



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Union[str, Any]=5						)   ->     List[Any]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''						) == 1
							UpperCAmelCase				: List[str]											=	torch.tensor(tokenizer.encode(_lowerCAmelCase     ,   add_special_tokens=_lowerCAmelCase						)						).unsqueeze(0						)  # Batch size 1
							UpperCAmelCase				: int											=	model(_lowerCAmelCase						)[0]  # The last hidden-state is the first element of the output tuple
							UpperCAmelCase				: List[Any]											=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							UpperCAmelCase				: int											=	logits[0, masked_index, :]
							UpperCAmelCase				: int											=	logits.softmax(dim=0						)
							UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	prob.topk(k=_lowerCAmelCase     ,   dim=0						)
							UpperCAmelCase				: List[Any]											=	''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(_lowerCAmelCase						)						)]						)
							UpperCAmelCase				: Optional[Any]											=	tokenizer.mask_token
							UpperCAmelCase				: Optional[Any]											=	[]
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''						)						):
														UpperCAmelCase				: int											=	predicted_token_bpe.replace('''\u2581'''     ,   ''' '''						)
														if " {0}".format(_lowerCAmelCase						) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(_lowerCAmelCase						)     ,   _lowerCAmelCase						),
																					        values[index].item(),
																					        predicted_token,
																					    )						)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(_lowerCAmelCase     ,   _lowerCAmelCase						),
																					        values[index].item(),
																					        predicted_token,
																					    )						)
							return topk_filled_outputs


UpperCamelCase__:     Any										=    CamembertTokenizer.from_pretrained("camembert-base")
UpperCamelCase__:     Union[str, Any]										=    CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

UpperCamelCase__:     Any										=    "Le camembert est <mask> :)"




print(fill_mask(masked_input, model, tokenizer, topk=3))










'''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]}")





