



import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch





@require_torch
@require_optimum
@slow
class    lowerCamelCase__  (						unittest.TestCase):
					"""simple docstring"""



					def 						_a      (self    ):






											'''simple docstring'''



											lowerCamelCase								=		"hf-internal-testing/tiny-random-t5"
											lowerCamelCase								=		AutoTokenizer.from_pretrained(lowerCAmelCase__    )
											lowerCamelCase								=		AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__    )

											lowerCamelCase								=		tokenizer("This is me"				,			return_tensors="pt"    )

											lowerCamelCase								=		model.to_bettertransformer()

											self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()    )    )

											lowerCamelCase								=		model.generate(**lowerCAmelCase__    )

											lowerCamelCase								=		model.reverse_bettertransformer()

											self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()    )    )

											with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(lowerCAmelCase__    )

																	lowerCamelCase								=		AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__    )

																	self.assertFalse(
																	    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()    )    )

																	lowerCamelCase								=		model_reloaded.generate(**lowerCAmelCase__    )
																	self.assertTrue(torch.allclose(lowerCAmelCase__				,			lowerCAmelCase__    )    )







					def 						_a      (self    ):






											'''simple docstring'''



											lowerCamelCase								=		"hf-internal-testing/tiny-random-t5"
											lowerCamelCase								=		AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__    )

											lowerCamelCase								=		model.to_bettertransformer()

											with tempfile.TemporaryDirectory() as tmpdirname:
																	with self.assertRaises(lowerCAmelCase__    ):
																							model.save_pretrained(lowerCAmelCase__    )

																	lowerCamelCase								=		model.reverse_bettertransformer()
																	model.save_pretrained(lowerCAmelCase__    )




from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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

def     __lowercase(	UpperCAmelCase__ = "mumbai" ):


      """simple docstring"""



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