






"""simple docstring"""



from __future__ import annotations

import requests

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

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

		lowercase_ : str							       =   {}
		for id_ in range(__snake_case	):
				lowercase_ : Dict							       =   {
				    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_torch_available, is_vision_available


__a      =				{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["Mask2FormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "Mask2FormerForUniversalSegmentation",
						    "Mask2FormerModel",
						    "Mask2FormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_maskaformer import MaskaFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_maskaformer import (
												    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MaskaFormerForUniversalSegmentation,
												    MaskaFormerModel,
												    MaskaFormerPreTrainedModel,
												)


else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure)
