# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# 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 new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# 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)
_URLS = {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
        datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
    ]

    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "year": datasets.Value("int32"),
                    "locationabbr": datasets.Value("string"),
                    "locationdesc": datasets.Value("string"),
                    "geolocation": datasets.Features({"latitude": datasets.Value("float32"), "longitude": datasets.Value("float32")}),
                    "disease_type": datasets.Value("int32"),
                    "data_value_type": datasets.Value("int32"),
                    "data_value": datasets.Value("float32"),
                    "break_out_category": datasets.Value("string"),
                    "break_out_details": datasets.Value("string"),
                    "break_out_type": datasets.Value("int32"),
                    "life_expectancy": datasets.Value("float32")
                    # These are the features of your dataset like images, labels ...
                }
            )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        dl_paths = dl_manager.download_and_extract({
            'train_csv': 'https://drive.google.com/file/d/1eChYmZ3RMq1v-ek1u6DD2m_dGIrz3sbi/view?usp=sharing'
        })
        return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "csvpath": dl_paths['train_csv'],
            },
        ),
    ]

    def _generate_examples(self, csvpath):
        with open(csvpath, encoding="utf-8") as f:
            reader = csv.DictReader(f)  

        for key, row in enumerate(reader):
            yield key, {
                "year": int(row["Year"]),
                "location_abbr": row["LocationAbbr"],
                "location_desc": row["LocationDesc"],
                "geolocation": {
                    "latitude": float(row["latitude"]),
                    "longitude": float(row["longitude"])
                },
                "disease_type": int(row["Disease_Type"]),
                "data_value_type": int(row["Data_Value_Type"]),
                "data_value": float(row["Data_Value"]),
                "break_out_category": row["Break_Out_Category"],
                "break_out_details": row["Break_Out_Details"],
                "break_out_type": int(row["Break_Out_Type"]),
                "life_expectancy": float(row["Life_Expectancy"]) if row["Life_Expectancy"] else None
            }
      
    @staticmethod
    def preprocess_data(filepath):
        data = pd.read_csv("https://drive.google.com/file/d/1ktRNl7jg0Z83rkymD9gcsGLdVqVaFtd-/view?usp=drive_link")
        data = data[['YearStart', 'LocationAbbr', 'LocationDesc', 'Geolocation', 'Topic', 'Question', 'Data_Value_Type', 'Data_Value', 'Data_Value_Alt', 
                     'Low_Confidence_Limit', 'High_Confidence_Limit', 'Break_Out_Category', 'Break_Out']]
    def convert_to_tuple(geo_str):
        if isinstance(geo_str, str):
          geo_str = geo_str.replace('POINT (', '').replace(')', '')
          lon, lat = map(float, geo_str.split())
            return (lon, lat)
        else:
            return geo_str
      
        data['Geolocation'] = data['Geolocation'].apply(convert_to_tuple)
        disease_columns = [
          'Major cardiovascular disease mortality rate among US adults (18+); NVSS',
          'Diseases of the heart (heart disease) mortality rate among US adults (18+); NVSS',
          'Acute myocardial infarction (heart attack) mortality rate among US adults (18+); NVSS',
          'Coronary heart disease mortality rate among US adults (18+); NVSS',
          'Heart failure mortality rate among US adults (18+); NVSS',
          'Cerebrovascular disease (stroke) mortality rate among US adults (18+); NVSS',
          'Ischemic stroke mortality rate among US adults (18+); NVSS',
          'Hemorrhagic stroke mortality rate among US adults (18+); NVSS'
          ]
      
        disease_column_mapping = {column_name: index for index, column_name in enumerate(disease_columns)}
        data['Question'] = data['Question'].apply(lambda x: disease_column_mapping.get(x, -1))
      
        sex_columns = ['Male', 'Female']
        sex_column_mapping = {column_name: index + 1 for index, column_name in enumerate(sex_columns)}
      
        age_columns = ['18-24', '25-44', '45-64', '65+']
        age_column_mapping = {column_name: index + 1 for index, column_name in enumerate(age_columns)}
      
        race_columns = ['Non-Hispanic White', 'Non-Hispanic Black', 'Hispanic', 'Other']
        race_column_mapping = {column_name: index + 1 for index, column_name in enumerate(race_columns)}
      
        def map_break_out_category(value):
            if value in sex_column_mapping:
                return sex_column_mapping[value]
            elif value in age_column_mapping:
                return age_column_mapping[value]
            elif value in race_column_mapping:
                return race_column_mapping[value]
            else:
                return value
      
        data['Break_Out_Type'] = data['Break_Out'].apply(map_break_out_category)
        data.drop(columns=['Topic', 'Low_Confidence_Limit', 'High_Confidence_Limit', 'Data_Value_Alt'], axis=1, inplace=True)
        data['Data_Value_Type'] = data['Data_Value_Type'].apply(lambda x: 1 if x == 'Age-Standardized' else 0)
        data.rename(columns={'Question':'Disease_Type', 'YearStart':'Year', 'Break_Out':'Break_Out_Details'}, inplace=True)
        data['Break_Out_Type'] = data['Break_Out_Type'].replace('Overall', 0)
      
        lt2000 = pd.read_csv("https://drive.google.com/file/d/1ktRNl7jg0Z83rkymD9gcsGLdVqVaFtd-/view?usp=drive_link")
        lt2000 = lt2000[(lt2000['race_name'] == 'Total') & (lt2000['age_name'] == '<1 year')]
        lt2000 = lt2000[['location_name', 'val']]
        lt2000.rename(columns={'val':'Life_Expectancy'}, inplace=True)
      
        lt2005 = pd.read_csv("https://drive.google.com/file/d/1xZqeOgj32-BkOhDTZVc4k_tp1ddnOEh7/view?usp=drive_link")
        lt2005 = lt2005[(lt2005['race_name'] == 'Total') & (lt2005['age_name'] == '<1 year')]
        lt2005 = lt2005[['location_name', 'val']]
        lt2005.rename(columns={'val':'Life_Expectancy'}, inplace=True)
      
        lt2010 = pd.read_csv("https://drive.google.com/file/d/1ItqHBuuUa38PVytfahaAV8NWwbhHMMg8/view?usp=drive_link")
        lt2010 = lt2010[(lt2010['race_name'] == 'Total') & (lt2010['age_name'] == '<1 year')]
        lt2010 = lt2010[['location_name', 'val']]
        lt2010.rename(columns={'val':'Life_Expectancy'}, inplace=True)
      
        lt2015 = pd.read_csv("https://drive.google.com/file/d/1rOgQY1RQiry2ionTKM_UWgT8cYD2E0vX/view?usp=drive_link")
        lt2015 = lt2015[(lt2015['race_name'] == 'Total') & (lt2015['age_name'] == '<1 year')]
        lt2015 = lt2015[['location_name', 'val']]
        lt2015.rename(columns={'val':'Life_Expectancy'}, inplace=True)
      
        lt_data = pd.concat([lt2000, lt2005, lt2010, lt2015])
        lt_data.drop_duplicates(subset=['location_name'], inplace=True)
      
        data2 = pd.merge(data, lt_data, how='inner', left_on='LocationDesc', right_on='location_name')
        data2.drop(columns=['location_name'], axis=1, inplace=True)
        data2 = data2[(data2['Break_Out_Details'] != '75+') & (data2['Break_Out_Details'] != '35+')]
        data2.rename(columns={'Question':'Disease_Type'}, inplace=True)
        data2['Life_Expectancy'] = np.where(data2['Break_Out_Type'] == 0, data2['Life_Expectancy'], np.nan)
        processed_filepath = '/content/drive/MyDrive/my_processed_data.csv'
        return processed_filepath