from collections import defaultdict
from itertools import permutations, combinations
from pathlib import Path

import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import functional as F

from data.registry import DATASET
from data.transform import transform_pipeline
from data.util import dlib_landmark


def normalize_tensor(tensor):
    tensor = tensor.float()
    tensor -= tensor.min()
    tensor /= tensor.max()
    return tensor


@DATASET.register_module()
class GenerationUnpairedDatasetWithEdge(Dataset):
    def __init__(self, root_a, root_b, random_pair, pipeline, edge_type, edges_path, landmarks_path, size=(256, 256),
                 with_path=False):
        assert edge_type in ["hed", "canny", "landmark_hed", "landmark_canny"]
        self.edge_type = edge_type
        self.size = size
        self.edges_path = Path(edges_path)
        self.landmarks_path = Path(landmarks_path)
        assert self.edges_path.exists()
        assert self.landmarks_path.exists()
        self.A = SingleFolderDataset(root_a, pipeline, with_path=True)
        self.B = SingleFolderDataset(root_b, pipeline, with_path=True)
        self.random_pair = random_pair
        self.with_path = with_path

    def get_edge(self, origin_path):
        op = Path(origin_path)
        if self.edge_type.startswith("landmark_"):
            edge_type = self.edge_type.lstrip("landmark_")
            use_landmark = op.parent.name.endswith("A")
        else:
            edge_type = self.edge_type
            use_landmark = False
        edge_path = self.edges_path / f"{op.parent.name}/{op.stem}.{edge_type}.png"
        origin_edge = F.to_tensor(Image.open(edge_path).resize(self.size, Image.BILINEAR))
        if not use_landmark:
            return origin_edge
        else:
            landmark_path = self.landmarks_path / f"{op.parent.name}/{op.stem}.txt"
            key_points, part_labels, part_edge = dlib_landmark.read_keypoints(landmark_path, size=self.size)

            dist_tensor = normalize_tensor(torch.from_numpy(dlib_landmark.dist_tensor(key_points, size=self.size)))
            part_labels = normalize_tensor(torch.from_numpy(part_labels))
            part_edge = torch.from_numpy(part_edge).unsqueeze(0).float()
            # edges = origin_edge * (part_labels.sum(0) == 0)  # remove edges within face
            # edges = part_edge + edges
            return torch.cat([origin_edge, part_edge, dist_tensor, part_labels])

    def __getitem__(self, idx):
        a_idx = idx % len(self.A)
        b_idx = idx % len(self.B) if not self.random_pair else torch.randint(len(self.B), (1,)).item()
        output = dict(a={}, b={})
        output["a"]["img"], output["a"]["path"] = self.A[a_idx]
        output["b"]["img"], output["b"]["path"] = self.B[b_idx]
        for p in "ab":
            output[p]["edge"] = self.get_edge(output[p]["path"])
        return output

    def __len__(self):
        return max(len(self.A), len(self.B))

    def __repr__(self):
        return f"<GenerationUnpairedDatasetWithEdge:\n\tA: {self.A}\n\tB: {self.B}>\nPipeline:\n{self.A.pipeline}"


@DATASET.register_module()
class PoseFacesWithSingleAnime(Dataset):
    def __init__(self, root_face, root_anime, landmark_path, num_face, face_pipeline, anime_pipeline, img_size,
                 with_order=True):
        self.num_face = num_face
        self.landmark_path = Path(landmark_path)
        self.with_order = with_order
        self.root_face = Path(root_face)
        self.root_anime = Path(root_anime)
        self.img_size = img_size
        self.face_samples = self.iter_folders()
        self.face_pipeline = transform_pipeline(face_pipeline)
        self.B = SingleFolderDataset(root_anime, anime_pipeline, with_path=True)

    def iter_folders(self):
        pics_per_person = defaultdict(list)
        for p in self.root_face.glob("*.jpg"):
            pics_per_person[p.stem[:7]].append(p.stem)
        data = []
        for p in pics_per_person:
            if len(pics_per_person[p]) >= self.num_face:
                if self.with_order:
                    data.extend(list(combinations(pics_per_person[p], self.num_face)))
                else:
                    data.extend(list(permutations(pics_per_person[p], self.num_face)))
        return data

    def read_pose(self, pose_txt):
        key_points, part_labels, part_edge = dlib_landmark.read_keypoints(pose_txt, size=self.img_size)
        dist_tensor = normalize_tensor(torch.from_numpy(dlib_landmark.dist_tensor(key_points, size=self.img_size)))
        part_labels = normalize_tensor(torch.from_numpy(part_labels))
        part_edge = torch.from_numpy(part_edge).unsqueeze(0).float()
        return torch.cat([part_labels, part_edge, dist_tensor])

    def __len__(self):
        return len(self.face_samples)

    def __getitem__(self, idx):
        output = dict()
        output["anime_img"], output["anime_path"] = self.B[torch.randint(len(self.B), (1,)).item()]
        for i, f in enumerate(self.face_samples[idx]):
            output[f"face_{i}"] = self.face_pipeline(self.root_face / f"{f}.jpg")
            output[f"pose_{i}"] = self.read_pose(self.landmark_path / self.root_face.name / f"{f}.txt")
            output[f"stem_{i}"] = f
        return output