import os
import re
import requests
import datasets
# import pandas as pd
from bs4 import BeautifulSoup


_DBNAME = os.path.basename(__file__).split('.')[0]

_HOMEPAGE = "https://huggingface.co/datasets/george-chou/" + _DBNAME

_URL = 'https://pytorch.org/vision/main/_modules/'

# _TYPES = pd.read_csv(_HOMEPAGE + '/resolve/main/data/backbone.csv',
#                      index_col='ver').to_dict()['type']

# _NAMES = sorted(list(set(_TYPES.values())))


class vi_backbones(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "ver": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    # "type": datasets.features.ClassLabel(names=_NAMES),
                    "input_size": datasets.Value("int16"),
                    "num_params": datasets.Value("int32"),
                    "url": datasets.Value("string"),
                }
            ),
            supervised_keys=("ver", "type"),
            homepage=_HOMEPAGE,
            license="mit"
        )

    def _parse_url(self, url):
        response = requests.get(url)
        html = response.text
        return BeautifulSoup(html, 'html.parser')

    def _generate_dataset(self, url):

        torch_page = self._parse_url(url)
        article = torch_page.find('article', {'id': 'pytorch-article'})
        ul = article.find('ul').find('ul')
        in1k_v1, in1k_v2 = [], []

        for li in ul.find_all('li'):
            name = str(li.text)
            if name.__contains__('torchvision.models.') and len(name.split('.')) == 3:
                if name.__contains__('_api') or name.__contains__('feature_extraction'):
                    continue

                href = li.find('a').get('href')
                model_page = self._parse_url(url + href)
                divs = model_page.select('div.viewcode-block')

                for div in divs:
                    div_id = str(div['id'])
                    if div_id.__contains__('_Weights'):
                        m_ver = div_id.split('_Weight')[0].lower()
                        m_type = re.search('[a-zA-Z]+', m_ver).group(0)
                        ints = div.find_all('span', {'class': 'mi'})
                        m_urls = div.find_all('span', {'class': 's2'})
                        input_size = int(ints[0].text)
                        num_params = int(ints[1].text)
                        m_url = m_urls[0].text
                        m_dict = {
                            'ver': m_ver,
                            'type': m_type,
                            'input_size': input_size,
                            'num_params': num_params,
                            'url': m_url
                        }
                        # print('Adding ' + m_ver + ' on IMAGENET1K_V1')
                        in1k_v1.append(m_dict)

                        if len(ints) > 2 and len(m_urls) > 1:
                            input_size = int(ints[2].text)
                            num_params = int(ints[3].text)
                            m_url = m_urls[1].text
                            m_dict = {
                                'ver': m_ver,
                                'type': m_type,
                                'input_size': input_size,
                                'num_params': num_params,
                                'url': m_url
                            }
                            # print('Adding ' + m_ver + ' on IMAGENET1K_V2')
                            in1k_v2.append(m_dict)

        return in1k_v1, in1k_v2

    def _split_generators(self, dl_manager):
        in1k_v1, in1k_v2 = self._generate_dataset(_URL)

        return [
            datasets.SplitGenerator(
                name="IMAGENET1K_V1",
                gen_kwargs={
                    "files": in1k_v1,
                },
            ),
            datasets.SplitGenerator(
                name="IMAGENET1K_V2",
                gen_kwargs={
                    "files": in1k_v2,
                },
            ),
        ]

    def _generate_examples(self, files):
        for i, model in enumerate(files):
            yield i, {
                "ver": model['ver'],
                "type": model['type'],
                "input_size": model['input_size'],
                "num_params": model['num_params'],
                "url": model['url'],
            }