code: def convert_blip2_checkpoint(
      model_name, pytorch_dump_folder_path=None, push_to_hub=False
  ):
      """
      Copy/paste/tweak model's weights to Transformers design.
      """
      tokenizer = (
          AutoTokenizer.from_pretrained("facebook/opt-2.7b")
          if "opt" in model_name
          else AutoTokenizer.from_pretrained("google/flan-t5-xl")
      )
      eos_token_id = tokenizer(
          "\n", add_special_tokens=False
      ).input_ids[0]
      config, image_size = get_blip2_config(
          model_name, eos_token_id=eos_token_id
      )
  
      hf_model = Blip2ForConditionalGeneration(config).eval()
  
      model_name_to_original = {
          "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
          "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
          "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
          "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
          "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
          "blip2-flan-t5-xl-coco": (
              "blip2_t5",
              "caption_coco_flant5xl",
          ),
          "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
      }
  
      name, type = model_name_to_original[model_name]
  
      # note: this script is tested on 2 GPUs, as models are compared in float32,
      # which requires quite some memory. Hence loading both on a
      # separate device is the easiest to compare
      hf_model_device = "cuda:0" if torch.cuda.is_available() else "cpu"
      lavis_device = "cuda:1" if torch.cuda.is_available() else "cpu"
  
      # load original model
      print("Loading original model...")
      original_model, vis_processors, _ = load_model_and_preprocess(
          name=name, model_type=type, is_eval=True, device=lavis_device
      )
      original_model.eval()
      print("Done!")
  
      # update state dict keys
      state_dict = original_model.state_dict()
      rename_keys = create_rename_keys(config)
      for src, dest in rename_keys:
          rename_key(state_dict, src, dest)
  
      # some keys can be renamed efficiently
      for key, val in state_dict.copy().items():
          val = state_dict.pop(key)
          if key.startswith("Qformer.bert"):
              key = key.replace("Qformer.bert", "qformer")
          if "attention.self" in key:
              key = key.replace("self", "attention")
          if "opt_proj" in key:
              key = key.replace("opt_proj", "language_projection")
          if "t5_proj" in key:
              key = key.replace("t5_proj", "language_projection")
          if key.startswith("opt"):
              key = key.replace("opt", "language")
          if key.startswith("t5"):
              key = key.replace("t5", "language")
          state_dict[key] = val
  
      # read in qv biases
      read_in_q_v_bias(state_dict, config)
  
      missing_keys, unexpected_keys = hf_model.load_state_dict(
          state_dict, strict=False
      )
      assert len(missing_keys) == 0
      assert unexpected_keys == ["qformer.embeddings.position_ids"]
  
      image = load_demo_image()
      original_pixel_values = (
          vis_processors["eval"](image).unsqueeze(0).to(lavis_device)
      )
      input_ids = tokenizer(["\n"], return_tensors="pt").input_ids.to(
          hf_model_device
      )
  
      # create processor
      image_processor = BlipImageProcessor(
          size={"height": image_size, "width": image_size},
          image_mean=OPENAI_CLIP_MEAN,
          image_std=OPENAI_CLIP_STD,
      )
      processor = Blip2Processor(
          image_processor=image_processor, tokenizer=tokenizer
      )
      pixel_values = processor(
          images=image, return_tensors="pt"
      ).pixel_values.to(hf_model_device)
  
      # make sure processor creates exact same pixel values
      assert torch.allclose(
          pixel_values, original_pixel_values.to(pixel_values.device)
      )
  
      original_model.to(lavis_device)
      hf_model.to(hf_model_device)
      with torch.no_grad():
          if "opt" in model_name:
              original_logits = original_model(
                  {"image": original_pixel_values, "text_input": [""]}
              ).logits
              logits = hf_model(pixel_values, input_ids).logits
          else:
              original_logits = original_model(
                  {
                      "image": original_pixel_values,
                      "text_input": ["\n"],
                      "text_output": ["\n"],
                  }
              ).logits
              labels = input_ids.masked_fill(
                  input_ids == tokenizer.pad_token_id, -100
              )
              logits = hf_model(
                  pixel_values, input_ids, labels=labels
              ).logits
  
      assert original_logits.shape == logits.shape
      print(
          "First values of original logits:", original_logits[0, :3, :3]
      )
      print("First values of HF logits:", logits[0, :3, :3])
  
      # assert values
      assert torch.allclose(
          original_logits.to(logits.device), logits, atol=1e-4
      )
      print("Looks ok!")
  
      print("Generating a caption...")
      prompt = "Question: what object is in this image? Answer:"
      input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(
          hf_model_device
      )
  
      set_seed(42)
  
      original_outputs = original_model.generate(
          {"image": original_pixel_values, "prompt": prompt},
          use_nucleus_sampling=True,
      )
      outputs = hf_model.generate(
          pixel_values,
          input_ids,
          do_sample=True,
          num_beams=5,
          max_length=30,
          min_length=1,
          top_p=0.9,
          repetition_penalty=1.0,
          length_penalty=1.0,
          temperature=1,
      )
      output_text = processor.batch_decode(
          outputs, skip_special_tokens=True
      )
      output_text = [text.strip() for text in output_text]
      print("Original generation:", original_outputs)
      print("HF generation:", output_text)
  
      if pytorch_dump_folder_path is not None:
          processor.save_pretrained(pytorch_dump_folder_path)
          hf_model.save_pretrained(pytorch_dump_folder_path)
  
      if push_to_hub:
          processor.push_to_hub(f"nielsr/{model_name}")
          hf_model.push_to_hub(f"nielsr/{model_name}")
  
file_path: transformers/src/transformers/models/blip_2/convert_blip_2_original_to_pytorch.py
imports: import argparse
  import requests
  import torch
  from lavis.models import load_model_and_preprocess
  from PIL import Image
  from transformers import AutoTokenizer, Blip2Config, Blip2ForConditionalGeneration, Blip2Processor, Blip2VisionConfig, BlipImageProcessor, OPTConfig, T5Config, set_seed
  from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
label_desc: Use image node_id 6 for calling a global function with example usage: convert_blip2_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub) without return types