import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__SCREAMING_SNAKE_CASE						:   Dict         =			version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
				from nltk import word_tokenize


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			'\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			'\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			'\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION		)
class        lowercase_   (					datasets.Metric		):



							def 	UpperCamelCase	(						self    ):
													return datasets.MetricInfo(
													    description=_DESCRIPTION				,    citation=_CITATION				,    inputs_description=_KWARGS_DESCRIPTION				,    features=datasets.Features(
													        {
													            "predictions": datasets.Value("string"				,    id="sequence"    ),
													            "references": datasets.Value("string"				,    id="sequence"    ),
													        }    )				,    codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]				,    reference_urls=[
													        "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
													        "https://en.wikipedia.org/wiki/METEOR",
													    ]				,    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													import nltk

													nltk.download("wordnet"    )
													if NLTK_VERSION >= version.Version("3.6.5"    ):
																			nltk.download("punkt"    )
													if NLTK_VERSION >= version.Version("3.6.6"    ):
																			nltk.download("omw-1.4"    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_=0.9				,    lowercase_=3				,    lowercase_=0.5    ):
													if NLTK_VERSION >= version.Version("3.6.5"    ):
																			_snake_case						:					Optional[Any]    =   [
																			    meteor_score.single_meteor_score(
																			        word_tokenize(lowercase_    )				,    word_tokenize(lowercase_    )				,    alpha=lowercase_				,    beta=lowercase_				,    gamma=lowercase_    )
																			    for ref, pred in zip(lowercase_				,    lowercase_    )
																			]
													else:
																			_snake_case						:					str    =   [
																			    meteor_score.single_meteor_score(lowercase_				,    lowercase_				,    alpha=lowercase_				,    beta=lowercase_				,    gamma=lowercase_    )
																			    for ref, pred in zip(lowercase_				,    lowercase_    )
																			]

													return {"meteor": np.mean(lowercase_    )}
from __future__ import annotations

import requests

__SCREAMING_SNAKE_CASE						:   Tuple         =			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  snake_case						(__lowercase ,      __lowercase = 1 ,      __lowercase = "new" ,      __lowercase = None						)	->	dict:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(__lowercase						) - valid_terms						)						):
												_snake_case						:					List[str]    =   F"""Invalid search term: {invalid_search_terms}"""
												raise ValueError(__lowercase						)
						_snake_case						:					Any    =   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						:					Optional[int]    =   response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(__lowercase						)}

						_snake_case						:					Union[str, Any]    =   {}
						for id_ in range(__lowercase						):
												_snake_case						:					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']))