"""NBFI"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "ID",
    "Client_Income",
    "Car_Owned",
    "Bike_Owned",
    "Active_Loan",
    "House_Own",
    "Child_Count",
    "Credit_Amount",
    "Loan_Annuity",
    "Accompany_Client",
    "Client_Income_Type",
    "Client_Education",
    "Client_Marital_Status",
    "Client_Gender",
    "Loan_Contract_Type",
    "Client_Housing_Type",
    "Population_Region_Relative",
    "Age_Days",
    "Employed_Days",
    "Registration_Days",
    "ID_Days",
    "Own_House_Age",
    "Mobile_Tag",
    "Homephone_Tag",
    "Workphone_Working",
    "Client_Occupation",
    "Client_Family_Members",
    "Cleint_City_Rating",
    "Application_Process_Day",
    "Application_Process_Hour",
    "Client_Permanent_Match_Tag",
    "Client_Contact_Work_Tag",
    "Type_Organization",
    "Score_Source_1",
    "Score_Source_2",
    "Score_Source_3",
    "Social_Circle_Default",
    "Phone_Change",
    "Credit_Bureau",
    "Default"
]
_BASE_FEATURE_NAMES = [
    "income",
    "owns_a_car",
    "owns_a_bike",
    "has_an_active_loan",
    "owns_a_house",
    "nr_children",
    "credit",
    "loan_annuity",
    "accompanied_by",
    "income_type",
    "education_level",
    "marital_status",
    "is_male",
    "type_of_contract",
    "type_of_housing",
    "residence_density",
    "age_in_days",
    "consecutive_days_of_employment",
    "nr_days_since_last_registration_change",
    "nr_days_since_last_document_change",
    "has_provided_a_mobile_number",
    "has_provided_a_home_number",
    "was_reachable_at_work",
    "job",
    "nr_family_members",
    "city_rating",
    "weekday_of_application",
    "hour_of_application",
    "same_residence_and_home",
    "same_work_and_home",
    "score_1",
    "score_2",
    "score_3",
    "nr_defaults_in_social_circle",
    "inquiries_in_last_year",
    "has_defaulted"
]
features_types_per_config = {
    "default": {
        "income": datasets.Value("float32"),
        "owns_a_car": datasets.Value("bool"),
        "owns_a_bike": datasets.Value("bool"),
        "has_an_active_loan": datasets.Value("bool"),
        "owns_a_house": datasets.Value("bool"),
        "nr_children": datasets.Value("int8"),
        "credit": datasets.Value("float32"),
        "loan_annuity": datasets.Value("float32"),
        "accompanied_by": datasets.Value("string"),
        "income_type": datasets.Value("string"),
        "education_level": datasets.Value("float32"),
        "marital_status": datasets.Value("string"),
        "is_male": datasets.Value("bool"),
        "type_of_contract": datasets.Value("string"),
        "type_of_housing": datasets.Value("string"),
        "residence_density": datasets.Value("float32"),
        "age_in_days": datasets.Value("int32"),
        "consecutive_days_of_employment": datasets.Value("int16"),
        "nr_days_since_last_registration_change": datasets.Value("int32"),
        "nr_days_since_last_document_change": datasets.Value("int32"),
        "has_provided_a_mobile_number": datasets.Value("bool"),
        "has_provided_a_home_number": datasets.Value("bool"),
        "was_reachable_at_work": datasets.Value("bool"),
        "job": datasets.Value("string"),
        "nr_family_members": datasets.Value("int8"),
        "city_rating": datasets.Value("int8"),
        "weekday_of_application": datasets.Value("int8"),
        "hour_of_application": datasets.Value("float32"),
        "same_residence_and_home": datasets.Value("bool"),
        "same_work_and_home": datasets.Value("bool"),
        "score_1": datasets.Value("float32"),
        "score_2": datasets.Value("float32"),
        "score_3": datasets.Value("float32"),
        "nr_defaults_in_social_circle": datasets.Value("bool"),
        "inquiries_in_last_year": datasets.Value("float32"),
        "has_defaulted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    }
}
_ENCODING_DICS = {}
_EDUCATION_ENCODING = {
    "Junior secondary": 0,
    "Secondary": 1,
    "Graduation dropout": 2,
    "Graduation": 2,
    "Post Grad": 4
}    

DESCRIPTION = "NBFI dataset from default prediction."
_HOMEPAGE = "https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset"
_URLS = ("https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset")
_CITATION = """"""

# Dataset info
urls_per_split = {
    "train": "https://gist.githubusercontent.com/msetzu/6c83dc3b7092d428ae2f08dc91e1020c/raw/9fc3171b293d0dc29963357450308eb4c7e3a15b/Train_Dataset.csv"
}

features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class NBFIConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(NBFIConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class NBFI(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "default"
    BUILDER_CONFIGS = [
        NBFIConfig(name="default",
                    description="NBFI for default binary classification.")
    ]


    def _info(self):
        if self.config.name not in features_per_config:
            raise ValueError(f"Unknown configuration: {self.config.name}")
        
        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                    features=features_per_config[self.config.name])

        return info
    
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        downloads = dl_manager.download_and_extract(urls_per_split)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
        ]
    
    def _generate_examples(self, filepath: str):
        if self.config.name == "default":
            data = pandas.read_csv(filepath)
            data = self.preprocess(data, config=self.config.name)

            for row_id, row in data.iterrows():
                data_row = dict(row)

                yield row_id, data_row
        else:
            raise ValueError(f"Unknown config: {self.config.name}")



    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
        data.drop("ID", axis="columns", inplace=True)
        data.drop("Own_House_Age", axis="columns", inplace=True)
        data.drop("Type_Organization", axis="columns", inplace=True)
        data.drop("Phone_Change", axis="columns", inplace=True)

        data = data[~data.Client_Income.isna()]
        data = data[~data.Client_Education.isna()]
        data = data[~data.Child_Count.isna()]
        data = data[~data.Client_Marital_Status.isna()]
        data = data[~data.Client_Gender.isna()]
        data = data[~data.Loan_Contract_Type.isna()]
        data = data[~data.Client_Housing_Type.isna()]
        data = data[~data.Age_Days.isna()]
        data = data[~data.Employed_Days.isna()]
        data = data[~data.Registration_Days.isna()]
        data = data[~data.ID_Days.isna()]
        data = data[~data.Cleint_City_Rating.isna()]
        data = data[~data.Application_Process_Day.isna()]
        data = data[~data.Application_Process_Hour.isna()]
        data = data[~data.Client_Permanent_Match_Tag.isna()]
        data = data[~data.Client_Contact_Work_Tag.isna()]
        data = data[~data.Score_Source_1.isna()]
        data = data[~data.Score_Source_2.isna()]
        data = data[~data.Score_Source_3.isna()]
        data = data[~data.Credit_Bureau.isna()]
        data = data[~data.Credit_Amount.isna()]
        data = data[~data.Loan_Annuity.isna()]
        data = data[~data.Accompany_Client.isna()]
        data = data[~data.Client_Occupation.isna()]
        data = data[~data.Client_Family_Members.isna()]
        data = data[~data.Social_Circle_Default.isna()]
        data = data[~data.Population_Region_Relative.isin(("@", "#"))]
        data = data[~data.Population_Region_Relative.isna()]
        data = data[data.Loan_Annuity != "#VALUE!"]
        data = data[data.Age_Days != "x"]
        data = data[data.Employed_Days != "x"]
        data = data[data.Registration_Days != "x"]
        data = data[data.ID_Days != "x"]
        
        data.columns = _BASE_FEATURE_NAMES

        data["education_level"] = data["education_level"].apply(lambda x: _EDUCATION_ENCODING[x])
        data["is_male"] = data["is_male"].apply(lambda x: x == "M")
        data["owns_a_car"] = data["owns_a_car"].apply(bool)
        data["owns_a_bike"] = data["owns_a_bike"].apply(bool)
        data["has_an_active_loan"] = data["has_an_active_loan"].apply(bool)
        data["owns_a_house"] = data["owns_a_house"].apply(bool)
        data["is_male"] = data["is_male"].apply(bool)
        data["has_provided_a_mobile_number"] = data["has_provided_a_mobile_number"].apply(bool)
        data["has_provided_a_home_number"] = data["has_provided_a_home_number"].apply(bool)
        data["was_reachable_at_work"] = data["was_reachable_at_work"].apply(bool)
        data["same_residence_and_home"] = data["same_residence_and_home"].apply(bool)
        data["same_work_and_home"] = data["same_work_and_home"].apply(bool)
        data["nr_defaults_in_social_circle"] = data["same_work_and_home"].apply(bool)
        data["residence_density"] = data["residence_density"].apply(float)
        
        data = data.astype({
            "is_male": "bool",
            "nr_children": "int8"
        })
        


        return data[list(features_types_per_config[config].keys())]