






'''simple docstring'''



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


def 							lowercase_				(    __A :       str					,      __A :       str			)       ->      List[str]:



 """simple docstring"""




 lowercase					:   List[Any]				  =RobertaPreLayerNormConfig.from_pretrained(
     __A					,      architectures=['''RobertaPreLayerNormForMaskedLM''']			)

 # convert state_dict
 lowercase					:   List[str]				  =torch.load(hf_hub_download(repo_id=__A					,      filename='''pytorch_model.bin'''			)			)
 lowercase					:   int				  ={}
 for tensor_key, tensor_value in original_state_dict.items():
  # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
  if tensor_key.startswith('''roberta.'''			):
   lowercase					:   List[Any]				  ='''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''			) :]

  # The original implementation contains weights which are not used, remove them from the state_dict
  if tensor_key.endswith('''.self.LayerNorm.weight'''			) or tensor_key.endswith('''.self.LayerNorm.bias'''			):
   continue

  lowercase					:   str				  =tensor_value

 lowercase					:   List[Any]				  =RobertaPreLayerNormForMaskedLM.from_pretrained(
     pretrained_model_name_or_path=__A					,      config=__A					,      state_dict=__A			)
 model.save_pretrained(__A			)

 # convert tokenizer
 lowercase					:   List[str]				  =AutoTokenizer.from_pretrained(__A			)
 tokenizer.save_pretrained(__A			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint-repo',
          default=None,
          type=str,
          required=True,
          help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)











'''simple docstring'''



from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE						=	'https://www.indeed.co.in/jobs?q=mobile+app+development&l='


def 							lowercase_				(    __A :       str = "mumbai"			)       ->      Generator[tuple[str, str], None, None]:



 """simple docstring"""




 lowercase					:   List[Any]				  =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'''}			):
  lowercase					:   List[str]				  =job.find('''a'''					,      attrs={'''data-tn-element''': '''jobTitle'''}			).text.strip()
  lowercase					:   Union[str, Any]				  =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]}""")



