from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__SCREAMING_SNAKE_CASE						:   Optional[int]         =			HfArgumentParser(InitializationArguments)
__SCREAMING_SNAKE_CASE						:   Tuple         =			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__SCREAMING_SNAKE_CASE						:   Dict         =			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__SCREAMING_SNAKE_CASE						:   Any         =			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
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']))