





"""simple docstring"""




from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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





def 						UpperCAmelCase__		(     _UpperCAmelCase = "mumbai"					):






  """simple docstring"""





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






"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)