app/utils/model_loader.py

import torch
import torch.nn as nn
from torch.nn import GroupNorm, LayerNorm
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import timm

class ViTWrapper(nn.Module):
    """Wrapper to make ViT compatible with feature extraction for ImageTagger"""
    def __init__(self, vit_model):
        super().__init__()
        self.vit = vit_model
        self.out_indices = (-1,)  # mimic timm.features_only
        
        # Get patch size and embedding dim from the model
        self.patch_size = vit_model.patch_embed.patch_size[0]
        self.embed_dim = vit_model.embed_dim
        
    def forward(self, x):
        B = x.size(0)

        # ➊ patch tokens
        x = self.vit.patch_embed(x)                       # (B, N, C)

        # ➋ prepend CLS
        cls_tok = self.vit.cls_token.expand(B, -1, -1)    # (B, 1, C)
        x = torch.cat((cls_tok, x), dim=1)                # (B, 1+N, C)

        # ➌ add positional encodings (full, incl. CLS)
        if self.vit.pos_embed is not None:
            x = x + self.vit.pos_embed[:, : x.size(1), :]

        x = self.vit.pos_drop(x)

        for blk in self.vit.blocks:
            x = blk(x)

        x = self.vit.norm(x)                              # (B, 1+N, C)

        # ➍ split back out
        cls_final   = x[:, 0]              # (B, C)
        patch_tokens = x[:, 1:]            # (B, N, C)

        # ➎ reshape patches to (B, C, H, W)
        B, N, C = patch_tokens.shape
        h = w = int(N ** 0.5)              # square assumption
        patch_features = patch_tokens.permute(0, 2, 1).reshape(B, C, h, w)

        # Return **both**: (patch map, CLS)
        return patch_features, cls_final
    
    def set_grad_checkpointing(self, enable=True):
        """Enable gradient checkpointing if supported"""
        if hasattr(self.vit, 'set_grad_checkpointing'):
            self.vit.set_grad_checkpointing(enable)
            return True
        return False

class ImageTagger(nn.Module):
    """
    ImageTagger with Vision Transformer backbone
    """
    def __init__(self, total_tags, dataset, model_name='vit_base_patch16_224', 
                 num_heads=16, dropout=0.1, pretrained=True, tag_context_size=256,
                 use_gradient_checkpointing=False, img_size=224):
        super().__init__()
        
        # Store checkpointing config
        self.use_gradient_checkpointing = use_gradient_checkpointing
        self.model_name = model_name
        self.img_size = img_size
        
        # Debug and stats flags
        self._flags = {
            'debug': False,
            'model_stats': True
        }
        
        # Core model config
        self.dataset = dataset
        self.tag_context_size = tag_context_size
        self.total_tags = total_tags
        
        print(f"🏗️ Building ImageTagger with ViT backbone and {total_tags} tags")
        print(f"   Backbone: {model_name}")
        print(f"   Image size: {img_size}x{img_size}")
        print(f"   Tag context size: {tag_context_size}")
        print(f"   Gradient checkpointing: {use_gradient_checkpointing}")
        print(f"   🎯 Custom embeddings, PyTorch native attention, no ground truth inclusion")
        
        # 1. Vision Transformer Backbone
        print("📦 Loading Vision Transformer backbone...")
        self._load_vit_backbone()
        
        # Get backbone dimensions by running a test forward pass
        self._determine_backbone_dimensions()
        
        self.embedding_dim = self.backbone.embed_dim

        # 2. Custom Tag Embeddings (no CLIP)
        print("🎯 Using custom tag embeddings (no CLIP)")
        self.tag_embedding = nn.Embedding(total_tags, self.embedding_dim)
        
        # 3. Shared weights approach - tag bias for initial predictions
        print("🔗 Using shared weights between initial head and tag embeddings")
        self.tag_bias = nn.Parameter(torch.zeros(total_tags))

     
        # 4. Image token extraction (for attention AND global pooling)
        self.image_token_proj = nn.Identity()
        
        # 5. Tags-as-queries cross-attention (using PyTorch's optimized implementation)
        self.cross_attention = nn.MultiheadAttention(
            embed_dim=self.embedding_dim, 
            num_heads=num_heads, 
            dropout=dropout,
            batch_first=True  # Use (batch, seq, feature) format
        )
        self.cross_norm = nn.LayerNorm(self.embedding_dim)
        
        # Initialize weights
        self._init_weights()
        
        # Enable gradient checkpointing
        if self.use_gradient_checkpointing:
            self._enable_gradient_checkpointing()
        
        print(f"✅ ImageTagger with ViT initialized!")
        self._print_parameter_count()
    
    def _load_vit_backbone(self):
        """Load Vision Transformer model from timm"""
        print(f"   Loading from timm: {self.model_name}")
        
        # Load the ViT model (not features_only, we want the full model for token extraction)
        vit_model = timm.create_model(
            self.model_name, 
            pretrained=True,
            img_size=self.img_size,
            num_classes=0  # Remove classification head
        )
        
        # Wrap it in our compatibility layer
        self.backbone = ViTWrapper(vit_model)
        
        print(f"   ✅ ViT loaded successfully")
        print(f"   Patch size: {self.backbone.patch_size}x{self.backbone.patch_size}")
        print(f"   Embed dim: {self.backbone.embed_dim}")
    
    def _determine_backbone_dimensions(self):
        """Determine backbone output dimensions"""
        print("   🔍 Determining backbone dimensions...")
        
        with torch.no_grad(), torch.autocast('cuda', dtype=torch.bfloat16):
            # Create a dummy input 
            dummy_input = torch.randn(1, 3, self.img_size, self.img_size)
            
            # Get features
            backbone_features, cls_dummy = self.backbone(dummy_input)
            feature_tensor = backbone_features
            
            self.backbone_dim = feature_tensor.shape[1]
            self.feature_map_size = feature_tensor.shape[2]
            
        print(f"   Backbone output: {self.backbone_dim}D, {self.feature_map_size}x{self.feature_map_size} spatial")
        print(f"   Total patch tokens: {self.feature_map_size * self.feature_map_size}")
    
    def _enable_gradient_checkpointing(self):
        """Enable gradient checkpointing for memory efficiency"""
        print("🔄 Enabling gradient checkpointing...")
        
        # Enable checkpointing for ViT backbone
        if self.backbone.set_grad_checkpointing(True):
            print("   ✅ ViT backbone checkpointing enabled")
        else:
            print("   ⚠️ ViT backbone doesn't support built-in checkpointing, will checkpoint manually")
    
    def _checkpoint_backbone(self, x):
        """Wrapper for backbone with gradient checkpointing"""
        if self.use_gradient_checkpointing and self.training:
            return checkpoint.checkpoint(self.backbone, x, use_reentrant=False)
        else:
            return self.backbone(x)
    
    def _checkpoint_image_proj(self, x):
        """Wrapper for image projection with gradient checkpointing"""
        if self.use_gradient_checkpointing and self.training:
            return checkpoint.checkpoint(self.image_token_proj, x, use_reentrant=False)
        else:
            return self.image_token_proj(x)
    
    def _checkpoint_cross_attention(self, query, key, value):
        """Wrapper for cross attention with gradient checkpointing"""
        def _attention_forward(q, k, v):
            attended_features, _ = self.cross_attention(query=q, key=k, value=v)
            return self.cross_norm(attended_features)
        
        if self.use_gradient_checkpointing and self.training:
            return checkpoint.checkpoint(_attention_forward, query, key, value, use_reentrant=False)
        else:
            return _attention_forward(query, key, value)
    
    def _checkpoint_candidate_selection(self, initial_logits):
        """Wrapper for candidate selection with gradient checkpointing"""
        def _candidate_forward(logits):
            return self._get_candidate_tags(logits)
        
        if self.use_gradient_checkpointing and self.training:
            return checkpoint.checkpoint(_candidate_forward, initial_logits, use_reentrant=False)
        else:
            return _candidate_forward(initial_logits)
    
    def _checkpoint_final_scoring(self, attended_features, candidate_indices):
        """Wrapper for final scoring with gradient checkpointing"""
        def _scoring_forward(features, indices):
            emb = self.tag_embedding(indices)
            # BF16 in, BF16 out
            return (features * emb).sum(dim=-1)
        
        if self.use_gradient_checkpointing and self.training:
            return checkpoint.checkpoint(_scoring_forward, attended_features, candidate_indices, use_reentrant=False)
        else:
            return _scoring_forward(attended_features, candidate_indices)
   
    def _init_weights(self):
        """Initialize weights for new modules"""
        def _init_layer(layer):
            if isinstance(layer, nn.Linear):
                nn.init.xavier_uniform_(layer.weight)
                if layer.bias is not None:
                    nn.init.zeros_(layer.bias)
            elif isinstance(layer, nn.Conv2d):
                nn.init.kaiming_normal_(layer.weight, mode='fan_out', nonlinearity='relu')
                if layer.bias is not None:
                    nn.init.zeros_(layer.bias)
            elif isinstance(layer, nn.Embedding):
                nn.init.normal_(layer.weight, mean=0, std=0.02)
        
        # Initialize new components
        self.image_token_proj.apply(_init_layer)
        
        # Initialize tag embeddings with normal distribution
        nn.init.normal_(self.tag_embedding.weight, mean=0, std=0.02)
        
        # Initialize tag bias
        nn.init.zeros_(self.tag_bias)
    
    def _print_parameter_count(self):
        """Print parameter statistics"""
        total_params = sum(p.numel() for p in self.parameters())
        trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
        backbone_params = sum(p.numel() for p in self.backbone.parameters())
        
        print(f"📊 Parameter Statistics:")
        print(f"   Total parameters: {total_params/1e6:.1f}M")
        print(f"   Trainable parameters: {trainable_params/1e6:.1f}M")
        print(f"   Frozen parameters: {(total_params-trainable_params)/1e6:.1f}M")
        print(f"   Backbone parameters: {backbone_params/1e6:.1f}M")
        
        if self.use_gradient_checkpointing:
            print(f"   🔄 Gradient checkpointing enabled for memory efficiency")
    
    @property
    def debug(self):
        return self._flags['debug']
    
    @property
    def model_stats(self):
        return self._flags['model_stats']
    
    def _get_candidate_tags(self, initial_logits, target_tags=None, hard_negatives=None):
        """Select candidate tags - no ground truth inclusion"""
        batch_size = initial_logits.size(0)
        
        # Simply select top K candidates based on initial predictions
        top_probs, top_indices = torch.topk(
            torch.sigmoid(initial_logits), 
            k=min(self.tag_context_size, self.total_tags),
            dim=1, largest=True, sorted=True
        )
        
        return top_indices
    
    def _analyze_predictions(self, predictions, tag_indices):
        """Analyze prediction patterns"""
        if not self.model_stats:
            return {}
        
        if torch._dynamo.is_compiling():
            return {}
        
        with torch.no_grad(), torch.autocast('cuda', dtype=torch.bfloat16):
            probs = torch.sigmoid(predictions)
            relevant_probs = torch.gather(probs, 1, tag_indices)
            
            return {
                'prediction_confidence': relevant_probs.mean().item(),
                'prediction_entropy': -(relevant_probs * torch.log(relevant_probs + 1e-9)).mean().item(),
                'high_confidence_ratio': (relevant_probs > 0.7).float().mean().item(),
                'above_threshold_ratio': (relevant_probs > 0.5).float().mean().item(),
            }
    
    def forward(self, x, targets=None, hard_negatives=None):
        """
        Forward pass with ViT backbone, CLS token support and gradient-checkpointing.
        All arithmetic tensors stay in the backbone’s dtype (BF16 under autocast,
        FP32 otherwise).  Anything that must mix dtypes is cast to match.
        """
        batch_size  = x.size(0)
        model_stats = {} if self.model_stats else {}

        # ------------------------------------------------------------------
        # 1. Backbone  →  patch map + CLS token
        # ------------------------------------------------------------------
        patch_map, cls_token = self._checkpoint_backbone(x)         # patch_map: [B, C, H, W]
                                                                    # cls_token: [B, C]

        # ------------------------------------------------------------------
        # 2. Tokens  →  global image vector
        # ------------------------------------------------------------------
        image_tokens_4d = self._checkpoint_image_proj(patch_map)    # [B, C, H, W]
        image_tokens    = image_tokens_4d.flatten(2).transpose(1, 2)  # [B, N, C]

        # “Dual-pool”: mean-pool patches ⊕ CLS
        global_features = 0.5 * (image_tokens.mean(dim=1, dtype=image_tokens.dtype) + cls_token)  # [B, C]

        compute_dtype = global_features.dtype                       # BF16 or FP32

        # ------------------------------------------------------------------
        # 3. Initial logits  (shared weights)
        # ------------------------------------------------------------------
        tag_weights = self.tag_embedding.weight.to(compute_dtype)   # [T, C]
        tag_bias    = self.tag_bias.to(compute_dtype)               # [T]

        initial_logits = global_features @ tag_weights.t() + tag_bias   # [B, T]
        initial_logits = initial_logits.to(compute_dtype)               # keep dtype uniform
        initial_preds  = initial_logits                                 # alias

        # ------------------------------------------------------------------
        # 4. Candidate set
        # ------------------------------------------------------------------
        candidate_indices = self._checkpoint_candidate_selection(initial_logits)  # [B, K]

        tag_embeddings   = self.tag_embedding(candidate_indices).to(compute_dtype)  # [B, K, C]

        attended_features = self._checkpoint_cross_attention(       # [B, K, C]
            tag_embeddings, image_tokens, image_tokens
        )

        # ------------------------------------------------------------------
        # 5. Score candidates  &  scatter back
        # ------------------------------------------------------------------
        candidate_logits = self._checkpoint_final_scoring(attended_features, candidate_indices)  # [B, K]

        # --- align dtypes so scatter never throws ---
        if candidate_logits.dtype != initial_logits.dtype:
            candidate_logits = candidate_logits.to(initial_logits.dtype)

        refined_logits = initial_logits.clone()
        refined_logits.scatter_(1, candidate_indices, candidate_logits)
        refined_preds = refined_logits

        # ------------------------------------------------------------------
        # 6. Optional stats
        # ------------------------------------------------------------------
        if self.model_stats and targets is not None and not torch._dynamo.is_compiling():
            model_stats['initial_prediction_stats'] = self._analyze_predictions(initial_preds,
                                                                                candidate_indices)
            model_stats['refined_prediction_stats'] = self._analyze_predictions(refined_preds,
                                                                                candidate_indices)

        return {
            'initial_predictions': initial_preds,
            'refined_predictions': refined_preds,
            'selected_candidates': candidate_indices,
            'model_stats': model_stats
        }
    
    def predict