

from __future__ import annotations

import requests

A_       : Optional[Any]         =			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 						UpperCamelCase					(lowercase_: str    ,      lowercase_: int = 1    ,      lowercase_: str = "new"    ,      lowercase_: list | None = None					)     ->      dict:
	A__							:    Dict        =		wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(lowercase_					) - valid_terms					)					):
		A__							:    Union[str, Any]        =		f"""Invalid search term: {invalid_search_terms}"""
		raise ValueError(lowercase_					)
	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 == 429:
		raise requests.HTTPError

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

	A__							:    List[Any]        =		{}
	for id_ in range(lowercase_					):
		A__							:    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']))






from typing import TYPE_CHECKING

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


A_       : List[str]         =			{
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Optional[int]         =			[
	    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXJapaneseForCausalLM',
	    'GPTNeoXJapaneseLayer',
	    'GPTNeoXJapaneseModel',
	    'GPTNeoXJapanesePreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	A_       : Any         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



