

from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

UpperCAmelCase_							:					List[Any]		 =   '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str = "mumbai"       )					->				Generator[tuple[str, str], None, None]:
	__A :			Dict		    =			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 :			Optional[int]		    =			job.find("""a"""			,attrs={"""data-tn-element""": """jobTitle"""}       ).text.strip()
		__A :			str		    =			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]}""")







lowerCamelCase__     :		List[str]        = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase__     :		List[Any]        = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase__     :		int        = {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






