import numpy as np
import torch
from torch import nn
import sys
import util

class DataLoader(nn.Module):
    """Data loader module for training MCFlow
    Args:
        mode (int): Determines if we are loading training or testing data
        seed (int): Used to determine the fold for cross validation experiments or reproducibility consideration if not
        path (str): Determines the dataset to load
        drp_percent (float): Determines the binomial success rate for observing a feature
    """
    MODE_TRAIN = 0
    MODE_TEST = 1

    def __init__(self, mode=MODE_TRAIN, seed=0, path='news', drp_percent=0.5):

        self.path = path
        if path == 'mnist':
            self.original_tr, self.original_te, img_shape = util.path_to_matrix(path)
        else:
            matrix = util.path_to_matrix(path)
            self.matrix, self.maxs, self.mins = util.preprocess(matrix) #Preprocess according to the paper cited above
        if path == 'mnist':
            self.mask_tr, self.mask_te = util.create_img_dropout_masks(drp_percent, path, img_shape, len(self.original_tr), len(self.original_te))
            self.train, self.test = util.fill_img_missingness(self.original_tr, self.original_te, self.mask_tr, self.mask_te, img_shape, 0) #For now 0 represents nearest neighbor calc
        else:
            np.random.shuffle(self.matrix)
            np.random.seed(seed)
            self.mask = util.make_static_mask(drp_percent, seed, path, self.matrix) #check if the mask is there or not in this function
            self.original_tr, self.original_te = util.create_k_fold(self.matrix, seed)
            self.unique_values = []
            self.mask_tr, self.mask_te = util.create_k_fold_mask(seed, self.mask)
            trans = np.transpose(self.matrix)
            for r_idx, rows in enumerate(trans):
                row = []
                for c_idx, element in enumerate(rows):
                    if self.mask[c_idx][r_idx] == 0:
                        row.append(element)
                self.unique_values.append(np.asarray(row))
            self.train, self.test = util.fill_missingness(self.matrix, self.mask, self.unique_values, self.path, seed)
        self.mode = mode


    def reset_imputed_values(self, nn_model, nf_model, seed, args):

        random_mat = np.clip(util.inference_imputation_networks(nn_model, nf_model, self.train, args), 0, 1)
        self.train = (1-self.mask_tr) * self.original_tr + self.mask_tr * random_mat
        random_mat = np.clip(util.inference_imputation_networks(nn_model, nf_model, self.test, args), 0, 1)
        self.test = (1-self.mask_te) * self.original_te + self.mask_te * random_mat

    def reset_img_imputed_values(self, nn_model, nf_model, seed, args):

        util.inference_img_imputation_networks(nn_model, nf_model, self.train, self.mask_tr, self.original_tr, args)
        util.inference_img_imputation_networks(nn_model, nf_model, self.test, self.mask_te, self.original_te, args)

    def __len__(self):
        if self.mode==0:
            return len(self.train)
        elif self.mode==1:
            return len(self.test)
        else:
            print("Data loader mode error -- acceptable modes are 0,1,2")
            sys.exit()

    def __getitem__(self, idx):
        if self.mode==0:
            return torch.Tensor(self.train[idx]) , (torch.Tensor(self.original_tr[idx]), torch.Tensor(self.mask_tr[idx]))
        elif self.mode==1:
            return torch.Tensor(self.test[idx]) , (torch.Tensor(self.original_te[idx]), torch.Tensor(self.mask_te[idx]))
        else:
            print("Data loader mode error -- acceptable modes are 0,1,2")
            sys.exit()

import numpy as np
import torch
from torch import nn
import sys
import util

class DataLoader(nn.Module):
    """Data loader module for training MCFlow
    Args:
        mode (int): Determines if we are loading training or testing data
        seed (int): Used to determine the fold for cross validation experiments or reproducibility consideration if not
        path (str): Determines the dataset to load
        drp_percent (float): Determines the binomial success rate for observing a feature
    """
    MODE_TRAIN = 0
    MODE_TEST = 1

    def __init__(self, mode=MODE_TRAIN, seed=0, path='news', drp_percent=0.5):

        self.path = path
        if path == 'mnist':
            self.original_tr, self.original_te, img_shape = util.path_to_matrix(path)
        else:
            matrix = util.path_to_matrix(path)
            self.matrix, self.maxs, self.mins = util.preprocess(matrix) #Preprocess according to the paper cited above
        if path == 'mnist':
            self.mask_tr, self.mask_te = util.create_img_dropout_masks(drp_percent, path, img_shape, len(self.original_tr), len(self.original_te))
            self.train, self.test = util.fill_img_missingness(self.original_tr, self.original_te, self.mask_tr, self.mask_te, img_shape, 0) #For now 0 represents nearest neighbor calc
        else:
            np.random.shuffle(self.matrix)
            np.random.seed(seed)
            self.mask = util.make_static_mask(drp_percent, seed, path, self.matrix) #check if the mask is there or not in this function
            self.original_tr, self.original_te = util.create_k_fold(self.matrix, seed)
            self.unique_values = []
            self.mask_tr, self.mask_te = util.create_k_fold_mask(seed, self.mask)
            trans = np.transpose(self.matrix)
            for r_idx, rows in enumerate(trans):
                row = []
                for c_idx, element in enumerate(rows):
                    if self.mask[c_idx][r_idx] == 0:
                        row.append(element)
                self.unique_values.append(np.asarray(row))
            self.train, self.test = util.fill_missingness(self.matrix, self.mask, self.unique_values, self.path, seed)
        self.mode = mode


    def reset_imputed_values(self, nn_model, nf_model, seed, args):

        random_mat = np.clip(util.inference_imputation_networks(nn_model, nf_model, self.train, args), 0, 1)
        self.train = (1-self.mask_tr) * self.original_tr + self.mask_tr * random_mat
        random_mat = np.clip(util.inference_imputation_networks(nn_model, nf_model, self.test, args), 0, 1)
        self.test = (1-self.mask_te) * self.original_te + self.mask_te * random_mat

    def reset_img_imputed_values(self, nn_model, nf_model, seed, args):

        util.inference_img_imputation_networks(nn_model, nf_model, self.train, self.mask_tr, self.original_tr, args)
        util.inference_img_imputation_networks(nn_model, nf_model, self.test, self.mask_te, self.original_te, args)

    def __len__(self):
        if self.mode==0:
            return len(self.train)
        elif self.mode==1:
            return len(self.test)
        else:
            print("Data loader mode error -- acceptable modes are 0,1,2")
            sys.exit()

    def __getitem__(self, idx):
        if self.mode==0:
            return torch.Tensor(self.train[idx]) , (torch.Tensor(self.original_tr[idx]), torch.Tensor(self.mask_tr[idx]))
        elif self.mode==1:
            return torch.Tensor(self.test[idx]) , (torch.Tensor(self.original_te[idx]), torch.Tensor(self.mask_te[idx]))
        else:
            print("Data loader mode error -- acceptable modes are 0,1,2")
            sys.exit()

Data loader module for training MCFlow     Args:         mode (int): Determines if we are loading training or testing data         seed (int): Used to determine the fold for cross validation experiments or reproducibility consideration if not         path (str): Determines the dataset to load         drp_percent (float): Determines the binomial success rate for observing a feature #Preprocess according to the paper cited above #For now 0 represents nearest neighbor calc #check if the mask is there or not in this function