


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property

@unittest.skipUnless(os.path.exists(snake_case__     )	, 'Tatoeba directory does not exist.'     )
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @cached_property
    def 		a_      (	self   ):
         snake_case					=    tempfile.mkdtemp()
         return TatoebaConverter(save_dir=_snake_case   )
    @slow
    def 		a_      (	self   ):
         self.resolver.convert_models(['''heb-eng''']   )




    @slow
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.resolver.write_model_card('''opus-mt-he-en'''      , dry_run=_snake_case   )
         assert mmeta["long_pair"] == "heb-eng"








from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_SCREAMING_SNAKE_CASE      :			str      				=   "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def       UpperCAmelCase__						(UpperCamelCase_ = "mumbai"    ):



     """simple docstring"""






     snake_case					=    BeautifulSoup(requests.get(url + location    ).content							,'''html.parser'''    )
     # This attribute finds out all the specifics listed in a job
     for job in soup.find_all('''div'''							,attrs={'''data-tn-component''': '''organicJob'''}    ):
          snake_case					=    job.find('''a'''							,attrs={'''data-tn-element''': '''jobTitle'''}    ).text.strip()
          snake_case					=    job.find('''span'''							,{'''class''': '''company'''}    ).text.strip()
          yield job_title, company_name


if __name__ == "__main__":
 for i, job in enumerate(fetch_jobs("Bangalore"), 1):
  print(f'''Job {i:>2} is {job[0]} at {job[1]}''')




