    !.v!6
WS#X%6 67 
 
 U   C  rR   rp   N)rh   collectionsr   typingr   	packagingr   configuration_utilsr   onnxr	   utilsr
   utils.backbone_utilsr   autor   
get_loggerre   r)   r   rp   __all__r(   rR   rQ   <module>r      s_     #   3   D ! 
		H	%i>! i>XZ * )
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