# Copyright 2023 Andre Barbosa, Igor Caetano Silveira & The HuggingFace Datasets Authors
#
# 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 math
import os
import re

import datasets
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
from tqdm.auto import tqdm

np.random.seed(42)  # Set the seed

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
TODO
"""

# 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 = ""

_URLS = {
    "sourceA": "https://huggingface.co/datasets/kamel-usp/aes_enem_dataset/resolve/main/sourceA.tar.gz?download=true",
}


PROMPTS_TO_IGNORE = [
    "brasileiros-tem-pessima-educacao-argumentativa-segundo-cientista",
    "carta-convite-discutir-discriminacao-na-escola",
    "informacao-no-rotulo-de-produtos-transgenicos",
]
CSV_HEADER = [
    "id",
    "id_prompt",
    "title",
    "essay",
    "grades",
    "general",
    "specific",
    "essay_year",
]


class AesEnemDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.0.1")

    # 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="sourceA", version=VERSION, description="TODO"),
        datasets.BuilderConfig(
            name="sourceB",
            version=VERSION,
            description="TODO",
        ),
    ]

    DEFAULT_CONFIG_NAME = "sourceA"  # 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 == "sourceA"
        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "id_prompt": datasets.Value("string"),
                    "essay_title": datasets.Value("string"),
                    "essay_text": datasets.Value("string"),
                    "grades": datasets.Sequence(datasets.Value("int16")),
                    "essay_year": datasets.Value("int16"),
                }
            )
        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

        urls = _URLS[self.config.name]
        extracted_files = dl_manager.download_and_extract({"sourceA": urls})
        html_parser = self._process_html_files(extracted_files)
        self._generate_splits(html_parser.sourceA)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        extracted_files["sourceA"], "sourceA", "train.csv"
                    ),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        extracted_files["sourceA"], "sourceA", "validation.csv"
                    ),
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        extracted_files["sourceA"], "sourceA", "test.csv"
                    ),
                    "split": "test",
                },
            ),
        ]

    def _process_html_files(self, paths_dict):
        html_parser = HTMLParser(paths_dict)
        html_parser.parse()
        return html_parser

    def _generate_splits(self, filepath: str, train_size=0.7):
        def map_year(year):
            if year <= 2017:
                return "<=2017"
            return str(year)

        def normalize_grades(grades):
            grades = grades.strip("[]").split(", ")
            grade_mapping = {"0.0": 0, "20": 40}

            # We will remove the rows that match the criteria below
            if any(
                single_grade in grades
                for single_grade in ["50", "100", "150", "0.5", "1.0", "1.5"]
            ):
                return None
            # Use the mapping to transform grades, ignoring the last grade
            mapped_grades = [
                int(grade_mapping.get(grade_concept, grade_concept))
                for grade_concept in grades[:-1]
            ]

            # Calculate and append the sum of the mapped grades as the last element
            mapped_grades.append(sum(mapped_grades))
            return mapped_grades

        df = pd.read_csv(filepath)
        df["general"] = df["general"].fillna("")
        df["essay_year"] = df["essay_year"].astype("int")
        df["mapped_year"] = df["essay_year"].apply(map_year)
        df["grades"] = df["grades"].apply(normalize_grades)
        df = df.dropna()
        buckets = df.groupby("mapped_year")["id_prompt"].unique().to_dict()        
        df.drop('mapped_year', axis=1, inplace=True)
        train_set = []
        val_set = []
        test_set = []
        for year, prompts in buckets.items():
            np.random.shuffle(prompts)
            num_prompts = len(prompts)

            # All prompts go to the test if less than 3
            if num_prompts <= 3:
                train_set.append(df[df["id_prompt"].isin([prompts[0]])])
                val_set.append(df[df["id_prompt"].isin([prompts[1]])])
                test_set.append(df[df["id_prompt"].isin([prompts[2]])])
                continue

            # Determine the number of prompts for each set based on train_size and remaining prompts
            num_train = math.floor(num_prompts * train_size)
            num_val_test = num_prompts - num_train
            num_val = num_val_test // 2
            num_test = num_val_test - num_val

            # Assign prompts to each set
            train_set.append(df[df["id_prompt"].isin(prompts[:num_train])])
            val_set.append(
                df[df["id_prompt"].isin(prompts[num_train : (num_train + num_val)])]
            )
            test_set.append(
                df[
                    df["id_prompt"].isin(
                        prompts[
                            (num_train + num_val) : (num_train + num_val + num_test)
                        ]
                    )
                ]
            )

        # Convert lists of groups to DataFrames
        train_df = pd.concat(train_set)
        val_df = pd.concat(val_set)
        test_df = pd.concat(test_set)

        # Data Validation Assertions
        assert (
            len(set(train_df["id_prompt"]).intersection(set(val_df["id_prompt"]))) == 0
        ), "Overlap between train and val id_prompt"
        assert (
            len(set(train_df["id_prompt"]).intersection(set(test_df["id_prompt"]))) == 0
        ), "Overlap between train and test id_prompt"
        assert (
            len(set(val_df["id_prompt"]).intersection(set(test_df["id_prompt"]))) == 0
        ), "Overlap between val and test id_prompt"
        dirname = os.path.dirname(filepath)
        train_df.to_csv(f"{dirname}/train.csv", index=False)
        val_df.to_csv(f"{dirname}/validation.csv", index=False)
        test_df.to_csv(f"{dirname}/test.csv", index=False)

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        if self.config.name == "sourceA":
            with open(filepath, encoding="utf-8") as csvfile:
                next(csvfile)
                csv_reader = csv.DictReader(csvfile, fieldnames=CSV_HEADER)
                for i, row in enumerate(csv_reader):
                    grades = row["grades"].strip("[]").split(", ")
                    yield i, {
                        "id": row["id"],
                        "id_prompt": row["id_prompt"],
                        "essay_title": row["title"],
                        "essay_text": row["essay"],
                        "grades": grades,
                        "essay_year": row["essay_year"],
                    }


class HTMLParser:
    def __init__(self, paths_dict):
        self.paths_dict = paths_dict
        self.sourceA = None
        self.sourceB = None

    def apply_soup(self, filepath, num):
        # recebe uma URL, salva o HTML dessa página e retorna o soup dela
        file = open(os.path.join(filepath, num), "r", encoding="utf8")
        conteudo = file.read()
        soup = BeautifulSoup(conteudo, "html.parser")
        return soup

    @staticmethod
    def _get_title(soup):
        title = soup.find("div", class_="container-composition")
        if title is None:
            title = soup.find("h1", class_="pg-color10").get_text()
        else:
            title = title.h2.get_text()
        title = title.replace("\xa0", "")
        return title

    @staticmethod
    def _get_grades(soup):
        grades = soup.find("section", class_="results-table")
        final_grades = []
        if grades is not None:
            grades = grades.find_all("span", class_="points")
            assert len(grades) == 6, f"Missing grades: {len(grades)}"
            for single_grade in grades:
                grade = int(single_grade.get_text())
                final_grades.append(grade)
            assert final_grades[-1] == sum(
                final_grades[:-1]
            ), "Grading sum is not making sense"
        else:
            grades = soup.find("div", class_="redacoes-corrigidas pg-bordercolor7")
            grades_sum = float(
                soup.find("th", class_="noBorder-left").get_text().replace(",", ".")
            )
            grades = grades.find_all("td")[:10]
            for idx in range(1, 10, 2):
                grade = float(grades[idx].get_text().replace(",", "."))
                final_grades.append(grade)
            assert grades_sum == sum(final_grades), "Grading sum is not making sense"
            final_grades.append(grades_sum)
        return final_grades

    @staticmethod
    def _get_general_comment(soup):
        def get_general_comment_aux(soup):
            result = soup.find("article", class_="list-item c")
            if result is not None:
                result = result.find("div", class_="description")
                return result.get_text()
            else:
                result = soup.find("p", style="margin: 0px 0px 11px;")
                if result is not None:
                    return result.get_text()
                else:
                    result = soup.find("p", style="margin: 0px;")
                    if result is not None:
                        return result.get_text()
                    else:
                        result = soup.find(
                            "p", style="margin: 0px; text-align: justify;"
                        )
                        if result is not None:
                            return result.get_text()
                        else:
                            return ""

        text = soup.find("div", class_="text")
        if text is not None:
            text = text.find("p")
            if (text is None) or (len(text.get_text()) < 2):
                return get_general_comment_aux(soup)
            return text.get_text()
        else:
            return get_general_comment_aux(soup)

    @staticmethod
    def _get_specific_comment(soup):
        result = soup.find("div", class_="text")
        if result is not None:
            result = result.find_all("li")
            cms = []
            if result != []:
                for item in result:
                    text = item.get_text()
                    if text != "\xa0":
                        cms.append(text)
                return cms
            else:
                result = soup.find("div", class_="text").find_all("p")
                for item in result:
                    text = item.get_text()
                    if text != "\xa0":
                        cms.append(text)
                return cms
        else:
            result = soup.find_all("article", class_="list-item c")
            if len(result) < 2:
                return ["First if"]
            result = result[1].find_all("p")
            cms = []
            for item in result:
                text = item.get_text()
                if text != "\xa0":
                    cms.append(text)
            return cms

    @staticmethod
    def _get_essay(soup):
        essay = soup.find("div", class_="text-composition")
        if essay is not None:
            essay = essay.find_all("p")
            for f in essay:
                while f.find("span", style="color:#00b050") is not None:
                    f.find("span", style="color:#00b050").decompose()
                while f.find("span", class_="certo") is not None:
                    f.find("span", class_="certo").decompose()
            result = []
            for paragraph in essay:
                result.append(paragraph.get_text())
            return result
        else:
            essay = soup.find("div", {"id": "texto"})
            essay.find("section", class_="list-items").decompose()
            essay = essay.find_all("p")
            for f in essay:
                while f.find("span", class_="certo") is not None:
                    f.find("span", class_="certo").decompose()
            result = []
            for paragraph in essay:
                result.append(paragraph.get_text())
            return result

    @staticmethod
    def _get_essay_year(soup):
        pattern = r"redações corrigidas - \w+/\d+"
        first_occurrence = re.search(pattern, soup.get_text().lower())
        matched_url = first_occurrence.group(0) if first_occurrence else None
        year_pattern = r"\d{4}"
        return re.search(year_pattern, matched_url).group(0)

    def _clean_title(self, title):
        smaller_index = title.find("[")
        if smaller_index == -1:
            return title
        else:
            bigger_index = title.find("]")
            new_title = title[:smaller_index] + title[bigger_index + 1 :]
            return self._clean_title(new_title.replace("  ", " "))

    def _clean_list(self, list):
        if list == []:
            return []
        else:
            new_list = []
            for phrase in list:
                phrase = (
                    phrase.replace("\xa0", "").replace(" ,", ",").replace(" .", ".")
                )
                while phrase.find("  ") != -1:
                    phrase = phrase.replace("  ", " ")
                if len(phrase) > 1:
                    new_list.append(phrase)
            return new_list

    def parse(self):
        for key, filepath in self.paths_dict.items():
            full_path = os.path.join(filepath, key)
            if key == "sourceA":
                self.sourceA = f"{full_path}/sourceA.csv"
            with open(
                f"{full_path}/{key}.csv", "w", newline="", encoding="utf8"
            ) as final_file:
                writer = csv.writer(final_file)
                writer.writerow(CSV_HEADER)
                sub_folders = [
                    name for name in os.listdir(full_path) if not name.endswith(".csv")
                ]
                essay_id = 0
                essay_title = None
                essay_text = None
                essay_grades = None
                general_comment = None
                specific_comment = None
                essay_year = None
                for prompt_folder in tqdm(
                    sub_folders,
                    desc=f"Parsing HTML files from: {key}",
                    total=len(sub_folders),
                ):
                    if prompt_folder in PROMPTS_TO_IGNORE:
                        continue
                    prompt = os.path.join(full_path, prompt_folder)
                    prompt_essays = [name for name in os.listdir(prompt)]
                    prompt_essays = prompt_essays[:-1]
                    essay_year = HTMLParser._get_essay_year(
                        self.apply_soup(prompt, "Prompt.html")
                    )
                    for essay in prompt_essays:
                        soup_text = self.apply_soup(prompt, essay)
                        if essay == "Prompt.html":
                            continue
                        essay_title = self._clean_title(
                            HTMLParser._get_title(soup_text).replace(";", ",")
                        )
                        essay_grades = HTMLParser._get_grades(soup_text)
                        general_comment = HTMLParser._get_general_comment(
                            soup_text
                        ).strip()
                        specific_comment = HTMLParser._get_specific_comment(soup_text)
                        if general_comment in specific_comment:
                            specific_comment.remove(general_comment)
                            if (len(specific_comment) > 1) and (
                                len(specific_comment[0]) < 2
                            ):
                                specific_comment = specific_comment[1:]
                        essay_text = self._clean_list(HTMLParser._get_essay(soup_text))
                        specific_comment = self._clean_list(specific_comment)
                        writer.writerow(
                            [
                                essay,
                                prompt_folder,
                                essay_title,
                                essay_text,
                                essay_grades,
                                general_comment,
                                specific_comment,
                                essay_year,
                            ]
                        )
                        essay_id += 1