"""
Shared model loading and embedding extraction utilities.

All evaluation scripts that need to load GAP-CLIP, the Fashion-CLIP baseline,
or the specialized color model should import from here instead of duplicating
the loading logic.
"""

from __future__ import annotations

import sys
from pathlib import Path
from typing import Tuple

import torch
import torch.nn.functional as F
from PIL import Image
from transformers import CLIPModel as CLIPModelTransformers
from transformers import CLIPProcessor

# Make project root importable when running evaluation scripts directly.
_PROJECT_ROOT = Path(__file__).resolve().parents[2]
if str(_PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(_PROJECT_ROOT))


# ---------------------------------------------------------------------------
# GAP-CLIP (main model)
# ---------------------------------------------------------------------------

def load_gap_clip(
    model_path: str,
    device: torch.device,
) -> Tuple[CLIPModelTransformers, CLIPProcessor]:
    """Load GAP-CLIP (LAION CLIP + fine-tuned checkpoint) and its processor.

    Args:
        model_path: Path to the `gap_clip.pth` checkpoint.
        device: Target device.

    Returns:
        (model, processor) ready for inference.
    """
    model = CLIPModelTransformers.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
    checkpoint = torch.load(model_path, map_location=device, weights_only=False)

    if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint:
        model.load_state_dict(checkpoint["model_state_dict"])
    else:
        model.load_state_dict(checkpoint)

    model = model.to(device)
    model.eval()
    processor = CLIPProcessor.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
    return model, processor


# ---------------------------------------------------------------------------
# Fashion-CLIP baseline
# ---------------------------------------------------------------------------

def load_baseline_fashion_clip(
    device: torch.device,
) -> Tuple[CLIPModelTransformers, CLIPProcessor]:
    """Load the Fashion-CLIP baseline (patrickjohncyh/fashion-clip).

    Returns:
        (model, processor) ready for inference.
    """
    model_name = "patrickjohncyh/fashion-clip"
    processor = CLIPProcessor.from_pretrained(model_name)
    model = CLIPModelTransformers.from_pretrained(model_name).to(device)
    model.eval()
    return model, processor


# ---------------------------------------------------------------------------
# Specialized 16D color model
# ---------------------------------------------------------------------------

def load_color_model(
    color_model_path: str,
    device: torch.device,
):
    """Load the specialized 16D color model (CLIP-backbone).

    Returns:
        (color_model, None)  -- second element kept for API compatibility
    """
    from training.color_model import ColorCLIP  # type: ignore

    print("Loading ColorCLIP (CLIP-backbone, 16D) ...")
    color_model = ColorCLIP.from_checkpoint(color_model_path, device=device)
    print("Color model loaded successfully")
    return color_model, None


def load_hierarchy_model(
    hierarchy_model_path: str,
    device: torch.device,
):
    """Load the hierarchy model (CLIP-backbone).

    Returns:
        hierarchy_model ready for inference.
    """
    from training.hierarchy_model import HierarchyModel  # type: ignore

    print("Loading HierarchyModel (CLIP-backbone, 64D) ...")
    model = HierarchyModel.from_checkpoint(hierarchy_model_path, device=device)
    print("Hierarchy model loaded successfully")
    return model



# ---------------------------------------------------------------------------
# Core encoding helpers (same as notebook)
# ---------------------------------------------------------------------------

def encode_text(model, processor, text_queries, device):
    """Encode text queries into embeddings (unnormalized)."""
    if isinstance(text_queries, str):
        text_queries = [text_queries]
    inputs = processor(text=text_queries, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    with torch.no_grad():
        text_features = model.get_text_features(**inputs)
    return text_features


def encode_image(model, processor, images, device):
    """Encode images into embeddings (unnormalized)."""
    if not isinstance(images, list):
        images = [images]
    inputs = processor(images=images, return_tensors="pt")
    inputs = {k: v.to(device) for k, v in inputs.items()}
    with torch.no_grad():
        image_features = model.get_image_features(**inputs)
    return image_features


# ---------------------------------------------------------------------------
# Normalized wrappers (preserve old call signatures used across eval scripts)
# ---------------------------------------------------------------------------

def get_text_embedding(model, processor, device, text):
    """Single normalized text embedding (shape: [512])."""
    return F.normalize(encode_text(model, processor, text, device), dim=-1).squeeze(0)


def get_text_embeddings_batch(model, processor, device, texts):
    """Normalized text embeddings for a batch (shape: [N, 512])."""
    return F.normalize(encode_text(model, processor, texts, device), dim=-1)


def get_image_embedding_from_pil(model, processor, device, pil_image):
    """Normalized image embedding from a PIL image (shape: [512])."""
    return F.normalize(encode_image(model, processor, pil_image, device), dim=-1).squeeze(0)