"""
SatCLIP Multi-Spectral Model

This model supports both RGB input and full multi-spectral Sentinel-2 input (12/13 bands).
"""
import sys
import os
import torch
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch.nn.functional as F
import torchvision.transforms as T
from PIL import Image
from huggingface_hub import hf_hub_download

import warnings

with warnings.catch_warnings():
    warnings.filterwarnings("ignore", category=FutureWarning)
    try:
        from models.SatCLIP.satclip.load import get_satclip
        print("Successfully imported models.SatCLIP.satclip.load.get_satclip.")
    except ImportError:
        get_satclip = None
        print("Warning: SatCLIP not available. Please check installation.")


class SatCLIPMSModel:
    """
    SatCLIP model wrapper supporting multi-spectral Sentinel-2 input.
    
    Supports:
    - RGB PIL Image input (auto-converted to 13 channels)
    - 12-band Sentinel-2 numpy array (auto-padded to 13 channels)
    - 13-band full Sentinel-2 tensor
    """
    
    def __init__(self, 
                 ckpt_path='./checkpoints/SatCLIP/satclip-vit16-l40.ckpt',
                 embedding_path=None,
                 device=None):
        
        self.device = device if device else ("cuda" if torch.cuda.is_available() else "cpu")
        if ckpt_path and 'hf' in ckpt_path:
            ckpt_path = hf_hub_download("microsoft/SatCLIP-ViT16-L40", "satclip-vit16-l40.ckpt")
        self.ckpt_path = ckpt_path
        self.embedding_path = embedding_path
        
        self.model = None
        self.df_embed = None
        self.image_embeddings = None
        
        # SatCLIP input size
        self.input_size = 224
        
        # Sentinel-2 bands mapping
        # MajorTOM 12 bands: [B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12]
        # SatCLIP 13 bands:  [B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B10, B11, B12]
        # B10 is missing in MajorTOM, need to insert zeros at index 10
        self.majortom_bands = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12']
        self.satclip_bands = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12']
        
        self.load_model()
        if self.embedding_path:
            self.load_embeddings()

    def load_model(self):
        if get_satclip is None:
            print("Error: SatCLIP functionality is not available.")
            return

        print(f"Loading SatCLIP-MS model from {self.ckpt_path}...")
        try:
            if not os.path.exists(self.ckpt_path):
                print(f"Warning: Checkpoint not found at {self.ckpt_path}")
                return

            # Load model using get_satclip
            # return_all=True to get both visual and location encoders
            self.model = get_satclip(self.ckpt_path, self.device, return_all=True)
            self.model.eval()
            print(f"SatCLIP-MS model loaded on {self.device}")
        except Exception as e:
            print(f"Error loading SatCLIP model: {e}")

    def load_embeddings(self):
        print(f"Loading SatCLIP embeddings from {self.embedding_path}...")
        try:
            if not os.path.exists(self.embedding_path):
                print(f"Warning: Embedding file not found at {self.embedding_path}")
                return

            self.df_embed = pq.read_table(self.embedding_path).to_pandas()
            
            # Pre-compute image embeddings tensor
            image_embeddings_np = np.stack(self.df_embed['embedding'].values)
            self.image_embeddings = torch.from_numpy(image_embeddings_np).to(self.device).float()
            self.image_embeddings = F.normalize(self.image_embeddings, dim=-1)
            print(f"SatCLIP Data loaded: {len(self.df_embed)} records")
        except Exception as e:
            print(f"Error loading SatCLIP embeddings: {e}")

    def encode_location(self, lat, lon):
        """
        Encode a (latitude, longitude) pair into a vector.
        """
        if self.model is None:
            return None
        
        # SatCLIP expects input shape (N, 2) -> (lon, lat)
        coords = torch.tensor([[lon, lat]], dtype=torch.double).to(self.device)
        
        with torch.no_grad():
            loc_features = self.model.encode_location(coords).float()
            loc_features = loc_features / loc_features.norm(dim=1, keepdim=True)
            
        return loc_features

    def _preprocess_12band_array(self, img_array: np.ndarray) -> torch.Tensor:
        """
        Preprocess a 12-band Sentinel-2 array to 13-band tensor for SatCLIP.
        
        Args:
            img_array: numpy array of shape (H, W, 12) with uint16 values (0-10000+)
            
        Returns:
            torch.Tensor of shape (13, 224, 224) normalized
        """
        # 1. Normalize (SatCLIP standard: / 10000.0)
        image = img_array.astype(np.float32) / 10000.0
        
        # 2. Channel First: (H, W, C) -> (C, H, W) -> (12, H, W)
        image = image.transpose(2, 0, 1)
        
        # 3. Insert B10 (zeros) at index 10 -> (13, H, W)
        # MajorTOM: [B01..B09(idx0-9), B11(idx10), B12(idx11)]
        # SatCLIP:  [B01..B09(idx0-9), B10(idx10), B11(idx11), B12(idx12)]
        B10 = np.zeros((1, image.shape[1], image.shape[2]), dtype=image.dtype)
        image_13 = np.concatenate([image[:10], B10, image[10:]], axis=0)
        
        # 4. Convert to Tensor
        image_tensor = torch.from_numpy(image_13)
        
        # 5. Resize to 224x224
        transforms = T.Resize((self.input_size, self.input_size), 
                             interpolation=T.InterpolationMode.BICUBIC,
                             antialias=True)
        image_tensor = transforms(image_tensor)
        
        return image_tensor

    def _preprocess_rgb_image(self, image: Image.Image) -> torch.Tensor:
        """
        Preprocess RGB PIL Image to 13-band tensor for SatCLIP.
        Maps RGB to B04, B03, B02 and zeros for other bands.
        
        Args:
            image: PIL RGB Image
            
        Returns:
            torch.Tensor of shape (13, 224, 224)
        """
        image = image.convert("RGB")
        image = image.resize((self.input_size, self.input_size))
        img_np = np.array(image).astype(np.float32) / 255.0
        
        # Construct 13 channels
        # S2 bands: B01, B02(B), B03(G), B04(R), B05...
        # Indices:  0=B01, 1=B02, 2=B03, 3=B04 ...
        input_tensor = np.zeros((13, self.input_size, self.input_size), dtype=np.float32)
        input_tensor[1] = img_np[:, :, 2]  # Blue -> B02
        input_tensor[2] = img_np[:, :, 1]  # Green -> B03
        input_tensor[3] = img_np[:, :, 0]  # Red -> B04
        
        return torch.from_numpy(input_tensor)

    def encode_image(self, image, is_multispectral=False):
        """
        Encode an image into a feature vector.
        
        Args:
            image: Can be one of:
                - PIL.Image (RGB) - will be converted to 13-band
                - np.ndarray of shape (H, W, 12) - 12-band Sentinel-2 data
                - torch.Tensor of shape (13, H, W) or (B, 13, H, W) - ready for model
            is_multispectral: Hint to indicate if numpy input is multi-spectral
            
        Returns:
            torch.Tensor: Normalized embedding vector
        """
        if self.model is None:
            return None
        
        try:
            # Handle different input types
            if isinstance(image, Image.Image):
                # RGB PIL Image
                input_tensor = self._preprocess_rgb_image(image).unsqueeze(0)
                
            elif isinstance(image, np.ndarray):
                # Numpy array - assumed to be 12-band Sentinel-2 (H, W, 12)
                if image.ndim == 3 and image.shape[-1] == 12:
                    input_tensor = self._preprocess_12band_array(image).unsqueeze(0)
                elif image.ndim == 3 and image.shape[-1] == 3:
                    # RGB numpy array
                    pil_img = Image.fromarray(image.astype(np.uint8))
                    input_tensor = self._preprocess_rgb_image(pil_img).unsqueeze(0)
                else:
                    print(f"Unsupported numpy array shape: {image.shape}")
                    return None
                    
            elif isinstance(image, torch.Tensor):
                # Already a tensor
                if image.dim() == 3:
                    input_tensor = image.unsqueeze(0)
                else:
                    input_tensor = image
                    
                # Resize if needed
                if input_tensor.shape[-1] != self.input_size or input_tensor.shape[-2] != self.input_size:
                    transforms = T.Resize((self.input_size, self.input_size),
                                         interpolation=T.InterpolationMode.BICUBIC,
                                         antialias=True)
                    input_tensor = transforms(input_tensor)
            else:
                print(f"Unsupported image type: {type(image)}")
                return None
            
            # Move to device and encode
            input_tensor = input_tensor.to(self.device)
            
            with torch.no_grad():
                img_feature = self.model.encode_image(input_tensor)
                img_feature = img_feature / img_feature.norm(dim=1, keepdim=True)
                
            return img_feature
            
        except Exception as e:
            print(f"Error encoding image in SatCLIP-MS: {e}")
            import traceback
            traceback.print_exc()
            return None

    def encode_batch(self, batch_tensors: list) -> np.ndarray:
        """
        Encode a batch of pre-processed tensors.
        
        Args:
            batch_tensors: List of torch.Tensor, each of shape (13, H, W)
            
        Returns:
            np.ndarray of shape (N, embedding_dim)
        """
        if self.model is None:
            return None
            
        try:
            t_stack = torch.stack(batch_tensors).to(self.device)
            
            with torch.no_grad():
                feats = self.model.encode_image(t_stack)
                feats = feats / feats.norm(dim=1, keepdim=True)
                
            return feats.cpu().numpy()
            
        except Exception as e:
            print(f"Error encoding batch: {e}")
            return None

    def search(self, query_features, top_k=5, top_percent=None, threshold=0.0):
        if self.image_embeddings is None:
            return None, None, None

        query_features = query_features.float()
        
        # Similarity calculation (Cosine similarity)
        probs = (self.image_embeddings @ query_features.T).detach().cpu().numpy().flatten()
        
        if top_percent is not None:
            k = int(len(probs) * top_percent)
            if k < 1:
                k = 1
            threshold = np.partition(probs, -k)[-k]

        # Filter by threshold
        mask = probs >= threshold
        filtered_indices = np.where(mask)[0]
        
        # Get top k
        top_indices = np.argsort(probs)[-top_k:][::-1]
        
        return probs, filtered_indices, top_indices