prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [CI] Test examples in CI
  ### Anything you want to discuss about vllm.
  
  Current scripts in `examples/` directory are not tested in CI. We should run them to ensure passing 
  
  </issue>
  <code>
  [start of examples/llava_example.py]
  1 import argparse
  2 import os
  3 import subprocess
  4 
  5 import torch
  6 
  7 from vllm import LLM
  8 from vllm.sequence import MultiModalData
  9 
  10 # The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
  11 
  12 
  13 def run_llava_pixel_values():
  14     llm = LLM(
  15         model="llava-hf/llava-1.5-7b-hf",
  16         image_input_type="pixel_values",
  17         image_token_id=32000,
  18         image_input_shape="1,3,336,336",
  19         image_feature_size=576,
  20     )
  21 
  22     prompt = "<image>" * 576 + (
  23         "\nUSER: What is the content of this image?\nASSISTANT:")
  24 
  25     # This should be provided by another online or offline component.
  26     images = torch.load("images/stop_sign_pixel_values.pt")
  27 
  28     outputs = llm.generate(prompt,
  29                            multi_modal_data=MultiModalData(
  30                                type=MultiModalData.Type.IMAGE, data=images))
  31     for o in outputs:
  32         generated_text = o.outputs[0].text
  33         print(generated_text)
  34 
  35 
  36 def run_llava_image_features():
  37     llm = LLM(
  38         model="llava-hf/llava-1.5-7b-hf",
  39         image_input_type="image_features",
  40         image_token_id=32000,
  41         image_input_shape="1,576,1024",
  42         image_feature_size=576,
  43     )
  44 
  45     prompt = "<image>" * 576 + (
  46         "\nUSER: What is the content of this image?\nASSISTANT:")
  47 
  48     # This should be provided by another online or offline component.
  49     images = torch.load("images/stop_sign_image_features.pt")
  50 
  51     outputs = llm.generate(prompt,
  52                            multi_modal_data=MultiModalData(
  53                                type=MultiModalData.Type.IMAGE, data=images))
  54     for o in outputs:
  55         generated_text = o.outputs[0].text
  56         print(generated_text)
  57 
  58 
  59 def main(args):
  60     if args.type == "pixel_values":
  61         run_llava_pixel_values()
  62     else:
  63         run_llava_image_features()
  64 
  65 
  66 if __name__ == "__main__":
  67     parser = argparse.ArgumentParser(description="Demo on Llava")
  68     parser.add_argument("--type",
  69                         type=str,
  70                         choices=["pixel_values", "image_features"],
  71                         default="pixel_values",
  72                         help="image input type")
  73     args = parser.parse_args()
  74     # Download from s3
  75     s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
  76     local_directory = "images"
  77 
  78     # Make sure the local directory exists or create it
  79     os.makedirs(local_directory, exist_ok=True)
  80 
  81     # Use AWS CLI to sync the directory
  82     subprocess.check_call(
  83         ["aws", "s3", "sync", s3_bucket_path, local_directory])
  84     main(args)
  85
  [end of examples/llava_example.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/examples/llava_example.py b/examples/llava_example.py
  --- a/examples/llava_example.py
  +++ b/examples/llava_example.py
  @@ -78,7 +78,13 @@
       # Make sure the local directory exists or create it
       os.makedirs(local_directory, exist_ok=True)
   
  -    # Use AWS CLI to sync the directory
  -    subprocess.check_call(
  -        ["aws", "s3", "sync", s3_bucket_path, local_directory])
  +    # Use AWS CLI to sync the directory, assume anonymous access
  +    subprocess.check_call([
  +        "aws",
  +        "s3",
  +        "sync",
  +        s3_bucket_path,
  +        local_directory,
  +        "--no-sign-request",
  +    ])
       main(args)
  
verification_info: {"golden_diff": "diff --git a/examples/llava_example.py b/examples/llava_example.py\n--- a/examples/llava_example.py\n+++ b/examples/llava_example.py\n@@ -78,7 +78,13 @@\n     # Make sure the local directory exists or create it\n     os.makedirs(local_directory, exist_ok=True)\n \n-    # Use AWS CLI to sync the directory\n-    subprocess.check_call(\n-        [\"aws\", \"s3\", \"sync\", s3_bucket_path, local_directory])\n+    # Use AWS CLI to sync the directory, assume anonymous access\n+    subprocess.check_call([\n+        \"aws\",\n+        \"s3\",\n+        \"sync\",\n+        s3_bucket_path,\n+        local_directory,\n+        \"--no-sign-request\",\n+    ])\n     main(args)\n", "issue": "[CI] Test examples in CI\n### Anything you want to discuss about vllm.\n\nCurrent scripts in `examples/` directory are not tested in CI. We should run them to ensure passing \n", "before_files": [{"content": "import argparse\nimport os\nimport subprocess\n\nimport torch\n\nfrom vllm import LLM\nfrom vllm.sequence import MultiModalData\n\n# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.\n\n\ndef run_llava_pixel_values():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"pixel_values\",\n        image_token_id=32000,\n        image_input_shape=\"1,3,336,336\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_pixel_values.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef run_llava_image_features():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"image_features\",\n        image_token_id=32000,\n        image_input_shape=\"1,576,1024\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_image_features.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef main(args):\n    if args.type == \"pixel_values\":\n        run_llava_pixel_values()\n    else:\n        run_llava_image_features()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Demo on Llava\")\n    parser.add_argument(\"--type\",\n                        type=str,\n                        choices=[\"pixel_values\", \"image_features\"],\n                        default=\"pixel_values\",\n                        help=\"image input type\")\n    args = parser.parse_args()\n    # Download from s3\n    s3_bucket_path = \"s3://air-example-data-2/vllm_opensource_llava/\"\n    local_directory = \"images\"\n\n    # Make sure the local directory exists or create it\n    os.makedirs(local_directory, exist_ok=True)\n\n    # Use AWS CLI to sync the directory\n    subprocess.check_call(\n        [\"aws\", \"s3\", \"sync\", s3_bucket_path, local_directory])\n    main(args)\n", "path": "examples/llava_example.py"}]}