#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# T0: Address all TODOs and remove all explanatory comments
""" TO: Add a description here.
"""


import csv
import json
import os
from typing import List
import datasets
import logging
import pandas as pd
import torch

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """
This typical dataset contains all the building permits issued or in progress 
within the city of Seattle starting from 1990 to recent, and this dataset is 
still updating as time flows. Information includes permit records urls, 
detailed address, and building costs etc.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://data.seattle.gov/Permitting/Building-Permits/76t5-zqzr/about_data"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "	http://www.seattle.gov/sdci"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://data.seattle.gov/Permitting/Building-Permits/76t5-zqzr/about_data"
_URLS = {
  "train": "https://github.com/HathawayLiu/Housing_dataset/raw/main/housing_train_dataset.csv",
  "test": "https://github.com/HathawayLiu/Housing_dataset/raw/main/housing_test_dataset.csv",
}

# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class HousingDataset(datasets.GeneratorBasedBuilder):
    """This dataset contains all building permits issued or in progress within 
    the city of Seattle. It includes the original columns in the datasets, with
    new added columns for corresponding neighborhood district and parking lot 
    near by each housing."""

    _URLS = _URLS
    VERSION = datasets.Version("1.1.0")

    def _info(self):
      return datasets.DatasetInfo(
          description=_DESCRIPTION,
          features=datasets.Features(
              {
                  # columns from original dataset
                  "permitnum": datasets.Value("string"),
                  "permitclass": datasets.Value("string"),
                  "permitclassmapped": datasets.Value("string"),
                  "permittypemapped": datasets.Value("string"),
                  "permittypedesc": datasets.Value("string"),
                  "description": datasets.Value("string"),
                  "housingunits": datasets.Value("int64"),
                  "housingunitsremoved": datasets.Value("int64"),
                  "housingunitsadded": datasets.Value("int64"),
                  "estprojectcost": datasets.Value("float32"),
                  "applieddate": datasets.Value("string"),
                  "issueddate": datasets.Value("string"),
                  "expiresdate": datasets.Value("string"),
                  "completeddate": datasets.Value("string"),
                  "statuscurrent": datasets.Value("string"),
                  "relatedmup": datasets.Value("string"),
                  "originaladdress1": datasets.Value("string"),
                  "originalcity": datasets.Value("string"),
                  "originalstate": datasets.Value("string"),
                  "originalzip": datasets.Value("int64"),
                  "contractorcompanyname": datasets.Value("string"),
                  "link": datasets.Value("string"),
                  "latitude": datasets.Value("float32"),
                  "longitude": datasets.Value("float32"),
                  "location1": datasets.Value("string"),
                  # new added columns below
                  "neighbordistrict": datasets.Value("string")
              }
          ),
          # No default supervised_keys (as we have to pass both question
          # and context as input).
          supervised_keys=None,
          homepage=_HOMEPAGE,
          citation=_CITATION,
      )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
      urls = self._URLS
      downloaded_files = dl_manager.download_and_extract(urls)

      return [
          datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
          datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
      ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logging.info("generating examples from = %s", filepath)
        with open(filepath) as f:
          housing_df = pd.read_csv(f)

          housing_df['EstProjectCost'] = housing_df["EstProjectCost"].replace('NA', 0)
          housing_df.dropna(subset = ['Latitude'], inplace = True)
          housing_df.dropna(subset = ['OriginalZip'], inplace = True)

          housing_df['Latitude'] = housing_df['Latitude'].astype(float)
          housing_df['Longitude'] = housing_df['Longitude'].astype(float)

          # Iterating through each row to generate examples
          for index, row in housing_df.iterrows():
              yield index, {
                  "permitnum": row.get("PermitNum", ""),
                  "permitclass": row.get("PermitClass", ""),
                  "permitclassmapped": row.get("PermitClassMapped", ""),
                  "permittypemapped": row.get("PermitTypeMapped", ""),
                  "permittypedesc": row.get("PermitTypeDesc", ""),
                  "description": row.get("Description", ""),
                  "housingunits": int(row.get("HousingUnits", "")),
                  "housingunitsremoved": int(row.get("HousingUnitsRemoved", "")),
                  "housingunitsadded": int(row.get("HousingUnitsAdded", "")),
                  "estprojectcost": float(row.get("EstProjectCost", "")),
                  "applieddate": str(row.get("AppliedDate", "")),
                  "issueddate": str(row.get("IssuedDate", "")),
                  "expiresdate": str(row.get("ExpiresDate", "")),
                  "completeddate": str(row.get("CompletedDate", "")),
                  "statuscurrent": row.get("StatusCurrent", ""),
                  "relatedmup": row.get("RelatedMup", ""),
                  "originaladdress1": row.get("OriginalAddress1", ""),
                  "originalcity": row.get("OriginalCity", ""),
                  "originalstate": row.get("OriginalState", ""),
                  "originalzip": int(row.get("OriginalZip", "")),
                  "contractorcompanyname": row.get("ContractorCompanyName", ""),
                  "link": row.get("Link", ""),
                  "latitude": row["Latitude"],
                  "longitude": row["Longitude"],
                  "location1": str(row["Latitude"]) + ", " + str(row["Longitude"]),
                  "neighbordistrict": row.get("NeighborDistrict", "")
              }