



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			'Wav2Vec2FeatureExtractor'
     _A       =			'AutoTokenizer'







     def __init__(self				,			__a				,			__a    ):






           '''simple docstring'''



           super().__init__(__a				,			__a    )
           lowerCamelCase								=		self.feature_extractor
           lowerCamelCase								=		False







     @classmethod
     def 						_a      (cls				,			__a				,			**__a    ):






           '''simple docstring'''



           try:
                 return super().from_pretrained(__a				,			**__a    )
           except OSError:
                 warnings.warn(
                     F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
                     " include a `tokenizer_class` attribute is deprecated and will be "
                     "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
                     " attribute to either your `config.json` or `tokenizer_config.json` "
                     "file to suppress this warning: "				,			__a				,			)

                 lowerCamelCase								=		WavaVecaFeatureExtractor.from_pretrained(__a				,			**__a    )
                 lowerCamelCase								=		WavaVecaCTCTokenizer.from_pretrained(__a				,			**__a    )

                 return cls(feature_extractor=__a				,			tokenizer=__a    )







     def __call__(self				,			*__a				,			**__a    ):






           '''simple docstring'''



           if self._in_target_context_manager:
                 return self.current_processor(*__a				,			**__a    )

           if "raw_speech" in kwargs:
                 warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."    )
                 lowerCamelCase								=		kwargs.pop("raw_speech"    )
           else:
                 lowerCamelCase								=		kwargs.pop("audio"				,			__a    )
           lowerCamelCase								=		kwargs.pop("sampling_rate"				,			__a    )
           lowerCamelCase								=		kwargs.pop("text"				,			__a    )
           if len(__a    ) > 0:
                 lowerCamelCase								=		args[0]
                 lowerCamelCase								=		args[1:]

           if audio is None and text is None:
                 raise ValueError("You need to specify either an `audio` or `text` input to process."    )

           if audio is not None:
                 lowerCamelCase								=		self.feature_extractor(__a				,			*__a				,			sampling_rate=__a				,			**__a    )
           if text is not None:
                 lowerCamelCase								=		self.tokenizer(__a				,			**__a    )

           if text is None:
                 return inputs
           elif audio is None:
                 return encodings
           else:
                 lowerCamelCase								=		encodings["input_ids"]
                 return inputs







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           if self._in_target_context_manager:
                 return self.current_processor.pad(*__a				,			**__a    )

           lowerCamelCase								=		kwargs.pop("input_features"				,			__a    )
           lowerCamelCase								=		kwargs.pop("labels"				,			__a    )
           if len(__a    ) > 0:
                 lowerCamelCase								=		args[0]
                 lowerCamelCase								=		args[1:]

           if input_features is not None:
                 lowerCamelCase								=		self.feature_extractor.pad(__a				,			*__a				,			**__a    )
           if labels is not None:
                 lowerCamelCase								=		self.tokenizer.pad(__a				,			**__a    )

           if labels is None:
                 return input_features
           elif input_features is None:
                 return labels
           else:
                 lowerCamelCase								=		labels["input_ids"]
                 return input_features







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           return self.tokenizer.batch_decode(*__a				,			**__a    )







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           return self.tokenizer.decode(*__a				,			**__a    )







     @contextmanager
     def 						_a      (self    ):






           '''simple docstring'''



           warnings.warn(
               "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
               "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
               "your audio inputs, or in a separate call."    )
           lowerCamelCase								=		True
           lowerCamelCase								=		self.tokenizer
           yield
           lowerCamelCase								=		self.feature_extractor
           lowerCamelCase								=		False




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]}""")