





"""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__    (					lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	int						=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
      _UpperCamelCase      :	Any						=       Image.open(requests.get(lowercase_		,stream=lowercase_			).raw			).convert("RGB"			)

      _UpperCamelCase      :	Dict						=       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      :	int						=       transform(lowercase_			).unsqueeze(0			).to(lowercase_			)
      return image




def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

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

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

      if "self_attn" in key:
            _UpperCamelCase      :	Optional[int]						=       re.sub(r"self_attn.proj"		,"self_attn.projection"		,lowercase_			)

      return key




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_=None			)					->		Tuple:




      """simple docstring"""

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

      _UpperCamelCase      :	Optional[int]						=       BlipForConditionalGeneration(lowercase_			).eval()

      _UpperCamelCase      :	Dict						=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

      _UpperCamelCase      :	Union[str, Any]						=       blip_decoder(pretrained=lowercase_		,image_size=384		,vit="base"			)
      _UpperCamelCase      :	Union[str, Any]						=       pt_model.eval()

      _UpperCamelCase      :	Optional[Any]						=       pt_model.state_dict()
      for key in modified_state_dict.copy():
            _UpperCamelCase      :	List[Any]						=       modified_state_dict.pop(lowercase_			)
            _UpperCamelCase      :	List[Any]						=       rename_key(lowercase_			)
            _UpperCamelCase      :	Any						=       value

      hf_model.load_state_dict(lowercase_			)

      _UpperCamelCase      :	Optional[int]						=       384
      _UpperCamelCase      :	int						=       load_demo_image(image_size=lowercase_		,device="cpu"			)
      _UpperCamelCase      :	List[Any]						=       BertTokenizer.from_pretrained("bert-base-uncased"			)
      _UpperCamelCase      :	Union[str, Any]						=       tokenizer(["a picture of"]			).input_ids

      _UpperCamelCase      :	Optional[int]						=       hf_model.generate(lowercase_		,lowercase_			)

      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]

      _UpperCamelCase      :	List[str]						=       hf_model.generate(lowercase_			)

      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(lowercase_			)

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

      _UpperCamelCase      :	List[str]						=       blip_vqa(pretrained=lowercase_		,image_size=lowercase_		,vit="base"			)
      vqa_model.eval()

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

      _UpperCamelCase      :	List[Any]						=       BlipForQuestionAnswering(lowercase_			)

      hf_vqa_model.load_state_dict(lowercase_			)

      _UpperCamelCase      :	Any						=       ["How many dogs are in this image?"]
      _UpperCamelCase      :	int						=       tokenizer(lowercase_		,return_tensors="pt"			).input_ids

      _UpperCamelCase      :	Tuple						=       hf_vqa_model.generate(lowercase_		,lowercase_			)
      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      :	List[Any]						=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

      _UpperCamelCase      :	Optional[Any]						=       blip_itm(pretrained=lowercase_		,image_size=lowercase_		,vit="base"			)
      itm_model.eval()

      _UpperCamelCase      :	str						=       itm_model.state_dict()
      for key in modified_state_dict.copy():
            _UpperCamelCase      :	Optional[Any]						=       modified_state_dict.pop(lowercase_			)
            _UpperCamelCase      :	Tuple						=       rename_key(lowercase_			)
            _UpperCamelCase      :	List[str]						=       value

      _UpperCamelCase      :	Union[str, Any]						=       BlipForImageTextRetrieval(lowercase_			)

      _UpperCamelCase      :	Dict						=       ["A picture of a woman with a dog sitting in a beach"]
      _UpperCamelCase      :	List[Any]						=       tokenizer(
          lowercase_		,return_tensors="pt"		,padding="max_length"		,truncation=lowercase_		,max_length=35		,).input_ids

      hf_itm_model.load_state_dict(lowercase_			)
      hf_itm_model.eval()

      _UpperCamelCase      :	Optional[Any]						=       hf_itm_model(lowercase_		,lowercase_		,use_itm_head=lowercase_			)
      _UpperCamelCase      :	Any						=       hf_itm_model(lowercase_		,lowercase_		,use_itm_head=lowercase_			)

      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__":
  lowerCamelCase__				  =   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")
  lowerCamelCase__				  =   parser.parse_args()

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







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
