from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE						:   int         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   List[Any]         =			{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'pegasus'
							_lowerCamelCase						=		['past_key_values']
							_lowerCamelCase						=		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



							def __init__(						self				,    lowercase_=50_265				,    lowercase_=1_024				,    lowercase_=12				,    lowercase_=4_096				,    lowercase_=16				,    lowercase_=12				,    lowercase_=4_096				,    lowercase_=16				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=True				,    lowercase_=True				,    lowercase_="gelu"				,    lowercase_=1_024				,    lowercase_=0.1				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.02				,    lowercase_=0				,    lowercase_=False				,    lowercase_=0				,    lowercase_=1				,    lowercase_=1				,    **lowercase_				,    ):
													_snake_case						:					List[Any]    =   vocab_size
													_snake_case						:					Any    =   max_position_embeddings
													_snake_case						:					Tuple    =   d_model
													_snake_case						:					Optional[int]    =   encoder_ffn_dim
													_snake_case						:					List[Any]    =   encoder_layers
													_snake_case						:					int    =   encoder_attention_heads
													_snake_case						:					Optional[Any]    =   decoder_ffn_dim
													_snake_case						:					str    =   decoder_layers
													_snake_case						:					Any    =   decoder_attention_heads
													_snake_case						:					str    =   dropout
													_snake_case						:					int    =   attention_dropout
													_snake_case						:					Dict    =   activation_dropout
													_snake_case						:					str    =   activation_function
													_snake_case						:					Any    =   init_std
													_snake_case						:					Any    =   encoder_layerdrop
													_snake_case						:					List[str]    =   decoder_layerdrop
													_snake_case						:					Any    =   use_cache
													_snake_case						:					Union[str, Any]    =   encoder_layers
													_snake_case						:					Optional[int]    =   scale_embedding  # scale factor will be sqrt(d_model) if True
													super().__init__(
													    pad_token_id=lowercase_				,    eos_token_id=lowercase_				,    is_encoder_decoder=lowercase_				,    decoder_start_token_id=lowercase_				,    forced_eos_token_id=lowercase_				,    **lowercase_				,    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self.encoder_attention_heads



							@property
							def 	UpperCamelCase	(						self    ):
													return self.d_model
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']))