



import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_lowercase		:				List[str]      		=logging.getLogger(__name__)

def      lowerCAmelCase_					(  )							->	Any:







			"""simple docstring"""
			a__       :						Tuple							=  argparse.ArgumentParser(
			    description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).""")
			parser.add_argument("""--file_path"""						,       type=_lowercase						,       default="""data/dump.txt"""						,       help="""The path to the data.""")
			parser.add_argument("""--tokenizer_type"""						,       type=_lowercase						,       default="""bert"""						,       choices=["""bert""", """roberta""", """gpt2"""])
			parser.add_argument("""--tokenizer_name"""						,       type=_lowercase						,       default="""bert-base-uncased"""						,       help="""The tokenizer to use.""")
			parser.add_argument("""--dump_file"""						,       type=_lowercase						,       default="""data/dump"""						,       help="""The dump file prefix.""")
			a__       :						str							=  parser.parse_args()

			logger.info(F'''Loading Tokenizer ({args.tokenizer_name})''')
			if args.tokenizer_type == "bert":
						a__       :						Dict							=  BertTokenizer.from_pretrained(args.tokenizer_name)
						a__       :						Union[str, Any]							=  tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
						a__       :						str							=  tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						a__       :						Dict							=  RobertaTokenizer.from_pretrained(args.tokenizer_name)
						a__       :						Union[str, Any]							=  tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
						a__       :						Dict							=  tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
			elif args.tokenizer_type == "gpt2":
						a__       :						Union[str, Any]							=  GPTaTokenizer.from_pretrained(args.tokenizer_name)
						a__       :						int							=  tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
						a__       :						int							=  tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

			logger.info(F'''Loading text from {args.file_path}''')
			with open(args.file_path						,       """r"""						,       encoding="""utf8""") as fp:
						a__       :						Any							=  fp.readlines()

			logger.info("""Start encoding""")
			logger.info(F'''{len(_lowercase)} examples to process.''')

			a__       :						str							=  []
			a__       :						Dict							=  0
			a__       :						Tuple							=  1_0000
			a__       :						Optional[Any]							=  time.time()
			for text in data:
						a__       :						Tuple							=  F'''{bos} {text.strip()} {sep}'''
						a__       :						Tuple							=  tokenizer.encode(_lowercase						,       add_special_tokens=_lowercase)
						rslt.append(_lowercase)

						iter += 1
						if iter % interval == 0:
									a__       :						int							=  time.time()
									logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl''')
									a__       :						int							=  time.time()
			logger.info("""Finished binarization""")
			logger.info(F'''{len(_lowercase)} examples processed.''')

			a__       :						List[str]							=  F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
			a__       :						Any							=  tokenizer.vocab_size
			if vocab_size < (1 << 16):
						a__       :						int							=  [np.uintaa(_lowercase) for d in rslt]
			else:
						a__       :						List[Any]							=  [np.intaa(_lowercase) for d in rslt]
			random.shuffle(rslt_)
			logger.info(F'''Dump to {dp_file}''')
			with open(_lowercase						,       """wb""") as handle:
						pickle.dump(rslt_						,       _lowercase						,       protocol=pickle.HIGHEST_PROTOCOL)


if __name__ == "__main__":
		main()









from __future__ import annotations

import requests

_lowercase		:				Dict      		=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      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       int = 1						,       _lowercase    :       str = "new"						,       _lowercase    :       list | None = None)							->	dict:







			"""simple docstring"""
			a__       :						Optional[Any]							=  wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(_lowercase) - valid_terms)):
						a__       :						Any							=  F'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(_lowercase)
			a__       :						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

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

			a__       :						int							=  {}
			for id_ in range(_lowercase):
						a__       :						List[Any]							=  {
						    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"]))




