
"""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
)
_A     :			Tuple  						=			logging.getLogger(__name__)


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

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

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

       lowercase       :							int									=				[]
       lowercase       :							List[str]									=				0
       lowercase       :							int									=				1_0000
       lowercase       :							int									=				time.time()
       for text in data:
              lowercase       :							Optional[Any]									=				f"""{bos} {text.strip()} {sep}"""
              lowercase       :							List[str]									=				tokenizer.encode(__snake_case	,					add_special_tokens=__snake_case    )
              rslt.append(__snake_case    )

              iter += 1
              if iter % interval == 0:
                     lowercase       :							List[str]									=				time.time()
                     logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""    )
                     lowercase       :							Union[str, Any]									=				time.time()
       logger.info("Finished binarization"    )
       logger.info(f"""{len(__snake_case    )} examples processed."""    )

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


if __name__ == "__main__":
   main()




"""simple docstring"""




def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]=False    )					->							Optional[Any]:

       if isinstance(__snake_case	,					__snake_case    ) and isinstance(__snake_case	,					__snake_case    ):
              lowercase       :							Tuple									=				len(set_a.intersection(__snake_case    )    )

              if alternative_union:
                     lowercase       :							Union[str, Any]									=				len(__snake_case    ) + len(__snake_case    )
              else:
                     lowercase       :							Optional[Any]									=				len(set_a.union(__snake_case    )    )

              return intersection / union

       if isinstance(__snake_case	,					(list, tuple)    ) and isinstance(__snake_case	,					(list, tuple)    ):
              lowercase       :							Tuple									=				[element for element in set_a if element in set_b]

              if alternative_union:
                     lowercase       :							List[str]									=				len(__snake_case    ) + len(__snake_case    )
                     return len(__snake_case    ) / union
              else:
                     lowercase       :							Dict									=				set_a + [element for element in set_b if element not in set_a]
                     return len(__snake_case    ) / len(__snake_case    )

              return len(__snake_case    ) / len(__snake_case    )
       return None


if __name__ == "__main__":
   _A     :			List[str]  						=			{"""a""", """b""", """c""", """d""", """e"""}
   _A     :			Dict  						=			{"""c""", """d""", """e""", """f""", """h""", """i"""}
   print(jaccard_similarity(set_a, set_b))


