'''simple docstring'''





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
)
SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.getLogger(__name__)





def 	lowerCAmelCase_				(					)    ->      Union[str, Any]:


     '''simple docstring'''




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

     logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""			)
     if args.tokenizer_type == "bert":
          UpperCAmelCase_           =   BertTokenizer.from_pretrained(args.tokenizer_name			)
          UpperCAmelCase_           =   tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
          UpperCAmelCase_           =   tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
     elif args.tokenizer_type == "roberta":
          UpperCAmelCase_           =   RobertaTokenizer.from_pretrained(args.tokenizer_name			)
          UpperCAmelCase_           =   tokenizer.special_tokens_map['''cls_token''']  # `<s>`
          UpperCAmelCase_           =   tokenizer.special_tokens_map['''sep_token''']  # `</s>`
     elif args.tokenizer_type == "gpt2":
          UpperCAmelCase_           =   GPTaTokenizer.from_pretrained(args.tokenizer_name			)
          UpperCAmelCase_           =   tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
          UpperCAmelCase_           =   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:
          UpperCAmelCase_           =   fp.readlines()

     logger.info("Start encoding"			)
     logger.info(f"""{len(__UpperCamelCase			)} examples to process."""			)

     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   1_00_00
     UpperCAmelCase_           =   time.time()
     for text in data:
          UpperCAmelCase_           =   f"""{bos} {text.strip()} {sep}"""
          UpperCAmelCase_           =   tokenizer.encode(__UpperCamelCase					,    add_special_tokens=__UpperCamelCase			)
          rslt.append(__UpperCamelCase			)

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

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


if __name__ == "__main__":
    main()









from datetime import datetime as dt
import os

from github import Github


A__  :  List[str]								=					[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Union[str, Any]									= Github(os.environ['''GITHUB_TOKEN'''] )
		lowerCAmelCase_			:     Tuple									= g.get_repo('''huggingface/transformers''' )
		lowerCAmelCase_			:     int									= repo.get_issues(state='''open''' )

		for issue in open_issues:
				lowerCAmelCase_			:     Optional[Any]									= sorted([comment for comment in issue.get_comments()]     ,key=lambda __UpperCamelCase : i.created_at     ,reverse=__UpperCamelCase )
				lowerCAmelCase_			:     Tuple									= comments[0] if len(__UpperCamelCase ) > 0 else None
				if (
				    last_comment is not None
				    and last_comment.user.login == "github-actions[bot]"
				    and (dt.utcnow() - issue.updated_at).days > 7
				    and (dt.utcnow() - issue.created_at).days >= 30
				    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
				):
						# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
						issue.edit(state='''closed''' )
				elif (
				    (dt.utcnow() - issue.updated_at).days > 23
				    and (dt.utcnow() - issue.created_at).days >= 30
				    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
				):
						# print(f"Would add stale comment to {issue.number}")
						issue.create_comment(
						    '''This issue has been automatically marked as stale because it has not had '''
						    '''recent activity. If you think this still needs to be addressed '''
						    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
						    '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
						    '''are likely to be ignored.''' )


if __name__ == "__main__":
						main()





