


import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
 UpperCamelCase__					:    Dict          =   Image.open(requests.get(__SCREAMING_SNAKE_CASE    , stream=__SCREAMING_SNAKE_CASE     ).raw     ).convert('''RGB'''     )

 UpperCamelCase__					:    Any          =   transforms.Compose(
     [
         transforms.Resize((image_size, image_size)    , interpolation=InterpolationMode.BICUBIC     ),
         transforms.ToTensor(),
         transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)    , (0.2686_2954, 0.2613_0258, 0.2757_7711)     ),
     ]     )
 UpperCamelCase__					:    str          =   transform(__SCREAMING_SNAKE_CASE     ).unsqueeze(0     ).to(__SCREAMING_SNAKE_CASE     )
 return image

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]     ):







 """simple docstring"""



 if "visual_encoder" in key:
  UpperCamelCase__					:    Dict          =   re.sub('''visual_encoder*'''    , '''vision_model.encoder'''    , __SCREAMING_SNAKE_CASE     )
 if "blocks" in key:
  UpperCamelCase__					:    Dict          =   re.sub(r'''blocks'''    , '''layers'''    , __SCREAMING_SNAKE_CASE     )
 if "attn" in key:
  UpperCamelCase__					:    Tuple          =   re.sub(r'''attn'''    , '''self_attn'''    , __SCREAMING_SNAKE_CASE     )
 if "norm1" in key:
  UpperCamelCase__					:    Union[str, Any]          =   re.sub(r'''norm1'''    , '''layer_norm1'''    , __SCREAMING_SNAKE_CASE     )
 if "norm2" in key:
  UpperCamelCase__					:    Optional[int]          =   re.sub(r'''norm2'''    , '''layer_norm2'''    , __SCREAMING_SNAKE_CASE     )
 if "encoder.norm" in key:
  UpperCamelCase__					:    str          =   re.sub(r'''encoder.norm'''    , '''post_layernorm'''    , __SCREAMING_SNAKE_CASE     )
 if "encoder.patch_embed.proj" in key:
  UpperCamelCase__					:    Union[str, Any]          =   re.sub(r'''encoder.patch_embed.proj'''    , '''embeddings.patch_embedding'''    , __SCREAMING_SNAKE_CASE     )

 if "encoder.pos_embed" in key:
  UpperCamelCase__					:    Optional[int]          =   re.sub(r'''encoder.pos_embed'''    , '''embeddings.position_embedding'''    , __SCREAMING_SNAKE_CASE     )
 if "encoder.cls_token" in key:
  UpperCamelCase__					:    List[Any]          =   re.sub(r'''encoder.cls_token'''    , '''embeddings.class_embedding'''    , __SCREAMING_SNAKE_CASE     )

 if "self_attn" in key:
  UpperCamelCase__					:    Union[str, Any]          =   re.sub(r'''self_attn.proj'''    , '''self_attn.projection'''    , __SCREAMING_SNAKE_CASE     )

 return key

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				str=None     ):







 """simple docstring"""



 if config_path is not None:
  UpperCamelCase__					:    List[Any]          =   BlipConfig.from_pretrained(__SCREAMING_SNAKE_CASE     )
 else:
  UpperCamelCase__					:    Dict          =   BlipConfig(projection_dim=512    , text_config={}    , vision_config={}     )

 UpperCamelCase__					:    Any          =   BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE     ).eval()

 UpperCamelCase__					:    List[str]          =   '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

 UpperCamelCase__					:    Optional[int]          =   blip_decoder(pretrained=__SCREAMING_SNAKE_CASE    , image_size=384    , vit='''base'''     )
 UpperCamelCase__					:    str          =   pt_model.eval()

 UpperCamelCase__					:    Optional[Any]          =   pt_model.state_dict()
 for key in modified_state_dict.copy():
  UpperCamelCase__					:    str          =   modified_state_dict.pop(__SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    List[str]          =   rename_key(__SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    Dict          =   value

 hf_model.load_state_dict(__SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Any          =   384
 UpperCamelCase__					:    str          =   load_demo_image(image_size=__SCREAMING_SNAKE_CASE    , device='''cpu'''     )
 UpperCamelCase__					:    List[Any]          =   BertTokenizer.from_pretrained('''bert-base-uncased'''     )
 UpperCamelCase__					:    Any          =   tokenizer(['''a picture of''']     ).input_ids

 UpperCamelCase__					:    str          =   hf_model.generate(__SCREAMING_SNAKE_CASE    , __SCREAMING_SNAKE_CASE     )

 assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

 UpperCamelCase__					:    Optional[int]          =   hf_model.generate(__SCREAMING_SNAKE_CASE     )

 assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

 if pytorch_dump_folder_path is not None:
  hf_model.save_pretrained(__SCREAMING_SNAKE_CASE     )

 # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
 UpperCamelCase__					:    List[str]          =   (
     '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
 )

 UpperCamelCase__					:    Optional[Any]          =   blip_vqa(pretrained=__SCREAMING_SNAKE_CASE    , image_size=__SCREAMING_SNAKE_CASE    , vit='''base'''     )
 vqa_model.eval()

 UpperCamelCase__					:    Union[str, Any]          =   vqa_model.state_dict()
 for key in modified_state_dict.copy():
  UpperCamelCase__					:    List[Any]          =   modified_state_dict.pop(__SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    str          =   rename_key(__SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    Union[str, Any]          =   value

 UpperCamelCase__					:    Dict          =   BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE     )

 hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Optional[int]          =   ['''How many dogs are in this image?''']
 UpperCamelCase__					:    Optional[Any]          =   tokenizer(__SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     ).input_ids

 UpperCamelCase__					:    Optional[Any]          =   hf_vqa_model.generate(__SCREAMING_SNAKE_CASE    , __SCREAMING_SNAKE_CASE     )
 print(tokenizer.decode(answer[0]     )     )

 assert tokenizer.decode(answer[0]     ) == "[UNK] 1 [SEP]"
 if pytorch_dump_folder_path is not None:
  hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''     )

 UpperCamelCase__					:    Union[str, Any]          =   '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

 UpperCamelCase__					:    Any          =   blip_itm(pretrained=__SCREAMING_SNAKE_CASE    , image_size=__SCREAMING_SNAKE_CASE    , vit='''base'''     )
 itm_model.eval()

 UpperCamelCase__					:    Any          =   itm_model.state_dict()
 for key in modified_state_dict.copy():
  UpperCamelCase__					:    Union[str, Any]          =   modified_state_dict.pop(__SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    Optional[int]          =   rename_key(__SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    Optional[Any]          =   value

 UpperCamelCase__					:    List[Any]          =   BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Dict          =   ['''A picture of a woman with a dog sitting in a beach''']
 UpperCamelCase__					:    str          =   tokenizer(
     __SCREAMING_SNAKE_CASE    , return_tensors='''pt'''    , padding='''max_length'''    , truncation=__SCREAMING_SNAKE_CASE    , max_length=35    , ).input_ids

 hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE     )
 hf_itm_model.eval()

 UpperCamelCase__					:    Union[str, Any]          =   hf_itm_model(__SCREAMING_SNAKE_CASE    , __SCREAMING_SNAKE_CASE    , use_itm_head=__SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Union[str, Any]          =   hf_itm_model(__SCREAMING_SNAKE_CASE    , __SCREAMING_SNAKE_CASE    , use_itm_head=__SCREAMING_SNAKE_CASE     )

 assert out[0].item() == 0.2110_6874_9427_7954
 assert torch.nn.functional.softmax(out_itm[0]    , dim=1     )[:, 1].item() == 0.4_5698_8453_8650_5127

 if pytorch_dump_folder_path is not None:
  hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''     )


if __name__ == "__main__":
 __UpperCamelCase       :       List[str]						       =				argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 __UpperCamelCase       :       Tuple						       =				parser.parse_args()

 convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






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]}")


