



"""simple docstring"""

from __future__ import annotations

import requests

lowercase__			   =  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       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: str = "new"  ,       _lowerCamelCase				: list | None = None					):
							__a		:		Union[str, Any]									=       wanted_data or []
							if invalid_search_terms := ", ".join(sorted(set(_lowerCamelCase					) - valid_terms					)					):
														__a		:		int									=       F'''Invalid search term: {invalid_search_terms}'''
														raise ValueError(_lowerCamelCase					)
							__a		:		Optional[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

							__a		:		Union[str, Any]									=       response.json()
							if not wanted_data:
														return {id_: data["data"]["children"][id_] for id_ in range(_lowerCamelCase					)}

							__a		:		Union[str, Any]									=       {}
							for id_ in range(_lowerCamelCase					):
														__a		:		Optional[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"]))






"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


lowercase__			   =  {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ErnieForCausalLM",
	    "ErnieForMaskedLM",
	    "ErnieForMultipleChoice",
	    "ErnieForNextSentencePrediction",
	    "ErnieForPreTraining",
	    "ErnieForQuestionAnswering",
	    "ErnieForSequenceClassification",
	    "ErnieForTokenClassification",
	    "ErnieModel",
	    "ErniePreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ernie import (
		    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ErnieForCausalLM,
		    ErnieForMaskedLM,
		    ErnieForMultipleChoice,
		    ErnieForNextSentencePrediction,
		    ErnieForPreTraining,
		    ErnieForQuestionAnswering,
		    ErnieForSequenceClassification,
		    ErnieForTokenClassification,
		    ErnieModel,
		    ErniePreTrainedModel,
		)

else:
	import sys

	lowercase__			   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

