

from __future__ import annotations

import requests

a_      =     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 			lowerCamelCase__			(       _a				,      _a = 1				,      _a = "new"				,      _a = None):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(_a) - valid_terms)):
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							f"Invalid search term: {invalid_search_terms}"
		raise ValueError(_a)
	SCREAMING_SNAKE_CASE :   Tuple					  =							requests.get(
	    f"https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"				,      headers={"User-agent": "A random string"}				,      )
	if response.status_code == 429:
		raise requests.HTTPError

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

	SCREAMING_SNAKE_CASE :   Optional[int]					  =							{}
	for id_ in range(_a):
		SCREAMING_SNAKE_CASE :   List[str]					  =							{
		    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']))




"""simple docstring"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      root / filename
      for filename in [
          '''vision.cpp''',
          os.path.join('''cpu'''  ,						'''ms_deform_attn_cpu.cpp'''	),
          os.path.join('''cuda'''  ,						'''ms_deform_attn_cuda.cu'''	),
      ]
  ]

  load(
      '''MultiScaleDeformableAttention'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						extra_cflags=['''-DWITH_CUDA=1''']  ,						extra_cuda_cflags=[
          '''-DCUDA_HAS_FP16=1''',
          '''-D__CUDA_NO_HALF_OPERATORS__''',
          '''-D__CUDA_NO_HALF_CONVERSIONS__''',
          '''-D__CUDA_NO_HALF2_OPERATORS__''',
      ]  ,						)

  import MultiScaleDeformableAttention as MSDA

  return MSDA





