"""simple docstring"""




from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

__lowerCAmelCase    :							Union[str, Any]													=						"https://www.indeed.co.in/jobs?q=mobile+app+development&l="







def 							_UpperCAmelCase		(       lowerCamelCase__ = "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]}")





"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




