import multiprocessing

import pandas as pd
import time

from bs4 import BeautifulSoup
from itertools import product

from tqdm import tqdm


class Scraper:
    broken_link = 'The operation you requested cannot currently be performed. We apologize for the inconvenience and will try to address this as soon as possible. Please check back soon and hopefully this will be fixed. If this is still not fixed, visit the "Help" tab to send us an email describing the problem.'
    base_url = "https://ebird.org/targets"
    months = {1: 'January', 2: 'February',
              3: 'March', 4: 'April',
              5: 'May', 6: 'June',
              7: 'July', 8: 'August',
              9: 'September', 10: 'October',
              11: 'November', 12: 'December'}

    def __init__(self, args, regions):
        self.hotspot = args.hotspot
        self.regions = regions
        self.params_list = self.parse_params(args)
        self.num_cores = int(args.num_cores)

    @staticmethod
    def parse_params(args):
        params = {}

        if args.life:
            params['t2'] = "life"
        elif args.ytd:
            params['t2'] = "year"
        elif args.mtd:
            params['t2'] = "month"
        elif args.day:
            params['t2'] = "day"
        else:
            raise Exception("Time parameter missing.")

        if args.month:
            params['bmo'] = int(args.month)
            params['emo'] = int(args.month)
        elif args.range:
            start, end = args.range.split('-')
            start, end = int(start), int(end)
            if start < 1 or start > 12 or end < 1 or end > 12:
                raise Exception("Month endpoints out of range")
            params['bmo'] = start
            params['emo'] = end
        elif args.year:
            params['bmo'] = 1
            params['emo'] = 12
        elif args.all_months:
            params_list = []
            for n in range(1, 13):
                p = params.copy()
                p['bmo'] = n
                p['emo'] = n
                params_list.append(p)
            return params_list
        else:
            raise Exception("Time of Year parameter missing.")

        return [params]

    def loc_params(self, params, row):
        pass

    @staticmethod
    def parse_percent(soup):
        try:
            return [float(d['title'].split('% ')[0])
                    for d in soup.find_all('div', {'class': 'ResultsStats-stats'})]
        except Exception as e:
            return []

    @staticmethod
    def parse_species(soup):
        native = soup.find('section', {'aria-labelledby': 'native-and-naturalized'})
        if not native:
            return pd.DataFrame({'common name': []}), True
        species = [[s.text.strip() for s in d.find('a').contents]
                   for d in native.find_all('div', {'class': 'SpecimenHeader'})
                   if d.find('a')]
        if not species:
            return pd.DataFrame({'common name': []}), True
        if len(species[0]) > 1:
            common_name = []
            scientific_name = []
            for s in species:
                common_name.append(s[0] if len(s[0]) else None)
                scientific_name.append(s[1])
            return pd.DataFrame({'common name': common_name, 'scientific name': scientific_name}), False
        else:
            return pd.DataFrame({'common name': species}), False

    def scrape_page(self, params, row, session):
        sleep = 1
        percent = []
        params = self.loc_params(params, row)

        while not percent:
            r = session.get(self.base_url, params=params)
            soup = BeautifulSoup(r.content, 'html.parser')
            if soup.find('p').text == Scraper.broken_link:
                return pd.DataFrame({'common name': []})
            has_species = len([int(s.text) for s in soup.find_all('strong') if s.has_attr('class')]) > 0
            if not has_species:
                time.sleep(sleep)
                sleep *= 2
                continue
            df, is_empty = self.parse_species(soup)
            if is_empty:
                return df
            percent = self.parse_percent(soup)[:df.shape[0]]
        num_checklists = int(''.join(s for s in soup.find('p').text if s.isdigit()))
        df['checklists'] = num_checklists
        df['percent'] = percent
        df['country'] = row['country']
        df['region'] = row['region']
        df['sub region'] = row['sub_region']
        df['code'] = row['sub_region_code']
        if self.hotspot:
            df['hotspot'] = row['hotspot']
            df['hotspot_code'] = row['hotspot_code']
        df['start month'], df['end month'] = Scraper.months[params['bmo']], Scraper.months[params['emo']]
        if df['common name'].isna().sum() == df.shape[0]:
            df.drop(columns=['common name'], inplace=True)
        return df

    def scrape_data(self, session):
        with multiprocessing.Pool(self.num_cores) as pool:
            results = [pool.apply_async(self.scrape_page, (params, row, session)) for
                       params, (_, row) in product(self.params_list, self.regions.iterrows())]
            results = [r.get() for r in tqdm(results, total=len(results))]
        return pd.concat(results).reset_index(drop=True)


class GlobalScraper(Scraper):

    def __init__(self, args, regions):
        super().__init__(args, regions)

    def loc_params(self, params, row):
        params['r1'] = row['hotspot_code'] if self.hotspot else row['sub_region_code']
        params['r2'] = 'world'
        return params


class CountryScraper(Scraper):

    def __init__(self, args, regions):
        super().__init__(args, regions)

    def loc_params(self, params, row):
        params['r1'] = row['hotspot_code'] if self.hotspot else row['sub_region_code']
        params['r2'] = row['country_code']
        return params


class RegionScraper(Scraper):

    def __init__(self, args, regions):
        super().__init__(args, regions)

    def loc_params(self, params, row):
        params['r1'] = row['hotspot_code'] if self.hotspot else row['sub_region_code']
        params['r2'] = row['region_code']
        return params


class SubRegionScraper(Scraper):

    def __init__(self, args, regions):
        super().__init__(args, regions)

    def loc_params(self, params, row):
        params['r1'] = row['hotspot_code'] if self.hotspot else row['sub_region_code']
        params['r2'] = row['sub_region_code']
        return params