"""
Custom GroundingDINO model class for transformers compatibility.
"""

import torch
import torch.nn as nn
from transformers import PreTrainedModel
from transformers.configuration_utils import PretrainedConfig


class GroundingDINOConfig(PretrainedConfig):
    """Configuration class for GroundingDINO."""
    
    model_type = "groundingdino"
    
    def __init__(
        self,
        num_classes=1180,
        num_queries=900,
        hidden_dim=256,
        num_feature_levels=4,
        nheads=8,
        enc_layers=6,
        dec_layers=6,
        dim_feedforward=2048,
        dropout=0.0,
        max_text_len=256,
        text_encoder_type="bert-base-uncased",
        backbone="swin_T_224_1k",
        position_embedding="sine",
        **kwargs
    ):
        super().__init__(**kwargs)
        self.num_classes = num_classes
        self.num_queries = num_queries
        self.hidden_dim = hidden_dim
        self.num_feature_levels = num_feature_levels
        self.nheads = nheads
        self.enc_layers = enc_layers
        self.dec_layers = dec_layers
        self.dim_feedforward = dim_feedforward
        self.dropout = dropout
        self.max_text_len = max_text_len
        self.text_encoder_type = text_encoder_type
        self.backbone = backbone
        self.position_embedding = position_embedding


class GroundingDINOModel(PreTrainedModel):
    """GroundingDINO model for transformers."""
    
    config_class = GroundingDINOConfig
    
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        
        # This is a placeholder - in practice, you would load the actual model architecture
        # For now, we'll create a simple wrapper
        self.model = None
    
    def forward(self, images, text_prompts=None, return_dict=True):
        """
        Forward pass of the model.
        
        Args:
            images: Input images tensor
            text_prompts: Text prompts for grounding
            return_dict: Whether to return a dictionary
            
        Returns:
            Model outputs
        """
        if self.model is None:
            raise NotImplementedError(
                "Model architecture not implemented. "
                "Please use the original GroundingDINO implementation for inference."
            )
        
        outputs = self.model(images, captions=text_prompts)
        
        if return_dict:
            return {
                "logits": outputs.get("pred_logits", torch.tensor([])),
                "boxes": outputs.get("pred_boxes", torch.tensor([])),
                "last_hidden_state": outputs.get("last_hidden_state", torch.tensor([]))
            }
        else:
            return outputs