






"""simple docstring"""





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     __lowercase					(    a :				List[str]       ,		a :				str ):
      __snake_case      :    int  			='''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
      __snake_case      :    Any  			=Image.open(requests.get(UpperCAmelCase__       ,		stream=UpperCAmelCase__ ).raw ).convert('''RGB''' )

      __snake_case      :    Tuple  			=transforms.Compose(
          [
              transforms.Resize((image_size, image_size)       ,		interpolation=InterpolationMode.BICUBIC ),
              transforms.ToTensor(),
              transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)       ,		(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1) ),
          ] )
      __snake_case      :    int  			=transform(UpperCAmelCase__ ).unsqueeze(0 ).to(UpperCAmelCase__ )
      return image



def     __lowercase					(    a :				Dict ):

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

      if "encoder.pos_embed" in key:
            __snake_case      :    Union[str, Any]  			=re.sub(R'''encoder.pos_embed'''       ,		'''embeddings.position_embedding'''       ,		UpperCAmelCase__ )
      if "encoder.cls_token" in key:
            __snake_case      :    int  			=re.sub(R'''encoder.cls_token'''       ,		'''embeddings.class_embedding'''       ,		UpperCAmelCase__ )

      if "self_attn" in key:
            __snake_case      :    Optional[Any]  			=re.sub(R'''self_attn.proj'''       ,		'''self_attn.projection'''       ,		UpperCAmelCase__ )

      return key



@torch.no_grad()
def     __lowercase					(    a :				int       ,		a :				List[str]=None ):

      if config_path is not None:
            __snake_case      :    str  			=BlipConfig.from_pretrained(UpperCAmelCase__ )
      else:
            __snake_case      :    int  			=BlipConfig(projection_dim=512       ,		text_config={}       ,		vision_config={} )

      __snake_case      :    int  			=BlipForConditionalGeneration(UpperCAmelCase__ ).eval()

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

      __snake_case      :    Optional[int]  			=blip_decoder(pretrained=UpperCAmelCase__       ,		image_size=384       ,		vit='''base''' )
      __snake_case      :    Optional[Any]  			=pt_model.eval()

      __snake_case      :    Union[str, Any]  			=pt_model.state_dict()
      for key in modified_state_dict.copy():
            __snake_case      :    Tuple  			=modified_state_dict.pop(UpperCAmelCase__ )
            __snake_case      :    Optional[int]  			=rename_key(UpperCAmelCase__ )
            __snake_case      :    int  			=value

      hf_model.load_state_dict(UpperCAmelCase__ )

      __snake_case      :    str  			=384
      __snake_case      :    Union[str, Any]  			=load_demo_image(image_size=UpperCAmelCase__       ,		device='''cpu''' )
      __snake_case      :    Optional[Any]  			=BertTokenizer.from_pretrained('''bert-base-uncased''' )
      __snake_case      :    str  			=tokenizer(['''a picture of'''] ).input_ids

      __snake_case      :    Tuple  			=hf_model.generate(UpperCAmelCase__       ,		UpperCAmelCase__ )

      assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

      __snake_case      :    int  			=hf_model.generate(UpperCAmelCase__ )

      assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

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

      # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
      __snake_case      :    Tuple  			=(
          '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
      )

      __snake_case      :    List[str]  			=blip_vqa(pretrained=UpperCAmelCase__       ,		image_size=UpperCAmelCase__       ,		vit='''base''' )
      vqa_model.eval()

      __snake_case      :    int  			=vqa_model.state_dict()
      for key in modified_state_dict.copy():
            __snake_case      :    Dict  			=modified_state_dict.pop(UpperCAmelCase__ )
            __snake_case      :    Union[str, Any]  			=rename_key(UpperCAmelCase__ )
            __snake_case      :    Optional[Any]  			=value

      __snake_case      :    Optional[Any]  			=BlipForQuestionAnswering(UpperCAmelCase__ )

      hf_vqa_model.load_state_dict(UpperCAmelCase__ )

      __snake_case      :    Tuple  			=['''How many dogs are in this image?''']
      __snake_case      :    List[str]  			=tokenizer(UpperCAmelCase__       ,		return_tensors='''pt''' ).input_ids

      __snake_case      :    Dict  			=hf_vqa_model.generate(UpperCAmelCase__       ,		UpperCAmelCase__ )
      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''' )

      __snake_case      :    str  			='''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

      __snake_case      :    Tuple  			=blip_itm(pretrained=UpperCAmelCase__       ,		image_size=UpperCAmelCase__       ,		vit='''base''' )
      itm_model.eval()

      __snake_case      :    Dict  			=itm_model.state_dict()
      for key in modified_state_dict.copy():
            __snake_case      :    List[Any]  			=modified_state_dict.pop(UpperCAmelCase__ )
            __snake_case      :    Union[str, Any]  			=rename_key(UpperCAmelCase__ )
            __snake_case      :    Any  			=value

      __snake_case      :    List[str]  			=BlipForImageTextRetrieval(UpperCAmelCase__ )

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

      hf_itm_model.load_state_dict(UpperCAmelCase__ )
      hf_itm_model.eval()

      __snake_case      :    int  			=hf_itm_model(UpperCAmelCase__       ,		UpperCAmelCase__       ,		use_itm_head=UpperCAmelCase__ )
      __snake_case      :    Dict  			=hf_itm_model(UpperCAmelCase__       ,		UpperCAmelCase__       ,		use_itm_head=UpperCAmelCase__ )

      assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
      assert torch.nn.functional.softmax(out_itm[0]       ,		dim=1 )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
    UpperCamelCase_	:				Optional[Any]										= 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_	:				Any										= parser.parse_args()

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










"""simple docstring"""





from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase_	:				Tuple										= {"""UserAgent""": UserAgent().random}



def     __lowercase					(    a :				str )					->					dict:
      __snake_case      :    Union[str, Any]  			=script.contents[0]
      __snake_case      :    List[str]  			=json.loads(data[data.find('''{"config"''' ) : -1] )
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]


class      _lowercase :


      def __init__(    self				:	int							,   a				:	Dict						):





            """simple docstring"""







            __snake_case      :    Dict  			=f'''https://www.instagram.com/{username}/'''
            __snake_case      :    str  			=self.get_json()


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=requests.get(self.url							,   headers=a						).text
            __snake_case      :    int  			=BeautifulSoup(a							,   '''html.parser'''						).find_all('''script'''						)
            try:
                  return extract_user_profile(scripts[4]						)
            except (json.decoder.JSONDecodeError, KeyError):
                  return extract_user_profile(scripts[3]						)


      def __repr__(    self				:	int						):





            """simple docstring"""







            return f'''{self.__class__.__name__}(\'{self.username}\')'''


      def __str__(    self				:	Any						):





            """simple docstring"""







            return f'''{self.fullname} ({self.username}) is {self.biography}'''


      @property
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            return self.user_data["username"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["full_name"]


      @property
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            return self.user_data["biography"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["business_email"]


      @property
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            return self.user_data["external_url"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["edge_followed_by"]["count"]


      @property
      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            return self.user_data["edge_follow"]["count"]


      @property
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            return self.user_data["edge_owner_to_timeline_media"]["count"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["profile_pic_url_hd"]


      @property
      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            return self.user_data["is_verified"]







      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["is_private"]



def     __lowercase					(    a :				str = "github" )					->					None:
      import os

      if os.environ.get('''CI''' ):
            return  # test failing on GitHub Actions
      __snake_case      :    Union[str, Any]  			=InstagramUser(a )
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data       ,		a )
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 150
      assert instagram_user.number_of_followers > 120_000
      assert instagram_user.number_of_followings > 15
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    UpperCamelCase_	:				Optional[int]										= InstagramUser("""github""")
    print(instagram_user)
    print(F'''{instagram_user.number_of_posts = }''')
    print(F'''{instagram_user.number_of_followers = }''')
    print(F'''{instagram_user.number_of_followings = }''')
    print(F'''{instagram_user.email = }''')
    print(F'''{instagram_user.website = }''')
    print(F'''{instagram_user.profile_picture_url = }''')
    print(F'''{instagram_user.is_verified = }''')
    print(F'''{instagram_user.is_private = }''')


