




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


UpperCamelCase__ :   Dict						=							logging.get_logger(__name__)

UpperCamelCase__ :   Optional[Any]						=							{}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       int						=     "llama"
				__a     :       int						=     ["past_key_values"]






				def __init__(			self      ,snake_case__=32000      ,snake_case__=4096      ,snake_case__=11008      ,snake_case__=32      ,snake_case__=32      ,snake_case__=None      ,snake_case__="silu"      ,snake_case__=2048      ,snake_case__=0.02      ,snake_case__=1E-6      ,snake_case__=True      ,snake_case__=0      ,snake_case__=1      ,snake_case__=2      ,snake_case__=1      ,snake_case__=False      ,snake_case__=None      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : str					  =      hidden_size
							SCREAMING_SNAKE_CASE_   : int					  =      intermediate_size
							SCREAMING_SNAKE_CASE_   : Any					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : str					  =      num_attention_heads

							# for backward compatibility
							if num_key_value_heads is None:
										SCREAMING_SNAKE_CASE_   : Any					  =      num_attention_heads

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_key_value_heads
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      rms_norm_eps
							SCREAMING_SNAKE_CASE_   : List[str]					  =      pretraining_tp
							SCREAMING_SNAKE_CASE_   : int					  =      use_cache
							SCREAMING_SNAKE_CASE_   : Any					  =      rope_scaling
							self._rope_scaling_validation()

							super().__init__(
							    pad_token_id=snake_case__      ,bos_token_id=snake_case__      ,eos_token_id=snake_case__      ,tie_word_embeddings=snake_case__      ,**snake_case__      ,)






				def 		snake_case	(			self ):
							if self.rope_scaling is None:
										return

							if not isinstance(self.rope_scaling      ,snake_case__ ) or len(self.rope_scaling ) != 2:
										raise ValueError(
										    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
										    F'got {self.rope_scaling}' )
							SCREAMING_SNAKE_CASE_   : Any					  =      self.rope_scaling.get('type'      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.rope_scaling.get('factor'      ,snake_case__ )
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
										raise ValueError(
										    F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
							if rope_scaling_factor is None or not isinstance(snake_case__      ,snake_case__ ) or rope_scaling_factor <= 1.0:
										raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )







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"""]))

