from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				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"""}     ):
    SCREAMING_SNAKE_CASE__      							=				job.find("""a"""			,					attrs={"""data-tn-element""": """jobTitle"""}     ).text.strip()
    SCREAMING_SNAKE_CASE__      							=				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]}""")

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCamelCase      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  for shard in shards:
    for i in range(__UpperCamelCase     ):
      yield {"i": i, "shard": shard}



def      __SCREAMING_SNAKE_CASE  ( )							-> Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

  SCREAMING_SNAKE_CASE__      							=				ArgumentParser()
  parser.add_argument("""--streaming"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--local_rank"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--num_workers"""			,					type=__UpperCamelCase			,					default=0     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  SCREAMING_SNAKE_CASE__      							=				args.streaming
  SCREAMING_SNAKE_CASE__      							=				args.num_workers

  SCREAMING_SNAKE_CASE__      							=				{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(__UpperCamelCase     )]}
  SCREAMING_SNAKE_CASE__      							=				IterableDataset.from_generator(__UpperCamelCase			,					gen_kwargs=__UpperCamelCase     )
  if not streaming:
    SCREAMING_SNAKE_CASE__      							=				Dataset.from_list(list(__UpperCamelCase     )     )

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				NUM_SHARDS * NUM_ITEMS_PER_SHARD
  SCREAMING_SNAKE_CASE__      							=				full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)     )

  SCREAMING_SNAKE_CASE__      							=				sum(1 for _ in dataloader     )
  if local_size != expected_local_size:
    raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
   main()
