# Reference: https://github.com/ByteDance-Seed/Depth-Anything-3
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, model_name: 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.insert(0, os.path.join(repo_path, 'src'))
        if not Path(repo_path).exists():
            raise FileNotFoundError(f'Cannot find the Depth-Anything-3 repository at {repo_path}. Please clone the repository and provide the path to it using the --repo option.')

        from depth_anything_3.api import DepthAnything3

        device = torch.device(device)

        # Instantiate model
        model = DepthAnything3.from_pretrained(f"ByteDance-Seed/{model_name}")

        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-3', help='Path to the Depth-Anything-3 repository.')
    @click.option('--model_name', type=click.Choice(['da3-base', 'da3-large', 'da3-giant']), default='da3-large', help='Model name.')
    @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, model_name: str, num_tokens: int, device: torch.device = 'cuda'):
        return Baseline(repo_path, model_name, 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-3 does not support camera intrinsics input in this baseline"

        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])

        # DA3 expects [B, N, 3, H, W] where N is number of views
        image = image.unsqueeze(1)  # [B, 1, 3, H, W]

        # Forward pass
        output = self.model(image)

        # Extract depth prediction
        # Output shape: [B, N, H, W]
        depth = output['depth'][:, 0]  # [B, H, W]

        # Convert depth to disparity (inverse depth)
        disparity = 1.0 / (depth + 1e-6)

        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
        }