




import doctest
from collections import deque

import numpy as np
class 							__snake_case	:



				def __init__(   self   :		List[str]   )	->     Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase     :     List[Any]              =   [2, 1, 2, -1]
								_lowerCAmelCase     :     List[str]              =   [1, 2, 3, 4]



				def    SCREAMING_SNAKE_CASE							(   self   :		str   )	->     Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase     :     List[str]              =   len(self.first_signal   )
								_lowerCAmelCase     :     int              =   len(self.second_signal   )

								_lowerCAmelCase     :     Optional[int]              =   max(lowercase__		,						lowercase__   )

								# create a zero matrix of max_length x max_length
								_lowerCAmelCase     :     Optional[int]              =   [[0] * max_length for i in range(lowercase__   )]

								# fills the smaller signal with zeros to make both signals of same length
								if length_first_signal < length_second_signal:
												self.first_signal += [0] * (max_length - length_first_signal)
								elif length_first_signal > length_second_signal:
												self.second_signal += [0] * (max_length - length_second_signal)
								for i in range(lowercase__   ):
												_lowerCAmelCase     :     Any              =   deque(self.second_signal   )
												rotated_signal.rotate(lowercase__   )
												for j, item in enumerate(lowercase__   ):
																matrix[i][j] += item

        # multiply the matrix with the first signal
								_lowerCAmelCase     :     int              =   np.matmul(np.transpose(lowercase__   )		,						np.transpose(self.first_signal   )   )

								# rounding-off to two decimal places
								return [round(lowercase__		,						2   ) for i in final_signal]


if __name__ == "__main__":
	doctest.testmod()





from __future__ import annotations

import requests

lowerCAmelCase       		=    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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 1      ,       lowercase_ = "new"      ,       lowercase_ = None      )		->     dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(lowercase_      ) - valid_terms      )      ):
												__UpperCAmelCase			:							List[Any]	    =	f"Invalid search term: {invalid_search_terms}"
												raise ValueError(lowercase_      )
						__UpperCAmelCase			:							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

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

						__UpperCAmelCase			:							List[Any]	    =	{}
						for id_ in range(lowercase_      ):
												__UpperCAmelCase			:							str	    =	{
												    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"""]))

