code: def convert_git_checkpoint(
      model_name, pytorch_dump_folder_path, push_to_hub=False
  ):
      """
      Copy/paste/tweak model's weights to our GIT structure.
      """
  
      model_name_to_url = {
          "git-base": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE/snapshot/model.pt",
          "git-base-coco": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_COCO/snapshot/model.pt",
          "git-base-textcaps": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_TEXTCAPS/snapshot/model.pt",
          "git-base-vqav2": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_VQAv2/snapshot/model.pt",
          "git-base-textvqa": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_TEXTVQA/snapshot/model.pt",  # todo
          "git-base-vatex": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_VATEX/snapshot/model.pt",
          "git-base-msrvtt-qa": (
              "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_MSRVTT_QA/snapshot/model.pt"
          ),
          "git-large": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE/snapshot/model.pt",
          "git-large-coco": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_COCO/snapshot/model.pt",
          "git-large-textcaps": (
              "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_TEXTCAPS/snapshot/model.pt"
          ),
          "git-large-vqav2": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_VQAv2/snapshot/model.pt",
          "git-large-textvqa": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_TEXTVQA/snapshot/model.pt",
          "git-large-vatex": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_VATEX/snapshot/model.pt",
          "git-large-msrvtt-qa": (
              "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_MSRVTT_QA/snapshot/model.pt"
          ),
          "git-large-r": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_R/snapshot/model.pt",
          "git-large-r-coco": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_R_COCO/snapshot/model.pt",
          "git-large-r-textcaps": (
              "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_R_TEXTCAPS/snapshot/model.pt"
          ),
      }
  
      model_name_to_path = {
          "git-large": "/Users/nielsrogge/Documents/GIT/git_large_model.pt",
          "git-large-coco": "/Users/nielsrogge/Documents/GIT/git_large_coco_model.pt",
          "git-large-textcaps": "/Users/nielsrogge/Documents/GIT/git_large_textcaps_model.pt",
          "git-large-vqav2": "/Users/nielsrogge/Documents/GIT/git_large_vqav2_model.pt",
          "git-large-textvqa": "/Users/nielsrogge/Documents/GIT/git_large_textvqa_model.pt",
      }
  
      # define GIT configuration based on model name
      config, image_size, is_video = get_git_config(model_name)
      if (
          "large" in model_name
          and not is_video
          and "large-r" not in model_name
      ):
          # large checkpoints take way too long to download
          checkpoint_path = model_name_to_path[model_name]
          state_dict = torch.load(checkpoint_path, map_location="cpu")[
              "model"
          ]
      else:
          checkpoint_url = model_name_to_url[model_name]
          state_dict = torch.hub.load_state_dict_from_url(
              checkpoint_url, map_location="cpu", file_name=model_name
          )["model"]
      # rename keys
      prefix = "module." if model_name == "git-base" else ""
      rename_keys = create_rename_keys(config, prefix=prefix)
      for src, dest in rename_keys:
          rename_key(state_dict, src, dest)
      read_in_q_k_v(state_dict, config, prefix=prefix)
  
      # load HuggingFace model
      model = GitForCausalLM(config)
      missing_keys, unexpected_keys = model.load_state_dict(
          state_dict, strict=False
      )
      model.eval()
  
      print("Missing keys:", missing_keys)
      print("Unexpected keys:", unexpected_keys)
  
      assert missing_keys == [
          "git.embeddings.position_ids",
          "git.image_encoder.vision_model.embeddings.position_ids",
      ]
      assert unexpected_keys == [
          "git.image_encoder.visual_projection.weight"
      ]
  
      # verify results
      image_processor = (
          VideoMAEImageProcessor(
              size={"shortest_edge": image_size},
              crop_size={"height": image_size, "width": image_size},
          )
          if is_video
          else CLIPImageProcessor(
              size={"shortest_edge": image_size},
              crop_size={"height": image_size, "width": image_size},
          )
      )
      tokenizer = AutoTokenizer.from_pretrained(
          "bert-base-uncased",
          model_input_names=["input_ids", "attention_mask"],
      )
      processor = GitProcessor(
          tokenizer=tokenizer, image_processor=image_processor
      )
  
      if is_video:
          video = prepare_video()
          pixel_values = processor(
              images=list(video), return_tensors="pt"
          ).pixel_values
      else:
          image = prepare_img(model_name)
          image_transforms = Compose(
              [
                  Resize(image_size, interpolation=Image.BICUBIC),
                  CenterCrop(image_size),
                  ToTensor(),
                  Normalize(
                      (0.48145466, 0.4578275, 0.40821073),
                      (0.26862954, 0.26130258, 0.27577711),
                  ),
              ]
          )
          original_pixel_values = image_transforms(image).unsqueeze(0)
          pixel_values = processor(
              images=image, return_tensors="pt"
          ).pixel_values
  
          assert torch.allclose(pixel_values, original_pixel_values)
  
      input_ids = torch.tensor([[101]])
      outputs = model(input_ids, pixel_values=pixel_values)
      logits = outputs.logits
      print("Logits:", logits[0, -1, :3])
  
      if model_name == "git-base":
          expected_slice_logits = torch.tensor(
              [-1.2832, -1.2835, -1.2840]
          )
      elif model_name == "git-base-coco":
          expected_slice_logits = torch.tensor(
              [-0.9925, -0.9930, -0.9935]
          )
      elif model_name == "git-base-textcaps":
          expected_slice_logits = torch.tensor(
              [-1.2980, -1.2983, -1.2985]
          )
      elif model_name == "git-base-vqav2":
          expected_slice_logits = torch.tensor(
              [-0.8570, -0.8568, -0.8561]
          )
      elif model_name == "git-base-textvqa":
          expected_slice_logits = torch.tensor(
              [-1.4085, -1.4083, -1.4082]
          )
      elif model_name == "git-base-vatex":
          expected_slice_logits = torch.tensor(
              [-1.3451, -1.3447, -1.3447]
          )
      elif model_name == "git-base-msrvtt-qa":
          expected_slice_logits = torch.tensor(
              [-0.8554, -0.8550, -0.8540]
          )
      elif model_name == "git-large":
          expected_slice_logits = torch.tensor(
              [-1.1708, -1.1707, -1.1705]
          )
      elif model_name == "git-large-coco":
          expected_slice_logits = torch.tensor(
              [-1.0425, -1.0423, -1.0422]
          )
      elif model_name == "git-large-textcaps":
          expected_slice_logits = torch.tensor(
              [-1.2705, -1.2708, -1.2706]
          )
      elif model_name == "git-large-vqav2":
          expected_slice_logits = torch.tensor(
              [-0.7042, -0.7043, -0.7043]
          )
      elif model_name == "git-large-textvqa":
          expected_slice_logits = torch.tensor(
              [-0.8590, -0.8592, -0.8590]
          )
      elif model_name == "git-large-vatex":
          expected_slice_logits = torch.tensor(
              [-1.0113, -1.0114, -1.0113]
          )
      elif model_name == "git-large-msrvtt-qa":
          expected_slice_logits = torch.tensor([0.0130, 0.0134, 0.0131])
      elif model_name == "git-large-r":
          expected_slice_logits = torch.tensor(
              [-1.1283, -1.1285, -1.1286]
          )
      elif model_name == "git-large-r-coco":
          expected_slice_logits = torch.tensor(
              [-0.9641, -0.9641, -0.9641]
          )
      elif model_name == "git-large-r-textcaps":
          expected_slice_logits = torch.tensor(
              [-1.1121, -1.1120, -1.1124]
          )
  
      assert torch.allclose(
          logits[0, -1, :3], expected_slice_logits, atol=1e-4
      )
      print("Looks ok!")
  
      prompt = ""
      if "textvqa" in model_name:
          prompt = "what does the front of the bus say at the top?"
      elif "msrvtt-qa" in model_name:
          prompt = "what does the woman eat?"
      elif "vqa" in model_name:
          prompt = "what are the cats doing?"
      input_ids = tokenizer(prompt, add_special_tokens=False).input_ids
      input_ids = [processor.tokenizer.cls_token_id] + input_ids
      input_ids = torch.tensor(input_ids).unsqueeze(0)
      print("Generating caption...")
      generated_ids = model.generate(
          pixel_values=pixel_values, input_ids=input_ids, max_length=50
      )
      print(
          "Generated caption:",
          processor.batch_decode(
              generated_ids, skip_special_tokens=True
          ),
      )
  
      if pytorch_dump_folder_path is not None:
          Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
          print(
              f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"
          )
          model.save_pretrained(pytorch_dump_folder_path)
          processor.save_pretrained(pytorch_dump_folder_path)
  
      if push_to_hub:
          print(
              f"Pushing model and processor of {model_name} to the hub..."
          )
          model.push_to_hub(f"microsoft/{model_name}")
          processor.push_to_hub(f"microsoft/{model_name}")
  
file_path: transformers/src/transformers/models/git/convert_git_to_pytorch.py
imports: import argparse
  from pathlib import Path
  import numpy
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
  from transformers import AutoTokenizer, CLIPImageProcessor, GitConfig, GitForCausalLM, GitProcessor, GitVisionConfig, VideoMAEImageProcessor
  from transformers.utils import logging
label_desc: Use image node_id 7 for calling a global function with example usage: convert_git_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub) without return types