"""
PyTorch compatible dataloader

"""


import math
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from sklearn.model_selection import StratifiedKFold
import dgl


# default collate function
def collate(samples):
    # The input `samples` is a list of pairs (graph, label).
    graphs, labels = map(list, zip(*samples))
    for g in graphs:
        # deal with node feats
        for feat in g.node_attr_schemes().keys():
            # TODO torch.Tensor is not recommended
            # torch.DoubleTensor and torch.tensor
            # will meet error in executor.py@runtime line 472, tensor.py@backend line 147
            # RuntimeError: expected type torch.cuda.DoubleTensor but got torch.cuda.FloatTensor
            g.ndata[feat] = torch.Tensor(g.ndata[feat])
        # no edge feats
    batched_graph = dgl.batch(graphs)
    labels = torch.tensor(labels)
    return batched_graph, labels


class GraphDataLoader():
    def __init__(self,
                 dataset,
                 batch_size,
                 device,
                 collate_fn=collate,
                 seed=0,
                 shuffle=True,
                 split_name='fold10',
                 fold_idx=0,
                 split_ratio=0.7):

        self.shuffle = shuffle
        self.seed = seed
        self.kwargs = {'pin_memory': True} if 'cuda' in device.type else {}

        labels = [l for _, l in dataset]

        if split_name == 'fold10':
            train_idx, valid_idx = self._split_fold10(
                labels, fold_idx, seed, shuffle)
        elif split_name == 'rand':
            train_idx, valid_idx = self._split_rand(
                labels, split_ratio, seed, shuffle)
        else:
            raise NotImplementedError()

        train_sampler = SubsetRandomSampler(train_idx)
        valid_sampler = SubsetRandomSampler(valid_idx)

        self.train_loader = DataLoader(
            dataset, sampler=train_sampler,
            batch_size=batch_size, collate_fn=collate, **self.kwargs)
        self.valid_loader = DataLoader(
            dataset, sampler=valid_sampler,
            batch_size=batch_size, collate_fn=collate, **self.kwargs)

    def train_valid_loader(self):
        return self.train_loader, self.valid_loader

    def _split_fold10(self, labels, fold_idx=0, seed=0, shuffle=True):
        ''' 10 flod '''
        assert 0 <= fold_idx and fold_idx < 10, print(
            "fold_idx must be from 0 to 9.")

        idx_list = []
        skf = StratifiedKFold(n_splits=10, shuffle=shuffle, random_state=seed)
        idx_list = []
        for idx in skf.split(np.zeros(len(labels)), labels):    # split(x, y)
            idx_list.append(idx)
        train_idx, valid_idx = idx_list[fold_idx]

        print(
            "train_set : test_set = %d : %d",
            len(train_idx), len(valid_idx))

        return train_idx, valid_idx

    def _split_rand(self, labels, split_ratio=0.7, seed=0, shuffle=True):
        num_entries = len(labels)
        indices = list(range(num_entries))
        np.random.seed(seed)
        np.random.shuffle(indices)
        split = int(math.floor(split_ratio * num_entries))
        train_idx, valid_idx = indices[:split], indices[split:]

        print(
            "train_set : test_set = %d : %d",
            len(train_idx), len(valid_idx))

        return train_idx, valid_idx

