






from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					str ,  __lowerCamelCase				:					pyspark.sql.DataFrame ,  __lowerCamelCase				:					Optional[NamedSplit] = None ,  __lowerCamelCase				:					Optional[Features] = None ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					str = "arrow" ,  **__lowerCamelCase				:					Dict ,  ):
		super().__init__(
		    split=__lowerCamelCase ,  features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  keep_in_memory=__lowerCamelCase ,  streaming=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :List[Any]          =	load_from_cache_file
		UpperCamelCase     :Tuple          =	file_format
		UpperCamelCase     :Any          =	Spark(
		    df=__lowerCamelCase ,  features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  working_dir=__lowerCamelCase ,  **__lowerCamelCase ,  )





	def 						_A (		self				:					str	):
		if self.streaming:
			return self.builder.as_streaming_dataset(split=self.split	)
		UpperCamelCase     :List[str]          =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
		self.builder.download_and_prepare(
		    download_mode=__lowerCamelCase ,  file_format=self._file_format ,  )
		return self.builder.as_dataset(split=self.split	)








'''simple docstring'''







from __future__ import annotations

import requests

_A	:			str         =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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase = 1				,			UpperCamelCase = "new"				,			UpperCamelCase = None      )      ->       dict:
							lowerCamelCase__					:	Any         =       wanted_data or []
							if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase      ) - valid_terms      )      ):
														lowerCamelCase__					:	str         =       f'''Invalid search term: {invalid_search_terms}'''
														raise ValueError(UpperCamelCase      )
							lowerCamelCase__					:	str         =       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

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

							lowerCamelCase__					:	Dict         =       {}
							for id_ in range(UpperCamelCase      ):
														lowerCamelCase__					:	Union[str, Any]         =       {
														    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''']))

