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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#   http://www.apache.org/licenses/LICENSE-2.0
#
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"""
Model inference module for Depth Anything 3 Gradio app.

This module handles all model-related operations including inference,
data processing, and result preparation.
"""

import gc
import glob
import os
from typing import Any, Dict, Optional, Tuple
import numpy as np
import torch

from depth_anything_3.api import DepthAnything3
from depth_anything_3.utils.export.glb import export_to_glb
from depth_anything_3.utils.export.gs import export_to_gs_video


class ModelInference:
    """
    Handles model inference and data processing for Depth Anything 3.
    """

    def __init__(self):
        """Initialize the model inference handler."""
        self.model = None

    def initialize_model(self, device: str = "cuda") -> None:
        """
        Initialize the DepthAnything3 model.

        Args:
            device: Device to load the model on
        """
        if self.model is None:
            # Get model directory from environment variable or use default
            model_dir = os.environ.get(
                "DA3_MODEL_DIR", "/dev/shm/da3_models/DA3HF-VITG-METRIC_VITL"
            )
            self.model = DepthAnything3.from_pretrained(model_dir)
            self.model = self.model.to(device)
        else:
            self.model = self.model.to(device)

        self.model.eval()

    def run_inference(
        self,
        target_dir: str,
        filter_black_bg: bool = False,
        filter_white_bg: bool = False,
        process_res_method: str = "upper_bound_resize",
        show_camera: bool = True,
        selected_first_frame: Optional[str] = None,
        save_percentage: float = 30.0,
        num_max_points: int = 1_000_000,
        infer_gs: bool = False,
        gs_trj_mode: str = "extend",
        gs_video_quality: str = "high",
    ) -> Tuple[Any, Dict[int, Dict[str, Any]]]:
        """
        Run DepthAnything3 model inference on images.

        Args:
            target_dir: Directory containing images
            apply_mask: Whether to apply mask for ambiguous depth classes
            mask_edges: Whether to mask edges
            filter_black_bg: Whether to filter black background
            filter_white_bg: Whether to filter white background
            process_res_method: Method for resizing input images
            show_camera: Whether to show camera in 3D view
            selected_first_frame: Selected first frame filename
            save_percentage: Percentage of points to save (0-100)
            infer_gs: Whether to infer 3D Gaussian Splatting

        Returns:
            Tuple of (prediction, processed_data)
        """
        print(f"Processing images from {target_dir}")

        # Device check
        device = "cuda" if torch.cuda.is_available() else "cpu"
        device = torch.device(device)

        # Initialize model if needed
        self.initialize_model(device)

        # Get image paths
        print("Loading images...")
        image_folder_path = os.path.join(target_dir, "images")
        all_image_paths = sorted(glob.glob(os.path.join(image_folder_path, "*")))

        # Filter for image files
        image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif"]
        all_image_paths = [
            path
            for path in all_image_paths
            if any(path.lower().endswith(ext) for ext in image_extensions)
        ]

        print(f"Found {len(all_image_paths)} images")
        print(f"All image paths: {all_image_paths}")

        # Apply first frame selection logic
        if selected_first_frame:
            # Find the image with matching filename
            selected_path = None
            for path in all_image_paths:
                if os.path.basename(path) == selected_first_frame:
                    selected_path = path
                    break

            if selected_path:
                # Move selected frame to the front
                image_paths = [selected_path] + [
                    path for path in all_image_paths if path != selected_path
                ]
                print(f"User selected first frame: {selected_first_frame} -> {selected_path}")
                print(f"Reordered image paths: {image_paths}")
            else:
                # Use default order if no match found
                image_paths = all_image_paths
                print(
                    f"Selected frame '{selected_first_frame}' not found in image paths. "
                    "Using default order."
                )
                first_frame_display = image_paths[0] if image_paths else "No images"
                print(f"Using default order (first frame): {first_frame_display}")
        else:
            # Use default order (sorted)
            image_paths = all_image_paths
            first_frame_display = image_paths[0] if image_paths else "No images"
            print(f"Using default order (first frame): {first_frame_display}")

        if len(image_paths) == 0:
            raise ValueError("No images found. Check your upload.")

        # Map UI options to actual method names
        method_mapping = {"high_res": "lower_bound_resize", "low_res": "upper_bound_resize"}
        actual_method = method_mapping.get(process_res_method, "upper_bound_crop")

        # Run model inference
        print(f"Running inference with method: {actual_method}")
        with torch.no_grad():
            prediction = self.model.inference(
                image_paths, export_dir=None, process_res_method=actual_method, infer_gs=infer_gs
            )
        # num_max_points: int = 1_000_000,
        export_to_glb(
            prediction,
            filter_black_bg=filter_black_bg,
            filter_white_bg=filter_white_bg,
            export_dir=target_dir,
            show_cameras=show_camera,
            conf_thresh_percentile=save_percentage,
            num_max_points=int(num_max_points),
        )

        # export to gs video if needed
        if infer_gs:
            mode_mapping = {"extend": "extend", "smooth": "interpolate_smooth"}
            print(f"GS mode: {gs_trj_mode}; Backend mode: {mode_mapping[gs_trj_mode]}")
            export_to_gs_video(
                prediction,
                export_dir=target_dir,
                chunk_size=4,
                trj_mode=mode_mapping.get(gs_trj_mode, "extend"),
                enable_tqdm=True,
                vis_depth="hcat",
                video_quality=gs_video_quality,
            )

        # Save predictions.npz for caching metric depth data
        self._save_predictions_cache(target_dir, prediction)

        # Process results
        processed_data = self._process_results(target_dir, prediction, image_paths)

        # Clean up
        torch.cuda.empty_cache()

        return prediction, processed_data

    def _save_predictions_cache(self, target_dir: str, prediction: Any) -> None:
        """
        Save predictions data to predictions.npz for caching.

        Args:
            target_dir: Directory to save the cache
            prediction: Model prediction object
        """
        try:
            output_file = os.path.join(target_dir, "predictions.npz")

            # Build save dict with prediction data
            save_dict = {}

            # Save processed images if available
            if prediction.processed_images is not None:
                save_dict["images"] = prediction.processed_images

            # Save depth data
            if prediction.depth is not None:
                save_dict["depths"] = np.round(prediction.depth, 6)

            # Save confidence if available
            if prediction.conf is not None:
                save_dict["conf"] = np.round(prediction.conf, 2)

            # Save camera parameters
            if prediction.extrinsics is not None:
                save_dict["extrinsics"] = prediction.extrinsics
            if prediction.intrinsics is not None:
                save_dict["intrinsics"] = prediction.intrinsics

            # Save to file
            np.savez_compressed(output_file, **save_dict)
            print(f"Saved predictions cache to: {output_file}")

        except Exception as e:
            print(f"Warning: Failed to save predictions cache: {e}")

    def _process_results(
        self, target_dir: str, prediction: Any, image_paths: list
    ) -> Dict[int, Dict[str, Any]]:
        """
        Process model results into structured data.

        Args:
            target_dir: Directory containing results
            prediction: Model prediction object
            image_paths: List of input image paths

        Returns:
            Dictionary containing processed data for each view
        """
        processed_data = {}

        # Read generated depth visualization files
        depth_vis_dir = os.path.join(target_dir, "depth_vis")

        if os.path.exists(depth_vis_dir):
            depth_files = sorted(glob.glob(os.path.join(depth_vis_dir, "*.jpg")))
            for i, depth_file in enumerate(depth_files):
                # Use processed images directly from API
                processed_image = None
                if prediction.processed_images is not None and i < len(
                    prediction.processed_images
                ):
                    processed_image = prediction.processed_images[i]

                processed_data[i] = {
                    "depth_image": depth_file,
                    "image": processed_image,
                    "original_image_path": image_paths[i] if i < len(image_paths) else None,
                    "depth": prediction.depth[i] if i < len(prediction.depth) else None,
                    "intrinsics": (
                        prediction.intrinsics[i]
                        if prediction.intrinsics is not None and i < len(prediction.intrinsics)
                        else None
                    ),
                    "mask": None,  # No mask information available
                }

        return processed_data

    def cleanup(self) -> None:
        """Clean up GPU memory."""
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()
