import torch
from tqdm import tqdm
from lightning import LightningDataModule
from datasets import load_dataset
import os
from torch.utils.data import DataLoader, TensorDataset
from torch.nn.utils.rnn import pad_sequence


class PointDataModule(LightningDataModule):
    def __init__(
        self,
        hf_repo: str,
        split_name: str,
        train_size: float = 0.2,
        val_size: float = 0.1,
        test_size: float = 0.1,
        batch_size: int = 32,
        remove_boundary_edges: bool = True,
    ):
        super().__init__()
        self.hf_repo = hf_repo
        self.split_name = split_name
        self.dataset_dict = {}
        self.geometry_dict = {}
        self.train_size = train_size
        self.val_size = val_size
        self.test_size = test_size
        self.batch_size = batch_size
        self.remove_boundary_edges = remove_boundary_edges

    def prepare_data(self):
        dataset = load_dataset(self.hf_repo, name="snapshots")[self.split_name]
        geometry = load_dataset(self.hf_repo, name="geometry")[self.split_name]

        total_len = len(dataset)
        train_len = int(self.train_size * total_len)
        valid_len = int(self.val_size * total_len)
        self.dataset_dict = {
            "train": dataset.select(range(0, train_len)),
            "val": dataset.select(range(train_len, train_len + valid_len)),
            "test": dataset.select(range(train_len + valid_len, total_len)),
        }
        self.geometry_dict = {
            "train": geometry.select(range(0, train_len)),
            "val": geometry.select(range(train_len, train_len + valid_len)),
            "test": geometry.select(range(train_len + valid_len, total_len)),
        }

    def _compute_boundary_mask(
        self, bottom_ids, right_ids, top_ids, left_ids, temperature
    ):
        left_ids = left_ids[~torch.isin(left_ids, bottom_ids)]
        right_ids = right_ids[~torch.isin(right_ids, bottom_ids)]
        left_ids = left_ids[~torch.isin(left_ids, top_ids)]
        right_ids = right_ids[~torch.isin(right_ids, top_ids)]

        bottom_bc = temperature[bottom_ids].median()
        bottom_bc_mask = torch.ones(len(bottom_ids)) * bottom_bc
        left_bc = temperature[left_ids].median()
        left_bc_mask = torch.ones(len(left_ids)) * left_bc
        right_bc = temperature[right_ids].median()
        right_bc_mask = torch.ones(len(right_ids)) * right_bc

        boundary_values = torch.cat(
            [bottom_bc_mask, right_bc_mask, left_bc_mask], dim=0
        )
        boundary_mask = torch.cat([bottom_ids, right_ids, left_ids], dim=0)

        return boundary_mask, boundary_values

    def _build_dataset(
        self,
        snapshot: dict,
        geometry: dict,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        conductivity = torch.tensor(
            snapshot["conductivity"], dtype=torch.float32
        )
        temperature = torch.tensor(snapshot["temperature"], dtype=torch.float32)
        pos = torch.tensor(geometry["points"], dtype=torch.float32)[:, :2]
        bottom_ids = torch.tensor(
            geometry["bottom_boundary_ids"], dtype=torch.long
        )
        top_ids = torch.tensor(geometry["top_boundary_ids"], dtype=torch.long)
        left_ids = torch.tensor(geometry["left_boundary_ids"], dtype=torch.long)
        right_ids = torch.tensor(
            geometry["right_boundary_ids"], dtype=torch.long
        )

        boundary_mask, boundary_values = self._compute_boundary_mask(
            bottom_ids, right_ids, top_ids, left_ids, temperature
        )

        x = torch.zeros_like(temperature, dtype=torch.float32).unsqueeze(-1)
        x[boundary_mask] = boundary_values.unsqueeze(-1)
        x = torch.cat([x, conductivity.unsqueeze(-1), pos], dim=-1)
        return x, temperature.unsqueeze(-1)

    def setup(self, stage: str = None):
        if stage == "fit" or stage is None:
            x = []
            y = []
            for snap, geom in tqdm(
                zip(self.dataset_dict["train"], self.geometry_dict["train"]),
                desc="Building train graphs",
                total=len(self.dataset_dict["train"]),
            ):
                x_i, y_i = self._build_dataset(snap, geom)
                x.append(x_i)
                y.append(y_i)
            self.train_dataset = TensorDataset(
                pad_sequence(x, batch_first=True, padding_value=-1),
                pad_sequence(y, batch_first=True, padding_value=-1),
            )

            for snap, geom in tqdm(
                zip(self.dataset_dict["val"], self.geometry_dict["val"]),
                desc="Building val graphs",
                total=len(self.dataset_dict["val"]),
            ):
                x_i, y_i = self._build_dataset(snap, geom)
                x.append(x_i)
                y.append(y_i)
            self.val_dataset = TensorDataset(
                pad_sequence(x, batch_first=True, padding_value=-1),
                pad_sequence(y, batch_first=True, padding_value=-1),
            )

        if stage == "test" or stage is None:
            x = []
            y = []
            for snap, geom in tqdm(
                zip(self.dataset_dict["test"], self.geometry_dict["test"]),
                desc="Building test graphs",
                total=len(self.dataset_dict["test"]),
            ):
                x_i, y_i = self._build_dataset(snap, geom)
                x.append(x_i)
                y.append(y_i)
            self.test_data = TensorDataset(
                pad_sequence(x, batch_first=True, padding_value=-1),
                pad_sequence(y, batch_first=True, padding_value=-1),
            )

    def train_dataloader(self):
        return DataLoader(
            self.train_dataset,
            batch_size=self.batch_size,
            shuffle=True,
            num_workers=8,
            pin_memory=True,
        )

    def val_dataloader(self):
        return DataLoader(
            self.val_dataset,
            batch_size=self.batch_size,
            shuffle=False,
            num_workers=8,
            pin_memory=True,
        )

    def test_dataloader(self):
        return DataLoader(
            self.test_data,
            batch_size=self.batch_size,
            shuffle=False,
            num_workers=8,
            pin_memory=True,
        )