# Reference: https://github.com/DepthAnything/Depth-Anything-V2
import os
import sys
from typing import *
from pathlib import Path

import click
import torch
import torch.nn.functional as F
import torchvision.transforms as T
import torchvision.transforms.functional as TF

from moge.test.baseline import MGEBaselineInterface


class Baseline(MGEBaselineInterface):
    def __init__(self, repo_path: str, backbone: str, num_tokens: int, device: Union[torch.device, str]):
        # Create from repo
        repo_path = os.path.abspath(repo_path)
        if repo_path not in sys.path:
            sys.path.append(repo_path)
        if not Path(repo_path).exists():
            raise FileNotFoundError(f'Cannot find the Depth-Anything repository at {repo_path}. Please clone the repository and provide the path to it using the --repo option.')
        from depth_anything_v2.dpt import DepthAnythingV2

        device = torch.device(device)

        # Instantiate model
        model = DepthAnythingV2(encoder=backbone, features=256, out_channels=[256, 512, 1024, 1024])

        # Load checkpoint
        checkpoint_path = os.path.join(repo_path, f'checkpoints/depth_anything_v2_{backbone}.pth')
        if not os.path.exists(checkpoint_path):
            raise FileNotFoundError(f'Cannot find the checkpoint file at {checkpoint_path}. Please download the checkpoint file and place it in the checkpoints directory.')
        checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=True)
        model.load_state_dict(checkpoint)
        
        model.to(device).eval()
        self.model = model
        self.num_tokens = num_tokens
        self.device = device

    @click.command()
    @click.option('--repo', 'repo_path', type=click.Path(), default='../Depth-Anything-V2', help='Path to the Depth-Anything repository.')
    @click.option('--backbone', type=click.Choice(['vits', 'vitb', 'vitl']), default='vitl', help='Encoder architecture.')
    @click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.')
    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
    @staticmethod
    def load(repo_path: str, backbone, num_tokens: int, device: torch.device = 'cuda'):
        return Baseline(repo_path, backbone, num_tokens, device)

    @torch.inference_mode()
    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
        original_height, original_width = image.shape[-2:]

        assert intrinsics is None, "Depth-Anything-V2 does not support camera intrinsics input"

        if image.ndim == 3:
            image = image.unsqueeze(0)
            omit_batch_dim = True
        else:
            omit_batch_dim = False
        
        if self.num_tokens is None:
            resize_factor = 518 / min(original_height, original_width)
            expected_width = round(original_width * resize_factor / 14) * 14
            expected_height = round(original_height * resize_factor / 14) * 14
        else:
            aspect_ratio = original_width / original_height
            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
            expected_width = tokens_cols * 14
            expected_height = tokens_rows * 14
        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
    
        image = TF.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

        disparity = self.model(image)

        disparity = F.interpolate(disparity[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0]

        if omit_batch_dim:
            disparity = disparity.squeeze(0)

        return {
            'disparity_affine_invariant': disparity
        }