

'''simple docstring'''


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[Any]=   "ClapFeatureExtractor"
							_lowerCamelCase		:      int=   ("RobertaTokenizer", "RobertaTokenizerFast")


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Optional[Any]:
								super().__init__(_snake_case    ,			_snake_case)


							def __call__(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      Tuple       	=					kwargs.pop('sampling_rate'    ,			_snake_case)

								if text is None and audios is None:
									raise ValueError('You have to specify either text or audios. Both cannot be none.')

								if text is not None:
									UpperCAmelCase_		:      str       	=					self.tokenizer(_snake_case    ,			return_tensors=_snake_case    ,			**_snake_case)

								if audios is not None:
									UpperCAmelCase_		:      List[Any]       	=					self.feature_extractor(
									    _snake_case    ,			sampling_rate=_snake_case    ,			return_tensors=_snake_case    ,			**_snake_case)

								if text is not None and audios is not None:
									UpperCAmelCase_		:      Optional[int]       	=					audio_features.input_features
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**_snake_case)    ,			tensor_type=_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Optional[Any]:
								return self.tokenizer.batch_decode(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Tuple:
								return self.tokenizer.decode(*_snake_case    ,			**_snake_case)





							@property
							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.tokenizer.model_input_names
								UpperCAmelCase_		:      Optional[int]       	=					self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))









'''simple docstring'''


from __future__ import annotations

import requests

lowerCAmelCase__										= 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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1   ,UpperCamelCase = "new"   ,UpperCamelCase = None  )    ->    dict:
	UpperCAmelCase_		:      Dict       	=					wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase  ) - valid_terms  )  ):
		UpperCAmelCase_		:      str       	=					f"""Invalid search term: {invalid_search_terms}"""
		raise ValueError(UpperCamelCase  )
	UpperCAmelCase_		:      Dict       	=					requests.get(
	    f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""   ,headers={'User-agent': 'A random string'}   ,)
	if response.status_code == 4_2_9:
		raise requests.HTTPError

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

	UpperCAmelCase_		:      List[str]       	=					{}
	for id_ in range(UpperCamelCase  ):
		UpperCAmelCase_		:      Tuple       	=					{
		    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"]))






