import numpy as np
import os
import torch


def data_provider(args):
    data = {}
    for category in ['train', 'val', 'test']:

        cat_data = np.load(os.path.join(args.root_path, args.data_path, category + '.npz'),allow_pickle=True)

        data['x_' + category] = torch.Tensor(cat_data['x'].astype(float)).to(torch.device(args.device))
        data['y_' + category] = torch.Tensor(cat_data['y'].astype(float)).to(torch.device(args.device))

    data['train_loader'] = Data_Loader(data['x_train'], data['y_train'], args.batch_size)
    data['val_loader'] = Data_Loader(data['x_val'], data['y_val'], args.batch_size)
    data['test_loader'] = Data_Loader(data['x_test'], data['y_test'], args.batch_size)
    train_loader = data['train_loader']
    vali_loader = data['val_loader']
    test_loader = data['test_loader']

    return train_loader, vali_loader, test_loader




class Data_Loader(object):
    def __init__(self, xs, ys, batch_size, pad_with_last_sample=False):
        """
        :param xs:
        :param ys:
        :param batch_size:
        :param pad_with_last_sample: pad with the last sample to make number of samples divisible to batch_size.
        """
        self.batch_size = batch_size
        self.current_ind = 0
        if pad_with_last_sample:
            num_padding = (batch_size - (len(xs) % batch_size)) % batch_size
            x_padding = np.repeat(xs[-1:], num_padding, axis=0)
            y_padding = np.repeat(ys[-1:], num_padding, axis=0)
            xs = np.concatenate([xs, x_padding], axis=0)
            ys = np.concatenate([ys, y_padding], axis=0)
        self.size = len(xs)
        self.num_batch = int(self.size // self.batch_size)
        self.xs = xs
        self.ys = ys

    def shuffle(self):
        permutation = np.random.permutation(self.size)
        xs, ys = self.xs[permutation], self.ys[permutation]
        self.xs = xs
        self.ys = ys

    def get_iterator(self):
        self.current_ind = 0

        def _wrapper():
            while self.current_ind < self.num_batch:
                start_ind = self.batch_size * self.current_ind
                end_ind = min(self.size, self.batch_size * (self.current_ind + 1))
                x_i = self.xs[start_ind: end_ind, ...]
                y_i = self.ys[start_ind: end_ind, ...]
                yield (x_i, y_i)
                self.current_ind += 1

        return _wrapper()


class StandardScaler():
    """
    Standard the input
    """

    def __init__(self, mean, std):
        self.mean = mean
        self.std = std

    def transform(self, data):
        return (data - self.mean) / self.std

    def inverse_transform(self, data):
        return (data * self.std) + self.mean




class Data_Loader(object):
    def __init__(self, xs, ys, batch_size, pad_with_last_sample=False):
        """
        :param xs:
        :param ys:
        :param batch_size:
        :param pad_with_last_sample: pad with the last sample to make number of samples divisible to batch_size.
        """
        self.batch_size = batch_size
        self.current_ind = 0
        if pad_with_last_sample:
            num_padding = (batch_size - (len(xs) % batch_size)) % batch_size
            x_padding = np.repeat(xs[-1:], num_padding, axis=0)
            y_padding = np.repeat(ys[-1:], num_padding, axis=0)
            xs = np.concatenate([xs, x_padding], axis=0)
            ys = np.concatenate([ys, y_padding], axis=0)
        self.size = len(xs)
        self.num_batch = int(self.size // self.batch_size)
        self.xs = xs
        self.ys = ys

    def shuffle(self):
        permutation = np.random.permutation(self.size)
        xs, ys = self.xs[permutation], self.ys[permutation]
        self.xs = xs
        self.ys = ys

    def get_iterator(self):
        self.current_ind = 0

        def _wrapper():
            while self.current_ind < self.num_batch:
                start_ind = self.batch_size * self.current_ind
                end_ind = min(self.size, self.batch_size * (self.current_ind + 1))
                x_i = self.xs[start_ind: end_ind, ...]
                y_i = self.ys[start_ind: end_ind, ...]
                yield (x_i, y_i)
                self.current_ind += 1

        return _wrapper()