# Reference: https://github.com/EnVision-Research/Lotus
# Strictly follows official `infer.py`:
#   from pipeline import LotusGPipeline, LotusDPipeline
#   pipeline = LotusXPipeline.from_pretrained(args.pretrained_model_name_or_path, torch_dtype=dtype)
#   # image in [-1, 1] tensor, shape (1, 3, H, W)
#   task_emb = torch.tensor([1, 0]).float().unsqueeze(0)
#   task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)], dim=-1)
#   pred = pipeline(rgb_in=image, prompt='', num_inference_steps=1,
#                   timesteps=[args.timestep], task_emb=task_emb,
#                   processing_res=processing_res, match_input_res=match_input_res,
#                   resample_method=resample_method).images[0]
#   if args.task_name == 'depth':
#       output_npy = pred.mean(axis=-1)
#
# Default released depth checkpoints (per README):
#   jingheya/lotus-depth-g-v1-0       (generation, depth)
#   jingheya/lotus-depth-d-v1-0       (regression, depth)
#   jingheya/lotus-depth-g-v2-1-disparity (generation, disparity)
#   jingheya/lotus-depth-d-v2-0-disparity (regression, disparity)
# Output key depends on whether the checkpoint predicts depth or disparity.

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 moge.test.baseline import MGEBaselineInterface


class Baseline(MGEBaselineInterface):
    def __init__(self, repo_path: str, pretrained: str, mode: str, task_name: str,
                 disparity: bool, timestep: int, processing_res: Optional[int],
                 half_precision: bool, seed: Optional[int], device: Union[torch.device, str]):
        repo_path = os.path.abspath(repo_path)
        if not Path(repo_path).exists():
            raise FileNotFoundError(
                f"Cannot find Lotus repo at {repo_path}. Clone https://github.com/EnVision-Research/Lotus."
            )
        # Lotus' pipeline / utils packages are at the repo root.
        if repo_path not in sys.path:
            sys.path.insert(0, repo_path)
        # MoGe's dataloader imports a different top-level package also named `pipeline`
        # (from EasternJournalist/pipeline). It is already cached in sys.modules by the
        # time we reach here, so `from pipeline import LotusGPipeline` would resolve to
        # the wrong module. Evict the cached entry so Python re-resolves against
        # Lotus' repo (which is first on sys.path).
        sys.modules.pop('pipeline', None)
        from pipeline import LotusGPipeline, LotusDPipeline

        device = torch.device(device)
        dtype = torch.float16 if half_precision else torch.float32

        if mode == 'generation':
            pipeline = LotusGPipeline.from_pretrained(pretrained, torch_dtype=dtype)
        elif mode == 'regression':
            pipeline = LotusDPipeline.from_pretrained(pretrained, torch_dtype=dtype)
        else:
            raise ValueError(f"Invalid mode: {mode}")
        pipeline = pipeline.to(device)
        pipeline.set_progress_bar_config(disable=True)

        self.pipeline = pipeline
        self.device = device
        self.dtype = dtype
        self.mode = mode
        self.task_name = task_name
        self.disparity = disparity
        self.timestep = timestep
        self.processing_res = processing_res
        self.generator = torch.Generator(device=device).manual_seed(seed) if seed is not None else None

    @click.command()
    @click.option('--repo', 'repo_path', type=click.Path(), default='../Lotus',
                  help='Path to the EnVision-Research/Lotus repository.')
    @click.option('--pretrained', type=str, default='jingheya/lotus-depth-d-v2-0-disparity',
                  help='HF checkpoint name or local dir. README default disparity v2 is recommended.')
    @click.option('--mode', type=click.Choice(['generation', 'regression']), default='regression',
                  help='Which Lotus pipeline (G/generation or D/regression).')
    @click.option('--task_name', type=click.Choice(['depth', 'normal']), default='depth')
    @click.option('--disparity', is_flag=True,
                  help='Set if the checkpoint predicts disparity (e.g. *-disparity ckpts).')
    @click.option('--timestep', type=int, default=999)
    @click.option('--processing_res', type=int, default=None,
                  help='Pipeline processing resolution. None uses default in checkpoint.')
    @click.option('--fp16', 'half_precision', is_flag=True, help='Run in half precision.')
    @click.option('--seed', type=int, default=None, help='Reproducibility seed (Lotus eval.sh uses 42).')
    @click.option('--device', type=str, default='cuda')
    @staticmethod
    def load(repo_path: str, pretrained: str, mode: str, task_name: str, disparity: bool,
             timestep: int, processing_res: Optional[int], half_precision: bool,
             seed: Optional[int], device: str = 'cuda'):
        return Baseline(repo_path, pretrained, mode, task_name, disparity, timestep,
                        processing_res, half_precision, seed, device)

    @torch.inference_mode()
    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
        omit_batch = image.ndim == 3
        if omit_batch:
            image = image.unsqueeze(0)
        assert image.shape[0] == 1, "Lotus baseline only supports batch size 1"
        _, _, H, W = image.shape

        # infer.py converts uint8 [0,255] to [-1,1] via `/127.5 - 1.0`. MoGe gives [0,1] floats,
        # so the equivalent normalization is `image * 2 - 1`.
        rgb_in = (image.to(self.device, dtype=self.dtype) * 2.0 - 1.0)

        task_emb = torch.tensor([1, 0], device=self.device, dtype=self.dtype).unsqueeze(0)
        task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)], dim=-1)

        pred = self.pipeline(
            rgb_in=rgb_in,
            prompt='',
            num_inference_steps=1,
            generator=self.generator,
            output_type='np',
            timesteps=[self.timestep],
            task_emb=task_emb,
            processing_res=self.processing_res,
            match_input_res=True,
            resample_method='bilinear',
        ).images[0]

        # Per infer.py: depth uses mean over channels; pred is HxWx3 in [0, 1].
        if self.task_name == 'depth':
            arr = pred.mean(axis=-1)
        else:
            raise NotImplementedError("Normal task is not exposed by this baseline.")
        depth_or_disp = torch.from_numpy(np.ascontiguousarray(arr)).to(self.device).float()
        if depth_or_disp.shape != (H, W):
            depth_or_disp = F.interpolate(depth_or_disp[None, None], size=(H, W),
                                          mode='bilinear', align_corners=False)[0, 0]

        # Lotus disparity ckpts: model physically predicts disparity in [0, 1]. Emit
        # ONLY `disparity_affine_invariant`. We previously synthesized `depth_affine_invariant`
        # via 1/disp, but this is numerically unstable near disparity=0 — the resulting
        # depth-space affine alignment is dominated by inverted-small-disparity outliers,
        # not by the model's actual depth quality. Cross-comparison with depth-emitting
        # models happens via MoGe's fall-through to `disparity_affine_invariant` (1/depth),
        # which IS numerically stable.
        if self.disparity:
            result = {'disparity_affine_invariant': depth_or_disp}
        else:
            # Lotus depth ckpt: directly affine-invariant depth.
            result = {'depth_affine_invariant': depth_or_disp}
        if not omit_batch:
            for k in result: result[k] = result[k].unsqueeze(0)
        return result