

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   (    A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Any:





      """simple docstring"""



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

      _lowercase			 =transforms.Compose(
          [
              transforms.Resize((image_size, image_size)   ,     interpolation=InterpolationMode.BICUBIC  ),
              transforms.ToTensor(),
              transforms.Normalize((0.48145466, 0.4578275, 0.40821073)   ,     (0.26862954, 0.26130258, 0.27577711)  ),
          ]  )
      _lowercase			 =transform(A__  ).unsqueeze(0  ).to(A__  )
      return image
def   a   (    A__			:     Optional[int]  )					->   int:





      """simple docstring"""



      if "visual_encoder" in key:
            _lowercase			 =re.sub('visual_encoder*'   ,     'vision_model.encoder'   ,     A__  )
      if "blocks" in key:
            _lowercase			 =re.sub(r'blocks'   ,     'layers'   ,     A__  )
      if "attn" in key:
            _lowercase			 =re.sub(r'attn'   ,     'self_attn'   ,     A__  )
      if "norm1" in key:
            _lowercase			 =re.sub(r'norm1'   ,     'layer_norm1'   ,     A__  )
      if "norm2" in key:
            _lowercase			 =re.sub(r'norm2'   ,     'layer_norm2'   ,     A__  )
      if "encoder.norm" in key:
            _lowercase			 =re.sub(r'encoder.norm'   ,     'post_layernorm'   ,     A__  )
      if "encoder.patch_embed.proj" in key:
            _lowercase			 =re.sub(r'encoder.patch_embed.proj'   ,     'embeddings.patch_embedding'   ,     A__  )

      if "encoder.pos_embed" in key:
            _lowercase			 =re.sub(r'encoder.pos_embed'   ,     'embeddings.position_embedding'   ,     A__  )
      if "encoder.cls_token" in key:
            _lowercase			 =re.sub(r'encoder.cls_token'   ,     'embeddings.class_embedding'   ,     A__  )

      if "self_attn" in key:
            _lowercase			 =re.sub(r'self_attn.proj'   ,     'self_attn.projection'   ,     A__  )

      return key
@torch.no_grad()
def   a   (    A__			:     Tuple   ,     A__			:     Optional[int]=None  )					->   List[str]:





      """simple docstring"""



      if config_path is not None:
            _lowercase			 =BlipConfig.from_pretrained(A__  )
      else:
            _lowercase			 =BlipConfig(projection_dim=512   ,     text_config={}   ,     vision_config={}  )

      _lowercase			 =BlipForConditionalGeneration(A__  ).eval()

      _lowercase			 ='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

      _lowercase			 =blip_decoder(pretrained=A__   ,     image_size=384   ,     vit='base'  )
      _lowercase			 =pt_model.eval()

      _lowercase			 =pt_model.state_dict()
      for key in modified_state_dict.copy():
            _lowercase			 =modified_state_dict.pop(A__  )
            _lowercase			 =rename_key(A__  )
            _lowercase			 =value

      hf_model.load_state_dict(A__  )

      _lowercase			 =384
      _lowercase			 =load_demo_image(image_size=A__   ,     device='cpu'  )
      _lowercase			 =BertTokenizer.from_pretrained('bert-base-uncased'  )
      _lowercase			 =tokenizer(['a picture of']  ).input_ids

      _lowercase			 =hf_model.generate(A__   ,     A__  )

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

      _lowercase			 =hf_model.generate(A__  )

      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(A__  )

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

      _lowercase			 =blip_vqa(pretrained=A__   ,     image_size=A__   ,     vit='base'  )
      vqa_model.eval()

      _lowercase			 =vqa_model.state_dict()
      for key in modified_state_dict.copy():
            _lowercase			 =modified_state_dict.pop(A__  )
            _lowercase			 =rename_key(A__  )
            _lowercase			 =value

      _lowercase			 =BlipForQuestionAnswering(A__  )

      hf_vqa_model.load_state_dict(A__  )

      _lowercase			 =['How many dogs are in this image?']
      _lowercase			 =tokenizer(A__   ,     return_tensors='pt'  ).input_ids

      _lowercase			 =hf_vqa_model.generate(A__   ,     A__  )
      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'  )

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

      _lowercase			 =blip_itm(pretrained=A__   ,     image_size=A__   ,     vit='base'  )
      itm_model.eval()

      _lowercase			 =itm_model.state_dict()
      for key in modified_state_dict.copy():
            _lowercase			 =modified_state_dict.pop(A__  )
            _lowercase			 =rename_key(A__  )
            _lowercase			 =value

      _lowercase			 =BlipForImageTextRetrieval(A__  )

      _lowercase			 =['A picture of a woman with a dog sitting in a beach']
      _lowercase			 =tokenizer(
          A__   ,     return_tensors='pt'   ,     padding='max_length'   ,     truncation=A__   ,     max_length=35   ,     ).input_ids

      hf_itm_model.load_state_dict(A__  )
      hf_itm_model.eval()

      _lowercase			 =hf_itm_model(A__   ,     A__   ,     use_itm_head=A__  )
      _lowercase			 =hf_itm_model(A__   ,     A__   ,     use_itm_head=A__  )

      assert out[0].item() == 0.2110687494277954
      assert torch.nn.functional.softmax(out_itm[0]   ,     dim=1  )[:, 1].item() == 0.45698845386505127

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


if __name__ == "__main__":
  lowercase_             =     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')
  lowercase_             =     parser.parse_args()

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





from __future__ import annotations

from typing import Any





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        _lowercase			 =num_of_nodes
        _lowercase			 =[]
        _lowercase			 ={}




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        self.m_edges.append([u_node, v_node, weight]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        if self.m_component[u_node] == u_node:
              return u_node
        return self.find_component(self.m_component[u_node]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if self.m_component[u_node] != u_node:
              for k in self.m_component:
                    _lowercase			 =self.find_component(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if component_size[u_node] <= component_size[v_node]:
              _lowercase			 =v_node
              component_size[v_node] += component_size[u_node]
              self.set_component(lowerCAmelCase      )

        elif component_size[u_node] >= component_size[v_node]:
              _lowercase			 =self.find_component(lowerCAmelCase      )
              component_size[u_node] += component_size[v_node]
              self.set_component(lowerCAmelCase      )




  def 						A__  (       self      )						->				None:
        '''simple docstring'''






        _lowercase			 =[]
        _lowercase			 =0

        _lowercase			 =[-1] * self.m_num_of_nodes

        # A list of components (initialized to all of the nodes)
        for node in range(self.m_num_of_nodes      ):
              self.m_component.update({node: node}      )
              component_size.append(1      )

        _lowercase			 =self.m_num_of_nodes

        while num_of_components > 1:
              for edge in self.m_edges:
                    _lowercase , _lowercase , _lowercase			 =edge

                    _lowercase			 =self.m_component[u]
                    _lowercase			 =self.m_component[v]

                    if u_component != v_component:

                          for component in (u_component, v_component):
                                if (
                                    minimum_weight_edge[component] == -1
                                    or minimum_weight_edge[component][2] > w
                                ):
                                      _lowercase			 =[u, v, w]

              for edge in minimum_weight_edge:
                    if isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
                          _lowercase , _lowercase , _lowercase			 =edge

                          _lowercase			 =self.m_component[u]
                          _lowercase			 =self.m_component[v]

                          if u_component != v_component:
                                mst_weight += w
                                self.union(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )
                                print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''      )
                                num_of_components -= 1

              _lowercase			 =[-1] * self.m_num_of_nodes
        print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''      )
def   a   (    )					->   None:





      """simple docstring"""





if __name__ == "__main__":
  import doctest

  doctest.testmod()


