


'''simple docstring'''
from __future__ import annotations

import requests

__snake_case				 =	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      a (     __a					,   __a = 1					,   __a = "new"					,   __a = None  )    ->      dict:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					wanted_data or []
					if invalid_search_terms := ", ".join(sorted(set(a__  ) - valid_terms  )  ):
										UpperCamelCase__  :Optional[int]				   =					f'''Invalid search term: {invalid_search_terms}'''
										raise ValueError(a__  )
					UpperCamelCase__  :Any				   =					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

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

					UpperCamelCase__  :str				   =					{}
					for id_ in range(a__  ):
										UpperCamelCase__  :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 importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			List[Any]		    =			ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
	    )       )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""			,type=a__			,default=1			,help="""Number of TPU cores to use (1 or 8)."""       )

	# positional
	parser.add_argument(
	    """training_script"""			,type=a__			,help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )			,)

	# rest from the training program
	parser.add_argument("""training_script_args"""			,nargs=a__       )

	return parser.parse_args()
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	__A :			Union[str, Any]		    =			parse_args()

	# Import training_script as a module.
	__A :			List[Any]		    =			Path(args.training_script       )
	sys.path.append(str(script_fpath.parent.resolve()       )       )
	__A :			str		    =			script_fpath.stem
	__A :			int		    =			importlib.import_module(a__       )

	# Patch sys.argv
	__A :			List[str]		    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores       )]

	xmp.spawn(mod._mp_fn			,args=()			,nprocs=args.num_cores       )


if __name__ == "__main__":
			main()





