# Reference: https://github.com/prs-eth/Marigold
# Strictly follows official `script/depth/run.py`:
#   from marigold import MarigoldDepthPipeline
#   pipe = MarigoldDepthPipeline.from_pretrained(checkpoint, torch_dtype=dtype)
#   pipe_out = pipe(input_pil_image, denoise_steps, ensemble_size, processing_res,
#                   match_input_res, batch_size, resample_method, ...)
#   depth_pred: np.ndarray = pipe_out.depth_np   # normalized affine-invariant depth
#
# Marigold reports its outputs as affine-invariant depth (Marigold paper, CVPR 2024).
# Returns key `depth_affine_invariant`.

import os
import sys
from typing import *
from pathlib import Path

import click
import torch
import torch.nn.functional as F
import numpy as np
from PIL import Image

from moge.test.baseline import MGEBaselineInterface


class Baseline(MGEBaselineInterface):
    def __init__(self, repo_path: str, checkpoint: str, denoise_steps: Optional[int],
                 ensemble_size: int, processing_res: Optional[int], half_precision: bool,
                 device: Union[torch.device, str]):
        repo_path = os.path.abspath(repo_path)
        if not Path(repo_path).exists():
            raise FileNotFoundError(
                f"Cannot find Marigold repo at {repo_path}. Clone https://github.com/prs-eth/Marigold."
            )
        if repo_path not in sys.path:
            sys.path.insert(0, repo_path)

        from marigold import MarigoldDepthPipeline

        device = torch.device(device)
        dtype = torch.float16 if half_precision else torch.float32
        variant = "fp16" if half_precision else None

        pipe = MarigoldDepthPipeline.from_pretrained(checkpoint, variant=variant, torch_dtype=dtype)
        try:
            pipe.enable_xformers_memory_efficient_attention()
        except ImportError:
            pass
        pipe = pipe.to(device)
        pipe.set_progress_bar_config(disable=True)

        self.pipe = pipe
        self.device = device
        self.denoise_steps = denoise_steps
        self.ensemble_size = ensemble_size
        self.processing_res = processing_res

    @click.command()
    @click.option('--repo', 'repo_path', type=click.Path(), default='../Marigold',
                  help='Path to the prs-eth/Marigold repository.')
    @click.option('--checkpoint', type=str, default='prs-eth/marigold-depth-v1-1',
                  help='HuggingFace ckpt name or local dir (run.py default).')
    @click.option('--denoise_steps', type=int, default=None,
                  help='Diffusion denoising steps. None -> default in ckpt.')
    @click.option('--ensemble_size', type=int, default=1,
                  help='Ensemble size. run.py default = 1.')
    @click.option('--processing_res', type=int, default=None,
                  help='Processing resolution. None -> default in ckpt.')
    @click.option('--fp16', 'half_precision', is_flag=True, help='Run in half precision.')
    @click.option('--device', type=str, default='cuda')
    @staticmethod
    def load(repo_path: str, checkpoint: str, denoise_steps: Optional[int],
             ensemble_size: int, processing_res: Optional[int], half_precision: bool,
             device: str = 'cuda'):
        return Baseline(repo_path, checkpoint, denoise_steps, ensemble_size,
                        processing_res, half_precision, device)

    @torch.inference_mode()
    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
        assert intrinsics is None or True, "Marigold does not consume intrinsics; argument ignored."
        omit_batch = image.ndim == 3
        if omit_batch:
            image = image.unsqueeze(0)
        assert image.shape[0] == 1, "Marigold baseline only supports batch size 1"
        _, _, H, W = image.shape

        # MoGe pipeline supplies image as float tensor in [0, 1]. Marigold pipe takes PIL.Image (run.py uses PIL).
        arr = (image[0].cpu().permute(1, 2, 0).clamp(0, 1).numpy() * 255).astype(np.uint8)
        pil = Image.fromarray(arr)

        kwargs: Dict[str, Any] = dict(
            ensemble_size=self.ensemble_size,
            match_input_res=True,
            batch_size=0,
            resample_method='bilinear',
            show_progress_bar=False,
        )
        if self.denoise_steps is not None:
            kwargs['denoising_steps'] = self.denoise_steps  # pipeline kwarg is "denoising_steps"
        if self.processing_res is not None:
            kwargs['processing_res'] = self.processing_res

        out = self.pipe(pil, **kwargs)

        # MarigoldDepthOutput.depth_np: HxW np.float32 in [0, 1]. Marigold paper:
        # affine-invariant depth (linear monotone with true depth, scale+shift free).
        depth_np = out.depth_np
        depth = torch.from_numpy(np.ascontiguousarray(depth_np)).to(self.device).float()

        # Resize back if pipeline yielded a different size (it shouldn't with match_input_res=True).
        if depth.shape[-2:] != (H, W):
            depth = F.interpolate(depth[None, None], size=(H, W), mode='bilinear', align_corners=False)[0, 0]

        # Marigold predicts affine-invariant depth (Marigold paper, CVPR 2024). Emit only
        # this physical key. MoGe compute_metrics reports `depth_affine_invariant` metric.
        result = {'depth_affine_invariant': depth}
        if not omit_batch:
            result['depth_affine_invariant'] = result['depth_affine_invariant'].unsqueeze(0)
        return result