
from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE__   :			Dict												=    "https://www.indeed.co.in/jobs?q=mobile+app+development&l="




def  __magic_name__ (   __lowerCAmelCase	:						str = "mumbai"							)			->   Generator[tuple[str, str], None, None]:
				__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]}')





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



