import os
from dataclasses import dataclass, field
from typing import Any, List, Optional, Tuple

import numpy as np
import torch
import torch.nn.functional as F
import trimesh
from einops import rearrange
from huggingface_hub import hf_hub_download
from jaxtyping import Float
from omegaconf import OmegaConf
from PIL import Image
from safetensors.torch import load_model
from torch import Tensor

from sf3d.models.isosurface import MarchingTetrahedraHelper
from sf3d.models.mesh import Mesh
from sf3d.models.utils import (
    BaseModule,
    ImageProcessor,
    convert_data,
    dilate_fill,
    dot,
    find_class,
    float32_to_uint8_np,
    normalize,
    scale_tensor,
)
from sf3d.utils import create_intrinsic_from_fov_deg, default_cond_c2w

from .texture_baker import TextureBaker


class SF3D(BaseModule):
    @dataclass
    class Config(BaseModule.Config):
        cond_image_size: int
        isosurface_resolution: int
        isosurface_threshold: float = 10.0
        radius: float = 1.0
        background_color: list[float] = field(default_factory=lambda: [0.5, 0.5, 0.5])
        default_fovy_deg: float = 40.0
        default_distance: float = 1.6

        camera_embedder_cls: str = ""
        camera_embedder: dict = field(default_factory=dict)

        image_tokenizer_cls: str = ""
        image_tokenizer: dict = field(default_factory=dict)

        tokenizer_cls: str = ""
        tokenizer: dict = field(default_factory=dict)

        backbone_cls: str = ""
        backbone: dict = field(default_factory=dict)

        post_processor_cls: str = ""
        post_processor: dict = field(default_factory=dict)

        decoder_cls: str = ""
        decoder: dict = field(default_factory=dict)

        image_estimator_cls: str = ""
        image_estimator: dict = field(default_factory=dict)

        global_estimator_cls: str = ""
        global_estimator: dict = field(default_factory=dict)

    cfg: Config

    @classmethod
    def from_pretrained(
        cls, pretrained_model_name_or_path: str, config_name: str, weight_name: str
    ):
        if os.path.isdir(pretrained_model_name_or_path):
            config_path = os.path.join(pretrained_model_name_or_path, config_name)
            weight_path = os.path.join(pretrained_model_name_or_path, weight_name)
        else:
            config_path = hf_hub_download(
                repo_id=pretrained_model_name_or_path, filename=config_name
            )
            weight_path = hf_hub_download(
                repo_id=pretrained_model_name_or_path, filename=weight_name
            )

        cfg = OmegaConf.load(config_path)
        OmegaConf.resolve(cfg)
        model = cls(cfg)
        load_model(model, weight_path)
        return model

    @property
    def device(self):
        return next(self.parameters()).device

    def configure(self):
        self.image_tokenizer = find_class(self.cfg.image_tokenizer_cls)(
            self.cfg.image_tokenizer
        )
        self.tokenizer = find_class(self.cfg.tokenizer_cls)(self.cfg.tokenizer)
        self.camera_embedder = find_class(self.cfg.camera_embedder_cls)(
            self.cfg.camera_embedder
        )
        self.backbone = find_class(self.cfg.backbone_cls)(self.cfg.backbone)
        self.post_processor = find_class(self.cfg.post_processor_cls)(
            self.cfg.post_processor
        )
        self.decoder = find_class(self.cfg.decoder_cls)(self.cfg.decoder)
        self.image_estimator = find_class(self.cfg.image_estimator_cls)(
            self.cfg.image_estimator
        )
        self.global_estimator = find_class(self.cfg.global_estimator_cls)(
            self.cfg.global_estimator
        )

        self.bbox: Float[Tensor, "2 3"]
        self.register_buffer(
            "bbox",
            torch.as_tensor(
                [
                    [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],
                    [self.cfg.radius, self.cfg.radius, self.cfg.radius],
                ],
                dtype=torch.float32,
            ),
        )
        self.isosurface_helper = MarchingTetrahedraHelper(
            self.cfg.isosurface_resolution,
            os.path.join(
                os.path.dirname(__file__),
                "..",
                "load",
                "tets",
                f"{self.cfg.isosurface_resolution}_tets.npz",
            ),
        )

        self.baker = TextureBaker()
        self.image_processor = ImageProcessor()

    def triplane_to_meshes(
        self, triplanes: Float[Tensor, "B 3 Cp Hp Wp"]
    ) -> list[Mesh]:
        meshes = []
        for i in range(triplanes.shape[0]):
            triplane = triplanes[i]
            grid_vertices = scale_tensor(
                self.isosurface_helper.grid_vertices.to(triplanes.device),
                self.isosurface_helper.points_range,
                self.bbox,
            )

            values = self.query_triplane(grid_vertices, triplane)
            decoded = self.decoder(values, include=["vertex_offset", "density"])
            sdf = decoded["density"] - self.cfg.isosurface_threshold

            deform = decoded["vertex_offset"].squeeze(0)

            mesh: Mesh = self.isosurface_helper(
                sdf.view(-1, 1), deform.view(-1, 3) if deform is not None else None
            )
            mesh.v_pos = scale_tensor(
                mesh.v_pos, self.isosurface_helper.points_range, self.bbox
            )

            meshes.append(mesh)

        return meshes

    def query_triplane(
        self,
        positions: Float[Tensor, "*B N 3"],
        triplanes: Float[Tensor, "*B 3 Cp Hp Wp"],
    ) -> Float[Tensor, "*B N F"]:
        batched = positions.ndim == 3
        if not batched:
            # no batch dimension
            triplanes = triplanes[None, ...]
            positions = positions[None, ...]
        assert triplanes.ndim == 5 and positions.ndim == 3

        positions = scale_tensor(
            positions, (-self.cfg.radius, self.cfg.radius), (-1, 1)
        )

        indices2D: Float[Tensor, "B 3 N 2"] = torch.stack(
            (positions[..., [0, 1]], positions[..., [0, 2]], positions[..., [1, 2]]),
            dim=-3,
        ).to(triplanes.dtype)
        out: Float[Tensor, "B3 Cp 1 N"] = F.grid_sample(
            rearrange(triplanes, "B Np Cp Hp Wp -> (B Np) Cp Hp Wp", Np=3).float(),
            rearrange(indices2D, "B Np N Nd -> (B Np) () N Nd", Np=3).float(),
            align_corners=True,
            mode="bilinear",
        )
        out = rearrange(out, "(B Np) Cp () N -> B N (Np Cp)", Np=3)

        return out

    def get_scene_codes(self, batch) -> Float[Tensor, "B 3 C H W"]:
        # if batch[rgb_cond] is only one view, add a view dimension
        if len(batch["rgb_cond"].shape) == 4:
            batch["rgb_cond"] = batch["rgb_cond"].unsqueeze(1)
            batch["mask_cond"] = batch["mask_cond"].unsqueeze(1)
            batch["c2w_cond"] = batch["c2w_cond"].unsqueeze(1)
            batch["intrinsic_cond"] = batch["intrinsic_cond"].unsqueeze(1)
            batch["intrinsic_normed_cond"] = batch["intrinsic_normed_cond"].unsqueeze(1)
        batch_size, n_input_views = batch["rgb_cond"].shape[:2]

        camera_embeds: Optional[Float[Tensor, "B Nv Cc"]]
        camera_embeds = self.camera_embedder(**batch)

        input_image_tokens: Float[Tensor, "B Nv Cit Nit"] = self.image_tokenizer(
            rearrange(batch["rgb_cond"], "B Nv H W C -> B Nv C H W"),
            modulation_cond=camera_embeds,
        )

        input_image_tokens = rearrange(
            input_image_tokens, "B Nv C Nt -> B (Nv Nt) C", Nv=n_input_views
        )

        tokens: Float[Tensor, "B Ct Nt"] = self.tokenizer(batch_size)

        tokens = self.backbone(
            tokens,
            encoder_hidden_states=input_image_tokens,
            modulation_cond=None,
        )

        direct_codes = self.tokenizer.detokenize(tokens)
        scene_codes = self.post_processor(direct_codes)
        return scene_codes, direct_codes

    def run_image(
        self,
        image: Image,
        bake_resolution: int,
        estimate_illumination: bool = False,
    ) -> Tuple[trimesh.Trimesh, dict[str, Any]]:
        if image.mode != "RGBA":
            raise ValueError("Image must be in RGBA mode")
        img_cond = (
            torch.from_numpy(
                np.asarray(
                    image.resize((self.cfg.cond_image_size, self.cfg.cond_image_size))
                ).astype(np.float32)
                / 255.0
            )
            .float()
            .clip(0, 1)
            .to(self.device)
        )
        mask_cond = img_cond[:, :, -1:]
        rgb_cond = torch.lerp(
            torch.tensor(self.cfg.background_color, device=self.device)[None, None, :],
            img_cond[:, :, :3],
            mask_cond,
        )

        c2w_cond = default_cond_c2w(self.cfg.default_distance).to(self.device)
        intrinsic, intrinsic_normed_cond = create_intrinsic_from_fov_deg(
            self.cfg.default_fovy_deg,
            self.cfg.cond_image_size,
            self.cfg.cond_image_size,
        )

        batch = {
            "rgb_cond": rgb_cond,
            "mask_cond": mask_cond,
            "c2w_cond": c2w_cond.unsqueeze(0),
            "intrinsic_cond": intrinsic.to(self.device).unsqueeze(0),
            "intrinsic_normed_cond": intrinsic_normed_cond.to(self.device).unsqueeze(0),
        }

        meshes, global_dict = self.generate_mesh(
            batch, bake_resolution, estimate_illumination
        )
        return meshes[0], global_dict

    def generate_mesh(
        self,
        batch,
        bake_resolution: int,
        estimate_illumination: bool = False,
    ) -> Tuple[List[trimesh.Trimesh], dict[str, Any]]:
        batch["rgb_cond"] = self.image_processor(
            batch["rgb_cond"], self.cfg.cond_image_size
        )
        batch["mask_cond"] = self.image_processor(
            batch["mask_cond"], self.cfg.cond_image_size
        )
        scene_codes, non_postprocessed_codes = self.get_scene_codes(batch)

        global_dict = {}
        if self.image_estimator is not None:
            global_dict.update(
                self.image_estimator(batch["rgb_cond"] * batch["mask_cond"])
            )
        if self.global_estimator is not None and estimate_illumination:
            global_dict.update(self.global_estimator(non_postprocessed_codes))

        with torch.no_grad():
            with torch.autocast(device_type="cuda", enabled=False):
                meshes = self.triplane_to_meshes(scene_codes)

                rets = []
                for i, mesh in enumerate(meshes):
                    # Check for empty mesh
                    if mesh.v_pos.shape[0] == 0:
                        rets.append(trimesh.Trimesh())
                        continue

                    mesh.unwrap_uv()

                    # Build textures
                    rast = self.baker.rasterize(
                        mesh.v_tex, mesh.t_pos_idx, bake_resolution
                    )
                    bake_mask = self.baker.get_mask(rast)

                    pos_bake = self.baker.interpolate(
                        mesh.v_pos,
                        rast,
                        mesh.t_pos_idx,
                        mesh.v_tex,
                    )
                    gb_pos = pos_bake[bake_mask]

                    tri_query = self.query_triplane(gb_pos, scene_codes[i])[0]
                    decoded = self.decoder(
                        tri_query, exclude=["density", "vertex_offset"]
                    )

                    nrm = self.baker.interpolate(
                        mesh.v_nrm,
                        rast,
                        mesh.t_pos_idx,
                        mesh.v_tex,
                    )
                    gb_nrm = F.normalize(nrm[bake_mask], dim=-1)
                    decoded["normal"] = gb_nrm

                    # Check if any keys in global_dict start with decoded_
                    for k, v in global_dict.items():
                        if k.startswith("decoder_"):
                            decoded[k.replace("decoder_", "")] = v[i]

                    mat_out = {
                        "albedo": decoded["features"],
                        "roughness": decoded["roughness"],
                        "metallic": decoded["metallic"],
                        "normal": normalize(decoded["perturb_normal"]),
                        "bump": None,
                    }

                    for k, v in mat_out.items():
                        if v is None:
                            continue
                        if v.shape[0] == 1:
                            # Skip and directly add a single value
                            mat_out[k] = v[0]
                        else:
                            f = torch.zeros(
                                bake_resolution,
                                bake_resolution,
                                v.shape[-1],
                                dtype=v.dtype,
                                device=v.device,
                            )
                            if v.shape == f.shape:
                                continue
                            if k == "normal":
                                # Use un-normalized tangents here so that larger smaller tris
                                # Don't effect the tangents that much
                                tng = self.baker.interpolate(
                                    mesh.v_tng,
                                    rast,
                                    mesh.t_pos_idx,
                                    mesh.v_tex,
                                )
                                gb_tng = tng[bake_mask]
                                gb_tng = F.normalize(gb_tng, dim=-1)
                                gb_btng = F.normalize(
                                    torch.cross(gb_tng, gb_nrm, dim=-1), dim=-1
                                )
                                normal = F.normalize(mat_out["normal"], dim=-1)

                                bump = torch.cat(
                                    # Check if we have to flip some things
                                    (
                                        dot(normal, gb_tng),
                                        dot(normal, gb_btng),
                                        dot(normal, gb_nrm).clip(
                                            0.3, 1
                                        ),  # Never go below 0.3. This would indicate a flipped (or close to one) normal
                                    ),
                                    -1,
                                )
                                bump[..., :2] *= 0.5
                                bump = (bump * 0.5 + 0.5).clamp(0, 1)

                                f[bake_mask] = bump.view(-1, 3)
                                mat_out["bump"] = f
                            else:
                                f[bake_mask] = v.view(-1, v.shape[-1])
                                mat_out[k] = f

                    def uv_padding(arr):
                        if arr.ndim == 1:
                            return arr
                        return (
                            dilate_fill(
                                arr.permute(2, 0, 1)[None, ...],
                                bake_mask.unsqueeze(0).unsqueeze(0),
                                iterations=bake_resolution // 150,
                            )
                            .squeeze(0)
                            .permute(1, 2, 0)
                        )

                    verts_np = convert_data(mesh.v_pos)
                    faces = convert_data(mesh.t_pos_idx)
                    uvs = convert_data(mesh.v_tex)

                    basecolor_tex = Image.fromarray(
                        float32_to_uint8_np(convert_data(uv_padding(mat_out["albedo"])))
                    ).convert("RGB")
                    basecolor_tex.format = "JPEG"

                    metallic = mat_out["metallic"].squeeze().cpu().item()
                    roughness = mat_out["roughness"].squeeze().cpu().item()

                    if "bump" in mat_out and mat_out["bump"] is not None:
                        bump_np = convert_data(uv_padding(mat_out["bump"]))
                        bump_up = np.ones_like(bump_np)
                        bump_up[..., :2] = 0.5
                        bump_up[..., 2:] = 1
                        bump_tex = Image.fromarray(
                            float32_to_uint8_np(
                                bump_np,
                                dither=True,
                                # Do not dither if something is perfectly flat
                                dither_mask=np.all(
                                    bump_np == bump_up, axis=-1, keepdims=True
                                ).astype(np.float32),
                            )
                        ).convert("RGB")
                        bump_tex.format = (
                            "JPEG"  # PNG would be better but the assets are larger
                        )
                    else:
                        bump_tex = None

                    material = trimesh.visual.material.PBRMaterial(
                        baseColorTexture=basecolor_tex,
                        roughnessFactor=roughness,
                        metallicFactor=metallic,
                        normalTexture=bump_tex,
                    )

                    tmesh = trimesh.Trimesh(
                        vertices=verts_np,
                        faces=faces,
                        visual=trimesh.visual.texture.TextureVisuals(
                            uv=uvs, material=material
                        ),
                    )
                    rot = trimesh.transformations.rotation_matrix(
                        np.radians(-90), [1, 0, 0]
                    )
                    tmesh.apply_transform(rot)
                    tmesh.apply_transform(
                        trimesh.transformations.rotation_matrix(
                            np.radians(90), [0, 1, 0]
                        )
                    )

                    tmesh.invert()

                    rets.append(tmesh)

        return rets, global_dict