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'engine_url': pediatorrent.url,
'desc_link': href
}
prettyPrinter(row)
return
if self.insideResult and tag == self.DIV and 'flex' in css_classes and 'gap-x-3' in css_classes:
self.insideYear = True
return
def handle_endtag(self, tag):
if self.insideLink and tag == self.A:
self.insideLink = False
return
if self.insideYear and tag == self.DIV:
self.insideYear = False
return
if not self.insideLink and self.insideResult and tag == self.DIV:
self.insideResult = False
return
if not self.insideResult and self.insideResultContainer and tag == self.DIV:
self.insideResultContainer = False
return
if not self.insideResultContainer and self.insideResultList and tag == self.DIV:
self.insideResultList = False
return
def download_torrent(self, info):
print(download_file(info))
def get_search_url(self, what, cat):
category = self.supported_categories[cat]
return f'{self.url}/{category}?query={what}'
def has_results(self, html):
no_results_matches = re.finditer(self.no_results_regex, html, re.MULTILINE)
no_results = [x.group() for x in no_results_matches]
return len(no_results) == 0
def search(self, what, cat='all'):
what = what.replace('%20', '+')
retrieved_html = retrieve_url(self.get_search_url(what,cat), self.headers)
parser = self.SearchResultsParser(self.url, cat)
parser.feed(retrieved_html)
parser.close()
# <FILESEP>
import os
from config import get_config, show_config, save_config, load_config
from data import Generator
from base import BasicScenario
from solver import REGISTRY
def run(config):
print(f"\n{'-' * 20} Start {'-' * 20}\n")
# Load solver info: environment and solver class
solver_info = REGISTRY.get(config.solver_name)
Env, Solver = solver_info['env'], solver_info['solver']
print(f'Use {config.solver_name} Solver (Type = {solver_info["type"]})...\n')
scenario = BasicScenario.from_config(Env, Solver, config)
scenario.run()
print(f"\n{'-' * 20} Complete {'-' * 20}\n")
if __name__ == '__main__':
# Please refer to `base.loader` to obtain all available solvers
# 1. Get Config
# The key settings are controlled with config.py
# while other advanced settings are listed in settings/*.yaml
config = get_config()
# You can modify some settings directly here.
# An example:
# config.solver_name = 'a3c_gcn_seq2seq' # modify the algorithm of the solver
# config.shortest_method = 'mcf' # modify the shortest path algorithm to Multi-commodity Flow
# config.num_train_epochs = 100 # modify the number of trainning epochs
# 2. Generate Dataset
# Although we do not generate a static dataset,
# the environment will automatically produce a random dataset.
p_net, v_net_simulator = Generator.generate_dataset(
config,
p_net=False,
v_nets=False,
save=False) # Here, no dataset will be generated and saved.
# 3. Start to Run
# A scenario with an environment and a solver will be create following provided config.
# The interaction between the environment and the solver will happen in this scenario.
run(config)