


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCamelCase       :       int						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Optional[int]						       =				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[str]			=      'conditional_detr'
     A:		Union[str, Any]			=      ['past_key_values']
     A:		List[str]			=      {
         'hidden_size': 'd_model',
         'num_attention_heads': 'encoder_attention_heads',
     }


     def __init__(      self							:     List[str]							,	lowerCamelCase__							:     Tuple=True							,	lowerCamelCase__							:     List[Any]=None							,	lowerCamelCase__							:     Tuple=3							,	lowerCamelCase__							:     Optional[Any]=300							,	lowerCamelCase__							:     Dict=6							,	lowerCamelCase__							:     str=2048							,	lowerCamelCase__							:     Optional[Any]=8							,	lowerCamelCase__							:     Any=6							,	lowerCamelCase__							:     Optional[Any]=2048							,	lowerCamelCase__							:     Tuple=8							,	lowerCamelCase__							:     int=0.0							,	lowerCamelCase__							:     List[Any]=0.0							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Optional[int]="relu"							,	lowerCamelCase__							:     List[str]=256							,	lowerCamelCase__							:     int=0.1							,	lowerCamelCase__							:     List[Any]=0.0							,	lowerCamelCase__							:     str=0.0							,	lowerCamelCase__							:     Optional[int]=0.02							,	lowerCamelCase__							:     Dict=1.0							,	lowerCamelCase__							:     Union[str, Any]=False							,	lowerCamelCase__							:     List[Any]="sine"							,	lowerCamelCase__							:     Any="resnet50"							,	lowerCamelCase__							:     Dict=True							,	lowerCamelCase__							:     List[Any]=False							,	lowerCamelCase__							:     Optional[Any]=2							,	lowerCamelCase__							:     int=5							,	lowerCamelCase__							:     Tuple=2							,	lowerCamelCase__							:     Optional[int]=1							,	lowerCamelCase__							:     int=1							,	lowerCamelCase__							:     Dict=2							,	lowerCamelCase__							:     List[Any]=5							,	lowerCamelCase__							:     Optional[Any]=2							,	lowerCamelCase__							:     List[str]=0.25							,	**lowerCamelCase__							:     Union[str, Any]							,	)  ->      Union[str, Any]:





      '''simple docstring'''






      if backbone_config is not None and use_timm_backbone:
       raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''			)

      if not use_timm_backbone:
       if backbone_config is None:
        logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''			)
        UpperCamelCase__					:    List[str]          =   CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']			)
       elif isinstance(lowerCamelCase__							,	lowerCamelCase__			):
        UpperCamelCase__					:    Optional[Any]          =   backbone_config.get('''model_type'''			)
        UpperCamelCase__					:    Dict          =   CONFIG_MAPPING[backbone_model_type]
        UpperCamelCase__					:    Union[str, Any]          =   config_class.from_dict(lowerCamelCase__			)

      UpperCamelCase__					:    str          =   use_timm_backbone
      UpperCamelCase__					:    Optional[int]          =   backbone_config
      UpperCamelCase__					:    Optional[int]          =   num_channels
      UpperCamelCase__					:    List[Any]          =   num_queries
      UpperCamelCase__					:    Any          =   d_model
      UpperCamelCase__					:    Optional[Any]          =   encoder_ffn_dim
      UpperCamelCase__					:    List[Any]          =   encoder_layers
      UpperCamelCase__					:    Dict          =   encoder_attention_heads
      UpperCamelCase__					:    int          =   decoder_ffn_dim
      UpperCamelCase__					:    Dict          =   decoder_layers
      UpperCamelCase__					:    Optional[int]          =   decoder_attention_heads
      UpperCamelCase__					:    Tuple          =   dropout
      UpperCamelCase__					:    Any          =   attention_dropout
      UpperCamelCase__					:    Optional[Any]          =   activation_dropout
      UpperCamelCase__					:    Union[str, Any]          =   activation_function
      UpperCamelCase__					:    List[Any]          =   init_std
      UpperCamelCase__					:    Optional[Any]          =   init_xavier_std
      UpperCamelCase__					:    Any          =   encoder_layerdrop
      UpperCamelCase__					:    Optional[Any]          =   decoder_layerdrop
      UpperCamelCase__					:    Optional[int]          =   encoder_layers
      UpperCamelCase__					:    Any          =   auxiliary_loss
      UpperCamelCase__					:    Tuple          =   position_embedding_type
      UpperCamelCase__					:    Tuple          =   backbone
      UpperCamelCase__					:    List[str]          =   use_pretrained_backbone
      UpperCamelCase__					:    str          =   dilation
      # Hungarian matcher
      UpperCamelCase__					:    int          =   class_cost
      UpperCamelCase__					:    int          =   bbox_cost
      UpperCamelCase__					:    Optional[Any]          =   giou_cost
      # Loss coefficients
      UpperCamelCase__					:    int          =   mask_loss_coefficient
      UpperCamelCase__					:    Optional[int]          =   dice_loss_coefficient
      UpperCamelCase__					:    List[Any]          =   cls_loss_coefficient
      UpperCamelCase__					:    Dict          =   bbox_loss_coefficient
      UpperCamelCase__					:    Dict          =   giou_loss_coefficient
      UpperCamelCase__					:    Dict          =   focal_alpha
      super().__init__(is_encoder_decoder=lowerCamelCase__							,	**lowerCamelCase__			)


     @property
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      int:





      '''simple docstring'''






      return self.encoder_attention_heads


     @property
     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      int:





      '''simple docstring'''






      return self.d_model


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





      '''simple docstring'''






      UpperCamelCase__					:    str          =   copy.deepcopy(self.__dict__			)
      if self.backbone_config is not None:
       UpperCamelCase__					:    str          =   self.backbone_config.to_dict()
      UpperCamelCase__					:    List[Any]          =   self.__class__.model_type
      return output



class       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[int]			=      version.parse("1.11")


     @property
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ('''pixel_mask''', {0: '''batch'''}),
          ]			)


     @property
     def      UpperCAmelCase__	(      self							:     Any			)  ->      float:





      '''simple docstring'''






      return 1E-5


     @property
     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      int:





      '''simple docstring'''






      return 12






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]}")


