import os
import sys
import numpy as np
import json 
import math
from sklearn.preprocessing import LabelBinarizer, LabelEncoder

import torch
from transformers import RobertaTokenizer, BertTokenizer
from torch.utils.data import Dataset
sys.path.append('/home/zekun/spatial_bert/spatial_bert/datasets')
from dataset_loader import SpatialDataset

import pdb


class USGS_MapDataset(SpatialDataset):
    def __init__(self, data_file_path,  tokenizer=None, max_token_len = 512, distance_norm_factor = 1, spatial_dist_fill=100, sep_between_neighbors = False):
        
        if tokenizer is None:
            self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        else:
            self.tokenizer = tokenizer

        self.max_token_len = max_token_len
        self.spatial_dist_fill = spatial_dist_fill # should be normalized distance fill, larger than all normalized neighbor distance
        self.sep_between_neighbors = sep_between_neighbors
        self.read_file(data_file_path)

        
        super(USGS_MapDataset, self).__init__(self.tokenizer , max_token_len , distance_norm_factor, sep_between_neighbors )
        
    def read_file(self, data_file_path):

        with open(data_file_path, 'r') as f:
            data = f.readlines()

        len_data = len(data)
        self.len_data = len_data
        self.data = data 


    def load_data(self, index):
        
        spatial_dist_fill = self.spatial_dist_fill
        line = self.data[index] # take one line from the input data according to the index

        line_data_dict = json.loads(line)

        # process pivot
        pivot_name = line_data_dict['info']['name']
        pivot_pos = line_data_dict['info']['geometry']

        neighbor_info = line_data_dict['neighbor_info']
        neighbor_name_list = neighbor_info['name_list']
        neighbor_geometry_list = neighbor_info['geometry_list']

        parsed_data = self.parse_spatial_context(pivot_name, pivot_pos, neighbor_name_list, neighbor_geometry_list, spatial_dist_fill )

        return parsed_data



    def __len__(self):
        return self.len_data

    def __getitem__(self, index):
        return self.load_data(index)