


from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[str]			=      ["input_features", "attention_mask"]


     def __init__(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[int]=80							,	lowerCamelCase__							:     int=16000							,	lowerCamelCase__							:     Optional[int]=0.0							,	lowerCamelCase__							:     Dict=10							,	lowerCamelCase__							:     Tuple=25							,	lowerCamelCase__							:     List[Any]="hamming_window"							,	lowerCamelCase__							:     Tuple=3_2768.0							,	lowerCamelCase__							:     Optional[int]=0.97							,	lowerCamelCase__							:     int=1.0							,	lowerCamelCase__							:     List[str]=True							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     str=False							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      Any:





      '''simple docstring'''






      super().__init__(feature_size=lowerCamelCase__							,	sampling_rate=lowerCamelCase__							,	padding_value=lowerCamelCase__							,	**lowerCamelCase__			)

      UpperCamelCase__					:    Union[str, Any]          =   feature_size
      UpperCamelCase__					:    Any          =   sampling_rate
      UpperCamelCase__					:    Optional[int]          =   padding_value
      UpperCamelCase__					:    Tuple          =   hop_length
      UpperCamelCase__					:    Dict          =   win_length
      UpperCamelCase__					:    Optional[int]          =   frame_signal_scale
      UpperCamelCase__					:    Optional[int]          =   preemphasis_coeff
      UpperCamelCase__					:    Union[str, Any]          =   mel_floor
      UpperCamelCase__					:    Optional[int]          =   normalize_means
      UpperCamelCase__					:    Any          =   normalize_vars
      UpperCamelCase__					:    Any          =   win_function
      UpperCamelCase__					:    Optional[Any]          =   return_attention_mask

      UpperCamelCase__					:    Any          =   win_length * sampling_rate // 1000
      UpperCamelCase__					:    Tuple          =   hop_length * sampling_rate // 1000

      UpperCamelCase__					:    Union[str, Any]          =   optimal_fft_length(self.sample_size			)
      UpperCamelCase__					:    Tuple          =   (self.n_fft // 2) + 1


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     np.array			)  ->      np.ndarray:





      '''simple docstring'''






      if self.win_function == "hamming_window":
       UpperCamelCase__					:    Tuple          =   window_function(window_length=self.sample_size							,	name=self.win_function							,	periodic=lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Union[str, Any]          =   window_function(window_length=self.sample_size							,	name=self.win_function			)

      UpperCamelCase__					:    List[str]          =   mel_filter_bank(
          num_frequency_bins=self.n_freqs							,	num_mel_filters=self.feature_size							,	min_frequency=0.0							,	max_frequency=self.sampling_rate / 2.0							,	sampling_rate=self.sampling_rate							,	)

      UpperCamelCase__					:    int          =   spectrogram(
          one_waveform * self.frame_signal_scale							,	window=lowerCamelCase__							,	frame_length=self.sample_size							,	hop_length=self.sample_stride							,	fft_length=self.n_fft							,	center=lowerCamelCase__							,	preemphasis=self.preemphasis_coeff							,	mel_filters=lowerCamelCase__							,	mel_floor=self.mel_floor							,	log_mel='''log'''							,	)
      return msfc_features.T


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     str			)  ->      Optional[Any]:





      '''simple docstring'''






      if self.normalize_means:
       UpperCamelCase__					:    Union[str, Any]          =   x[:input_length].mean(axis=0			)
       UpperCamelCase__					:    str          =   np.subtract(lowerCamelCase__							,	lowerCamelCase__			)
      if self.normalize_vars:
       UpperCamelCase__					:    Any          =   x[:input_length].std(axis=0			)
       UpperCamelCase__					:    List[Any]          =   np.divide(lowerCamelCase__							,	lowerCamelCase__			)

      if input_length < x.shape[0]:
       UpperCamelCase__					:    int          =   padding_value

      # make sure array is in float32
      UpperCamelCase__					:    Any          =   x.astype(np.floataa			)

      return x


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     List[np.ndarray]							,	lowerCamelCase__							:     Optional[np.ndarray] = None			)  ->      List[np.ndarray]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
      return [self._normalize_one(lowerCamelCase__							,	lowerCamelCase__							,	self.padding_value			) for x, n in zip(lowerCamelCase__							,	lowerCamelCase__			)]


     def __call__(      self							:     Optional[Any]							,	lowerCamelCase__							:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,	lowerCamelCase__							:     Union[bool, str, PaddingStrategy] = False							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[Union[str, TensorType]] = None							,	lowerCamelCase__							:     Optional[int] = None							,	**lowerCamelCase__							:     str							,	)  ->      BatchFeature:





      '''simple docstring'''






      if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
        raise ValueError(
            F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
            F" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
            F" {self.sampling_rate} and not {sampling_rate}."			)
      else:
       logger.warning(
           '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
           '''Failing to do so can result in silent errors that might be hard to debug.'''			)

      UpperCamelCase__					:    List[Any]          =   isinstance(lowerCamelCase__							,	np.ndarray			) and len(raw_speech.shape			) > 1
      if is_batched_numpy and len(raw_speech.shape			) > 2:
       raise ValueError(F"Only mono-channel audio is supported for input to {self}"			)
      UpperCamelCase__					:    str          =   is_batched_numpy or (
          isinstance(lowerCamelCase__							,	(list, tuple)			) and (isinstance(raw_speech[0]							,	(np.ndarray, tuple, list)			))
      )

      if is_batched:
       UpperCamelCase__					:    List[Any]          =   [np.asarray(lowerCamelCase__							,	dtype=np.floataa			) for speech in raw_speech]
      elif not is_batched and not isinstance(lowerCamelCase__							,	np.ndarray			):
       UpperCamelCase__					:    Any          =   np.asarray(lowerCamelCase__							,	dtype=np.floataa			)
      elif isinstance(lowerCamelCase__							,	np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
       UpperCamelCase__					:    Tuple          =   raw_speech.astype(np.floataa			)

      # always return batch
      if not is_batched:
       UpperCamelCase__					:    Optional[Any]          =   [raw_speech]

      # extract fbank features
      UpperCamelCase__					:    int          =   [self._extract_mfsc_features(lowerCamelCase__			) for one_waveform in raw_speech]

      # convert into correct format for padding
      UpperCamelCase__					:    Union[str, Any]          =   BatchFeature({'''input_features''': features}			)

      UpperCamelCase__					:    Dict          =   self.pad(
          lowerCamelCase__							,	padding=lowerCamelCase__							,	max_length=lowerCamelCase__							,	truncation=lowerCamelCase__							,	pad_to_multiple_of=lowerCamelCase__							,	return_attention_mask=lowerCamelCase__							,	**lowerCamelCase__							,	)
      # make sure list is in array format
      UpperCamelCase__					:    Union[str, Any]          =   padded_inputs.get('''input_features'''			)
      if isinstance(input_features[0]							,	lowerCamelCase__			):
       UpperCamelCase__					:    Optional[int]          =   [np.asarray(lowerCamelCase__							,	dtype=np.floataa			) for feature in input_features]

      UpperCamelCase__					:    Dict          =   padded_inputs.get('''attention_mask'''			)
      if attention_mask is not None:
       UpperCamelCase__					:    Optional[Any]          =   [np.asarray(lowerCamelCase__							,	dtype=np.intaa			) for array in attention_mask]

      if self.normalize_means or self.normalize_vars:
       UpperCamelCase__					:    List[Any]          =   (
           np.array(lowerCamelCase__							,	dtype=np.intaa			)
           if self._get_padding_strategies(lowerCamelCase__							,	max_length=lowerCamelCase__			) is not PaddingStrategy.DO_NOT_PAD
           and padding
           else None
       )
       UpperCamelCase__					:    Optional[int]          =   self.normalize(
           padded_inputs['''input_features''']							,	attention_mask=lowerCamelCase__			)

      if return_tensors is not None:
       UpperCamelCase__					:    Tuple          =   padded_inputs.convert_to_tensors(lowerCamelCase__			)

      return padded_inputs






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				str = "mumbai"     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   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'''}     ):
  UpperCamelCase__					:    Optional[Any]          =   job.find('''a'''    , attrs={'''data-tn-element''': '''jobTitle'''}     ).text.strip()
  UpperCamelCase__					:    Union[str, Any]          =   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]}")


