



'''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
)
UpperCamelCase__:     List[str]										=    logging.getLogger(__name__)


def 				snake_case_  (      )   ->     Any:
							UpperCAmelCase				: 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=_lowerCAmelCase     ,   default='''data/dump.txt'''     ,   help='''The path to the data.'''						)
							parser.add_argument('''--tokenizer_type'''     ,   type=_lowerCAmelCase     ,   default='''bert'''     ,   choices=['''bert''', '''roberta''', '''gpt2''']						)
							parser.add_argument('''--tokenizer_name'''     ,   type=_lowerCAmelCase     ,   default='''bert-base-uncased'''     ,   help='''The tokenizer to use.'''						)
							parser.add_argument('''--dump_file'''     ,   type=_lowerCAmelCase     ,   default='''data/dump'''     ,   help='''The dump file prefix.'''						)
							UpperCAmelCase				: List[Any]											=	parser.parse_args()

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

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

							UpperCAmelCase				: List[Any]											=	[]
							UpperCAmelCase				: Any											=	0
							UpperCAmelCase				: Optional[Any]											=	10000
							UpperCAmelCase				: List[Any]											=	time.time()
							for text in data:
														UpperCAmelCase				: Union[str, Any]											=	f"""{bos} {text.strip()} {sep}"""
														UpperCAmelCase				: Union[str, Any]											=	tokenizer.encode(_lowerCAmelCase     ,   add_special_tokens=_lowerCAmelCase						)
														rslt.append(_lowerCAmelCase						)

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

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


if __name__ == "__main__":
						main()










'''simple docstring'''






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

UpperCamelCase__:     Union[str, Any]										=    "https://www.indeed.co.in/jobs?q=mobile+app+development&l="


def 				snake_case_  (      _lowerCAmelCase						:		str = "mumbai"						)   ->     Generator[tuple[str, str], None, None]:
							UpperCAmelCase				: Optional[int]											=	BeautifulSoup(requests.get(url + location						).content     ,   '''html.parser'''						)
							# This attribute finds out all the specifics listed in a job
							for job in soup.find_all('''div'''     ,   attrs={'''data-tn-component''': '''organicJob'''}						):
														UpperCAmelCase				: Optional[Any]											=	job.find('''a'''     ,   attrs={'''data-tn-element''': '''jobTitle'''}						).text.strip()
														UpperCAmelCase				: int											=	job.find('''span'''     ,   {'''class''': '''company'''}						).text.strip()
														yield job_title, company_name


if __name__ == "__main__":
						for i, job in enumerate(fetch_jobs("Bangalore"), 1):
												print(F"Job {i:>2} is {job[0]} at {job[1]}")





