^jqfddlmZddlmZddlmZedeGddeZdgZy ) )strict)PreTrainedConfig)auto_docstringz$tue-mps/coco_panoptic_eomt_large_640) checkpointceZdZUdZdZdZeed<dZeed<dZ eed<d Z e ed <d Z e ezed <d Ze ed<dZe ed<dZeeezeeefzed<dZeeezeeefzed<dZeed<dZeed<dZe ed<d Ze ezed<dZeed<d Ze ezed<dZeed<dZeed <d!Ze ed"<d#Ze ed$<d%Ze ed&<d%Z e ed'<d(Z!eed)<d*Z"e ed+<d,Z#e ed-<d.Z$eed/<dZ%eed0<y1)2 EomtConfiga layerscale_value (`float`, *optional*, defaults to 1.0): Initial value for the LayerScale parameter. num_upscale_blocks (`int`, *optional*, defaults to 2): Number of upsampling blocks used in the decoder or segmentation head. use_swiglu_ffn (`bool`, *optional*, defaults to `False`): Whether to use the SwiGLU feedforward neural network. num_blocks (`int`, *optional*, defaults to 4): Number of feature blocks or stages in the architecture. no_object_weight (`float`, *optional*, defaults to 0.1): Loss weight for the 'no object' class in panoptic/instance segmentation. class_weight (`float`, *optional*, defaults to 2.0): Loss weight for classification targets. mask_weight (`float`, *optional*, defaults to 5.0): Loss weight for mask prediction. train_num_points (`int`, *optional*, defaults to 12544): Number of points to sample for mask loss computation during training. oversample_ratio (`float`, *optional*, defaults to 3.0): Oversampling ratio used in point sampling for mask training. importance_sample_ratio (`float`, *optional*, defaults to 0.75): Ratio of points to sample based on importance during training. num_queries (`int`, *optional*, defaults to 200): Number of object queries in the Transformer. num_register_tokens (`int`, *optional*, defaults to 4): Number of learnable register tokens added to the transformer input. Example: ```python >>> from transformers import EomtConfig, EomtForUniversalSegmentation >>> # Initialize configuration >>> config = EomtConfig() >>> # Initialize model >>> model = EomtForUniversalSegmentation(config) >>> # Access config >>> config = model.config ```eomti hidden_sizenum_hidden_layersnum_attention_headsgelu hidden_actghidden_dropout_probg{Gz?initializer_rangegư>layer_norm_epsi image_size patch_sizer num_channels mlp_ratiog?layerscale_valuedrop_path_ratenum_upscale_blocksattention_dropoutFuse_swiglu_ffn num_blocksg?no_object_weightg@ class_weightg@ mask_weight dice_weighti1train_num_pointsg@oversample_ratiog?importance_sample_ratio num_queriesnum_register_tokensN)&__name__ __module__ __qualname____doc__ model_typer int__annotations__r rrstrrfloatrrrlisttuplerrrrrrrrboolr r!r"r#r$r%r&r'r)r*v/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/eomt/configuration_eomt.pyr r sU' RJKs!!J'**#u# NE 47Jd3i%S/1746Jd3i%S/16L#Is!e!"%NECK%%(us{( ND J!e!L%KK!c!!e!%)U)K  r8r N)huggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__r7r8r9r>sH(/3#ABE!!E!CE!P .r8