




from __future__ import annotations

import requests

lowercase__							:	str											=		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 		lowerCamelCase__       (						_A	,      _A = 1	,      _A = "new"	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(UpperCAmelCase__				) - valid_terms				)				):
		snake_case_						       =	f"Invalid search term: {invalid_search_terms}"
		raise ValueError(UpperCAmelCase__				)
	snake_case_						       =	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

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

	snake_case_						       =	{}
	for id_ in range(UpperCAmelCase__				):
		snake_case_						       =	{
		    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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}


class        A__   (   _snake_case    ,    _snake_case      ):
    lowercase						       =		"resnet"
    lowercase						       =		["basic", "bottleneck"]



    def __init__(       self		,					UpperCamelCase__=3		,					UpperCamelCase__=64		,					UpperCamelCase__=[256, 512, 1024, 2048]		,					UpperCamelCase__=[3, 4, 6, 3]		,					UpperCamelCase__="bottleneck"		,					UpperCamelCase__="relu"		,					UpperCamelCase__=False		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)
           if layer_type not in self.layer_types:
                  raise ValueError(f'''layer_type={layer_type} is not one of {",".join(self.layer_types							)}'''							)
           A_						       =   num_channels
           A_						       =   embedding_size
           A_						       =   hidden_sizes
           A_						       =   depths
           A_						       =   layer_type
           A_						       =   hidden_act
           A_						       =   downsample_in_first_stage
           A_						       =   ["""stem"""] + [f'''stage{idx}''' for idx in range(1		,					len(UpperCamelCase__							) + 1							)]
           A_       ,      A_						       =   get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__		,					out_indices=UpperCamelCase__		,					stage_names=self.stage_names							)


class        A__   (   _snake_case      ):
    lowercase						       =		version.parse("1.11"      )



    @property
    def        snake_case_      (       self							)    ->   Mapping[str, Mapping[int, str]]:





           '''simple docstring'''
           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ]							)







    @property
    def        snake_case_      (       self							)    ->   float:





           '''simple docstring'''
           return 1e-3
