



from __future__ import annotations

import requests

A_		:     Tuple        =     set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any ,					UpperCAmelCase__ :Optional[Any] = 1 ,					UpperCAmelCase__ :int = "new" ,					UpperCAmelCase__ :List[Any] = None					):






 '''simple docstring'''

 a						=		wanted_data or []
 if invalid_search_terms := ", ".join(sorted(set(UpperCAmelCase__					) - valid_terms					)					):
  a						=		F"""Invalid search term: {invalid_search_terms}"""
  raise ValueError(UpperCAmelCase__					)
 a						=		requests.get(
     F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}""" ,					headers={"User-agent": "A random string"} ,					)
 if response.status_code == 4_29:
  raise requests.HTTPError

 a						=		response.json()
 if not wanted_data:
  return {id_: data["data"]["children"][id_] for id_ in range(UpperCAmelCase__					)}

 a						=		{}
 for id_ in range(UpperCAmelCase__					):
  a						=		{
      item: data["data"]["children"][id_]["data"][item] for item in wanted_data
  }
 return data_dict


if __name__ == "__main__":
     # If you get Error 429, that means you are rate limited.Try after some time
     print(get_subreddit_data('''learnpython''', wanted_data=['''title''', '''url''', '''selftext''']))




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(UpperCAmelCase__	),   '''Tatoeba directory does not exist.'''	)
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    return TatoebaConverter(save_dir=__lowerCAmelCase							)

   @slow
   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    self.resolver.convert_models(["heb-eng"]							)




   @slow
   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.resolver.write_model_card("opus-mt-he-en"							,   dry_run=__lowerCAmelCase							)
    assert mmeta["long_pair"] == "heb-eng"

