





from __future__ import annotations

import requests

A_		:		Dict		     =			set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__ = 1     ,   UpperCAmelCase__ = "new"     ,   UpperCAmelCase__ = None  )	->    dict:
  UpperCamelCase_: Optional[Any]   				=    wanted_data or []
  if invalid_search_terms := ", ".join(sorted(set(UpperCAmelCase__  ) - valid_terms  )  ):
    UpperCamelCase_: Optional[int]   				=    F'''Invalid search term: {invalid_search_terms}'''
    raise ValueError(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    requests.get(
      F'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''     ,   headers={'User-agent': 'A random string'}     ,   )
  if response.status_code == 4_2_9:
    raise requests.HTTPError

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

  UpperCamelCase_: Dict   				=    {}
  for id_ in range(UpperCAmelCase__  ):
    UpperCamelCase_: Optional[int]   				=    {
        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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  ,      return_dict=_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    AutoTokenizer.from_pretrained('google/mt5-small'							)

         UpperCamelCase_: Dict   				=    tokenizer('Hello there'  ,      return_tensors='pt'							).input_ids
         UpperCamelCase_: Optional[Any]   				=    tokenizer('Hi I am'  ,      return_tensors='pt'							).input_ids

         UpperCamelCase_: int   				=    model(input_ids.to(_lowerCamelCase							)  ,      labels=labels.to(_lowerCamelCase							)							).loss
         UpperCamelCase_: Tuple   				=    -(labels.shape[-1] * loss.item())

         UpperCamelCase_: Any   				=    -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)