



import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_						:     Union[str, Any]	     =		logging.get_logger(__name__)

a_						:     List[Any]	     =		{
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}





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



					_A       =			'data2vec-audio'







					def __init__(self				,			__a=32				,			__a=7_68				,			__a=12				,			__a=12				,			__a=30_72				,			__a="gelu"				,			__a=0.1				,			__a=0.1				,			__a=0.1				,			__a=0.0				,			__a=0.1				,			__a=0.1				,			__a=0.02				,			__a=1E-5				,			__a="gelu"				,			__a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)				,			__a=(5, 2, 2, 2, 2, 2, 2)				,			__a=(10, 3, 3, 3, 3, 2, 2)				,			__a=False				,			__a=16				,			__a=19				,			__a=5				,			__a=0.05				,			__a=10				,			__a=2				,			__a=0.0				,			__a=10				,			__a=0				,			__a="sum"				,			__a=False				,			__a=False				,			__a=2_56				,			__a=(5_12, 5_12, 5_12, 5_12, 15_00)				,			__a=(5, 3, 3, 1, 1)				,			__a=(1, 2, 3, 1, 1)				,			__a=5_12				,			__a=0				,			__a=1				,			__a=2				,			__a=False				,			__a=3				,			__a=2				,			__a=3				,			__a=None				,			**__a				,			):






											'''simple docstring'''



											super().__init__(**__a				,			pad_token_id=__a				,			bos_token_id=__a				,			eos_token_id=__a    )
											lowerCamelCase								=		hidden_size
											lowerCamelCase								=		feat_extract_activation
											lowerCamelCase								=		list(__a    )
											lowerCamelCase								=		list(__a    )
											lowerCamelCase								=		list(__a    )
											lowerCamelCase								=		conv_bias
											lowerCamelCase								=		num_conv_pos_embeddings
											lowerCamelCase								=		num_conv_pos_embedding_groups
											lowerCamelCase								=		conv_pos_kernel_size
											lowerCamelCase								=		len(self.conv_dim    )
											lowerCamelCase								=		num_hidden_layers
											lowerCamelCase								=		intermediate_size
											lowerCamelCase								=		hidden_act
											lowerCamelCase								=		num_attention_heads
											lowerCamelCase								=		hidden_dropout
											lowerCamelCase								=		attention_dropout
											lowerCamelCase								=		activation_dropout
											lowerCamelCase								=		feat_proj_dropout
											lowerCamelCase								=		final_dropout
											lowerCamelCase								=		layerdrop
											lowerCamelCase								=		layer_norm_eps
											lowerCamelCase								=		initializer_range
											lowerCamelCase								=		vocab_size
											lowerCamelCase								=		use_weighted_layer_sum

											if (
											    (len(self.conv_stride    ) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
											    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
											):
																	raise ValueError(
																	    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																	    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																	    F""" {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,"""
																	    F""" `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											lowerCamelCase								=		mask_time_prob
											lowerCamelCase								=		mask_time_length
											lowerCamelCase								=		mask_time_min_masks
											lowerCamelCase								=		mask_feature_prob
											lowerCamelCase								=		mask_feature_length
											lowerCamelCase								=		mask_feature_min_masks

											# ctc loss
											lowerCamelCase								=		ctc_loss_reduction
											lowerCamelCase								=		ctc_zero_infinity

											# adapter
											lowerCamelCase								=		add_adapter
											lowerCamelCase								=		adapter_kernel_size
											lowerCamelCase								=		adapter_stride
											lowerCamelCase								=		num_adapter_layers
											lowerCamelCase								=		output_hidden_size or hidden_size

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											lowerCamelCase								=		classifier_proj_size

											# XVector-specific parameters. Feel free to ignore for other classes.
											lowerCamelCase								=		list(__a    )
											lowerCamelCase								=		list(__a    )
											lowerCamelCase								=		list(__a    )
											lowerCamelCase								=		xvector_output_dim







					@property
					def 						_a      (self    ):






											'''simple docstring'''



											return math.prod(self.conv_stride    )




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]}""")