


import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


_SCREAMING_SNAKE_CASE      :			str      				=   logging.get_logger(__name__)

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		CLIPConfig

    __magic_name__        =		['CLIPEncoderLayer']
    def __init__(	self      , __snake_case   ):
         super().__init__(__snake_case   )

         snake_case					=    CLIPVisionModelWithProjection(config.vision_config   )

         snake_case					=    nn.Linear(config.vision_config.projection_dim      , 1   )
         snake_case					=    nn.Linear(config.vision_config.projection_dim      , 1   )




    @torch.no_grad()
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case=0.5      , __snake_case=0.5   ):
         snake_case					=    self.vision_model(__snake_case   )[0]

         snake_case					=    self.p_head(__snake_case   )
         snake_case					=    nsfw_detected.flatten()
         snake_case					=    nsfw_detected > p_threshold
         snake_case					=    nsfw_detected.tolist()

         if any(__snake_case   ):
              logger.warning(
                  '''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
                  ''' Try again with a different prompt and/or seed.'''   )

         for idx, nsfw_detected_ in enumerate(__snake_case   ):
              if nsfw_detected_:
                   snake_case					=    np.zeros(images[idx].shape   )

         snake_case					=    self.w_head(__snake_case   )
         snake_case					=    watermark_detected.flatten()
         snake_case					=    watermark_detected > w_threshold
         snake_case					=    watermark_detected.tolist()

         if any(__snake_case   ):
              logger.warning(
                  '''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
                  ''' Try again with a different prompt and/or seed.'''   )

         for idx, watermark_detected_ in enumerate(__snake_case   ):
              if watermark_detected_:
                   snake_case					=    np.zeros(images[idx].shape   )

         return images, nsfw_detected, watermark_detected








from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_SCREAMING_SNAKE_CASE      :			str      				=   "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def       UpperCAmelCase__						(UpperCamelCase_ = "mumbai"    ):



     """simple docstring"""






     snake_case					=    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'''}    ):
          snake_case					=    job.find('''a'''							,attrs={'''data-tn-element''': '''jobTitle'''}    ).text.strip()
          snake_case					=    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]}''')




