






from __future__ import annotations

import requests

a_							=    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  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				int = 1    ,   UpperCamelCase_					:				str = "new"    ,   UpperCamelCase_					:				list | None = None   ) ->   dict:
						"""simple docstring"""







						lowerCAmelCase__       =						wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase_   ) - valid_terms   )   ):
												lowerCAmelCase__       =						F"Invalid search term: {invalid_search_terms}"
												raise ValueError(UpperCamelCase_   )
						lowerCAmelCase__       =						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__       =						response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(UpperCamelCase_   )}

						lowerCAmelCase__       =						{}
						for id_ in range(UpperCamelCase_   ):
												lowerCAmelCase__       =						{
												    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''']))














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






