
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml




class        SCREAMING_SNAKE_CASE__					(     yaml.SafeLoader ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      [self.constructed_objects[key_node] for key_node, _ in node.value]
				_snake_case          =      [tuple(lowercase     ) if isinstance(lowercase      ,  lowercase     ) else key for key in keys]
				_snake_case          =      Counter(lowercase     )
				_snake_case          =      [key for key in counter if counter[key] > 1]
				if duplicate_keys:
							raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''     )





	def 			A							(  self     :   Dict      ,  lowercase     :   List[str]      ,  lowercase     :   Dict=False     ):


				'''simple docstring'''


				_snake_case          =      super().construct_mapping(lowercase      ,  deep=lowercase     )
				self._check_no_duplicates_on_constructed_node(lowercase     )
				return mapping






def 	a_		(	__lowercase       :     str			)			->  Tuple[Optional[str], str]:






			"""simple docstring"""




			_snake_case          =      list(readme_content.splitlines()			)
			if full_content and full_content[0] == "---" and "---" in full_content[1:]:
						_snake_case          =      full_content[1:].index('---'			) + 1
						_snake_case          =      '\n'.join(full_content[1:sep_idx]			)
						return yamlblock, "\n".join(full_content[sep_idx + 1 :]			)

			return None, "\n".join(__lowercase			)




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		str          = {"train_eval_index"}  # train-eval-index in the YAML metadata



	@classmethod
	def 			A							(  cls     :   Tuple      ,  lowercase     :   Path     ):


				'''simple docstring'''


				with open(lowercase      ,  encoding='utf-8'     ) as readme_file:
							_snake_case     , _snake_case          =      _split_yaml_from_readme(readme_file.read()     )
				if yaml_string is not None:
							return cls.from_yaml_string(lowercase     )
				else:
							return cls()



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Path     ):


				'''simple docstring'''


				if path.exists():
							with open(lowercase      ,  encoding='utf-8'     ) as readme_file:
										_snake_case          =      readme_file.read()
				else:
							_snake_case          =      None
				_snake_case          =      self._to_readme(lowercase     )
				with open(lowercase      ,  'w'      ,  encoding='utf-8'     ) as readme_file:
							readme_file.write(lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Optional[str] = None     ):


				'''simple docstring'''


				if readme_content is not None:
							_snake_case     , _snake_case          =      _split_yaml_from_readme(lowercase     )
							_snake_case          =      '---\n' + self.to_yaml_string() + '---\n' + content
				else:
							_snake_case          =      '---\n' + self.to_yaml_string() + '---\n'
				return full_content



	@classmethod
	def 			A							(  cls     :   int      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      yaml.load(lowercase      ,  Loader=_NoDuplicateSafeLoader     ) or {}

				# Convert the YAML keys to DatasetMetadata fields
				_snake_case          =      {
				    (key.replace('-'      ,  '_'     ) if key.replace('-'      ,  '_'     ) in cls._FIELDS_WITH_DASHES else key): value
				    for key, value in metadata_dict.items()
				}
				return cls(**lowercase     )





	def 			A							(  self     :   str     ):


				'''simple docstring'''


				return yaml.safe_dump(
				    {
				        (key.replace('_'      ,  '-'     ) if key in self._FIELDS_WITH_DASHES else key): value
				        for key, value in self.items()
				    }      ,  sort_keys=lowercase      ,  allow_unicode=lowercase      ,  encoding='utf-8'      ,  ).decode('utf-8'     )




_lowerCamelCase	:    List[Any]          =      {
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
			from argparse import ArgumentParser

			_lowerCamelCase	:    List[str]          =      ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
			ap.add_argument('''readme_filepath''')
			_lowerCamelCase	:    int          =      ap.parse_args()

			_lowerCamelCase	:    Tuple          =      Path(args.readme_filepath)
			_lowerCamelCase	:    Union[str, Any]          =      DatasetMetadata.from_readme(readme_filepath)
			print(dataset_metadata)
			dataset_metadata.to_readme(readme_filepath)

from __future__ import annotations

import requests

_lowerCamelCase	:    List[str]          =      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 	a_		(	__lowercase       :     str	, __lowercase       :     int = 1	, __lowercase       :     str = "new"	, __lowercase       :     list | None = None			)			->  dict:
			_snake_case          =      wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(__lowercase			) - valid_terms			)			):
						_snake_case          =      f'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(__lowercase			)
			_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(__lowercase			)}

			_snake_case          =      {}
			for id_ in range(__lowercase			):
						_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''']))