


"""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        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     int = 1    ,					lowerCAmelCase__ :     str = "new"    ,					lowerCAmelCase__ :     list | None = None			) ->      dict:
		__a           =			wanted_data or []
		if invalid_search_terms := ", ".join(sorted(set(lowerCAmelCase__			) - valid_terms			)			):
				__a           =			f'''Invalid search term: {invalid_search_terms}'''
				raise ValueError(lowerCAmelCase__			)
		__a           =			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

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

		__a           =			{}
		for id_ in range(lowerCAmelCase__			):
				__a           =			{
				    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_tf_available, is_torch_available


A__								=						{
    '''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
    '''tokenization_ctrl''': ['''CTRLTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''CTRLForSequenceClassification''',
				    '''CTRLLMHeadModel''',
				    '''CTRLModel''',
				    '''CTRLPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFCTRLForSequenceClassification''',
				    '''TFCTRLLMHeadModel''',
				    '''TFCTRLModel''',
				    '''TFCTRLPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
				from .tokenization_ctrl import CTRLTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ctrl import (
								    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CTRLForSequenceClassification,
								    CTRLLMHeadModel,
								    CTRLModel,
								    CTRLPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ctrl import (
								    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFCTRLForSequenceClassification,
								    TFCTRLLMHeadModel,
								    TFCTRLModel,
								    TFCTRLPreTrainedModel,
								)

else:
				import sys

				A__								=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

