Minor ruff fix all

node-hub/dora-argotranslate/dora_argotranslate/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-argotranslate/dora_argotranslate/main.py

@@ -15,8 +15,8 @@
 available_packages = argostranslate.package.get_available_packages()
 package_to_install = next(
     filter(
-        lambda x: x.from_code == from_code and x.to_code == to_code, available_packages
-    )
+        lambda x: x.from_code == from_code and x.to_code == to_code, available_packages,
+    ),
 )
 argostranslate.package.install_from_path(package_to_install.download())
 

node-hub/dora-distil-whisper/dora_distil_whisper/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-distil-whisper/dora_distil_whisper/main.py

@@ -78,7 +78,7 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
     if sum(1 for char in text if "\u4e00" <= char <= "\u9fff") / len(text) > 0.5:
         # Chinese text processing
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(text) // 2)
+            min_repeat_length, min(max_repeat_length, len(text) // 2),
         ):
             for i in range(len(text) - repeat_length * 2 + 1):
                 chunk1 = text[i : i + repeat_length]
@@ -90,7 +90,7 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
         # Non-Chinese (space-separated) text processing
         words = text.split()
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(words) // 2)
+            min_repeat_length, min(max_repeat_length, len(words) // 2),
         ):
             for i in range(len(words) - repeat_length * 2 + 1):
                 chunk1 = " ".join(words[i : i + repeat_length])

node-hub/dora-echo/dora_echo/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-echo/dora_echo/main.py

@@ -20,7 +20,7 @@ def main():
     args = parser.parse_args()
 
     node = Node(
-        args.name
+        args.name,
     )  # provide the name to connect to the dataflow if dynamic node
 
     for event in node:

node-hub/dora-internvl/dora_internvl/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-internvl/dora_internvl/main.py

@@ -21,7 +21,7 @@ def build_transform(input_size):
             T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
             T.ToTensor(),
             T.Normalize(mean=MEAN, std=STD),
-        ]
+        ],
     )
     return transform
 
@@ -43,7 +43,7 @@ def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_
 
 
 def dynamic_preprocess(
-    image, min_num=1, max_num=12, image_size=448, use_thumbnail=False
+    image, min_num=1, max_num=12, image_size=448, use_thumbnail=False,
 ):
     orig_width, orig_height = image.size
     aspect_ratio = orig_width / orig_height
@@ -60,7 +60,7 @@ def dynamic_preprocess(
 
     # find the closest aspect ratio to the target
     target_aspect_ratio = find_closest_aspect_ratio(
-        aspect_ratio, target_ratios, orig_width, orig_height, image_size
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size,
     )
 
     # calculate the target width and height
@@ -92,7 +92,7 @@ def load_image(image_array: np.array, input_size=448, max_num=12):
     image = Image.fromarray(image_array).convert("RGB")
     transform = build_transform(input_size=input_size)
     images = dynamic_preprocess(
-        image, image_size=input_size, use_thumbnail=True, max_num=max_num
+        image, image_size=input_size, use_thumbnail=True, max_num=max_num,
     )
     pixel_values = [transform(image) for image in images]
     pixel_values = torch.stack(pixel_values)
@@ -117,7 +117,7 @@ def main():
         .to(device)
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_path, trust_remote_code=True, use_fast=False
+        model_path, trust_remote_code=True, use_fast=False,
     )
 
     node = Node()
@@ -139,10 +139,7 @@ def main():
                 width = metadata["width"]
                 height = metadata["height"]
 
-                if encoding == "bgr8":
-                    channels = 3
-                    storage_type = np.uint8
-                elif encoding == "rgb8":
+                if encoding == "bgr8" or encoding == "rgb8":
                     channels = 3
                     storage_type = np.uint8
                 else:
@@ -169,7 +166,7 @@ def main():
                     )
                     generation_config = dict(max_new_tokens=1024, do_sample=True)
                     response = model.chat(
-                        tokenizer, pixel_values, question, generation_config
+                        tokenizer, pixel_values, question, generation_config,
                     )
                     node.send_output(
                         "text",

node-hub/dora-keyboard/dora_keyboard/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-microphone/dora_microphone/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-microphone/dora_microphone/main.py

@@ -19,7 +19,6 @@ def main():
     always_none = node.next(timeout=0.001) is None
     finished = False
 
-    # noqa
     def callback(indata, frames, time, status):
         nonlocal buffer, node, start_recording_time, finished
 
@@ -36,7 +35,7 @@ def callback(indata, frames, time, status):
 
     # Start recording
     with sd.InputStream(
-        callback=callback, dtype=np.int16, channels=1, samplerate=SAMPLE_RATE
+        callback=callback, dtype=np.int16, channels=1, samplerate=SAMPLE_RATE,
     ):
         while not finished:
-            sd.sleep(int(1000))
+            sd.sleep(1000)

node-hub/dora-openai-server/dora_openai_server/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-openai-server/dora_openai_server/main.py

@@ -56,13 +56,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
         print("Passing input as string")
     if isinstance(data, list):
         data = pa.array(data)  # initialize pyarrow array
-    elif isinstance(data, str):
-        data = pa.array([data])
-    elif isinstance(data, int):
-        data = pa.array([data])
-    elif isinstance(data, float):
-        data = pa.array([data])
-    elif isinstance(data, dict):
+    elif isinstance(data, str) or isinstance(data, int) or isinstance(data, float) or isinstance(data, dict):
         data = pa.array([data])
     else:
         data = pa.array(data)  # initialize pyarrow array
@@ -74,12 +68,10 @@ async def create_chat_completion(request: ChatCompletionRequest):
         if event["type"] == "ERROR":
             response_str = "No response received. Err: " + event["value"][0].as_py()
             break
-        elif event["type"] == "INPUT" and event["id"] == "v1/chat/completions":
+        if event["type"] == "INPUT" and event["id"] == "v1/chat/completions":
             response = event["value"]
             response_str = response[0].as_py() if response else "No response received"
             break
-        else:
-            pass
 
     return ChatCompletionResponse(
         id="chatcmpl-1234",
@@ -91,7 +83,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
                 "index": 0,
                 "message": {"role": "assistant", "content": response_str},
                 "finish_reason": "stop",
-            }
+            },
         ],
         usage={
             "prompt_tokens": len(data),
@@ -111,7 +103,7 @@ async def list_models():
                 "object": "model",
                 "created": 1677610602,
                 "owned_by": "openai",
-            }
+            },
         ],
     }
 

node-hub/dora-opus/dora_opus/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-opus/dora_opus/main.py

@@ -29,7 +29,7 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
     if sum(1 for char in text if "\u4e00" <= char <= "\u9fff") / len(text) > 0.5:
         # Chinese text processing
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(text) // 2)
+            min_repeat_length, min(max_repeat_length, len(text) // 2),
         ):
             for i in range(len(text) - repeat_length * 2 + 1):
                 chunk1 = text[i : i + repeat_length]
@@ -41,7 +41,7 @@ def cut_repetition(text, min_repeat_length=4, max_repeat_length=50):
         # Non-Chinese (space-separated) text processing
         words = text.split()
         for repeat_length in range(
-            min_repeat_length, min(max_repeat_length, len(words) // 2)
+            min_repeat_length, min(max_repeat_length, len(words) // 2),
         ):
             for i in range(len(words) - repeat_length * 2 + 1):
                 chunk1 = " ".join(words[i : i + repeat_length])

node-hub/dora-outtetts/dora_outtetts/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-outtetts/dora_outtetts/main.py

@@ -46,7 +46,6 @@ def create_speaker(interface, path):
     interface.save_speaker(speaker, "speaker.json")
 
     print("saved speaker.json")
-    return
 
 
 def main(arg_list: list[str] | None = None):
@@ -86,7 +85,7 @@ def main(arg_list: list[str] | None = None):
                     f"""Node received:
                 id: {event["id"]},
                 value: {event["value"]},
-                metadata: {event["metadata"]}"""
+                metadata: {event["metadata"]}""",
                 )
 
             elif event["id"] == "text":

node-hub/dora-parler/dora_parler/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-parler/dora_parler/main.py

@@ -31,7 +31,7 @@
         MODEL_NAME_OR_PATH = snapshot_download(MODEL_NAME_OR_PATH)
 
 model = ParlerTTSForConditionalGeneration.from_pretrained(
-    MODEL_NAME_OR_PATH, torch_dtype=torch_dtype, low_cpu_mem_usage=True
+    MODEL_NAME_OR_PATH, torch_dtype=torch_dtype, low_cpu_mem_usage=True,
 ).to(device)
 model.generation_config.cache_implementation = "static"
 model.forward = torch.compile(model.forward, mode="default")
@@ -72,7 +72,7 @@ def __init__(self):
         self.stop_signal = False
 
     def __call__(
-        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs,
     ) -> bool:
         return self.stop_signal
 
@@ -125,7 +125,7 @@ def generate_base(
             if event["id"] == "stop":
                 stopping_criteria.stop()
                 break
-            elif event["id"] == "text":
+            if event["id"] == "text":
                 stopping_criteria.stop()
 
                 text = event["value"][0].as_py()

node-hub/dora-piper/dora_piper/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."

node-hub/dora-piper/dora_piper/main.py

@@ -10,8 +10,7 @@
 
 
 def enable_fun(piper: C_PiperInterface):
-    """
-    使能机械臂并检测使能状态,尝试5s,如果使能超时则退出程序
+    """使能机械臂并检测使能状态,尝试5s,如果使能超时则退出程序
     """
     enable_flag = False
     # 设置超时时间（秒）

node-hub/dora-pyorbbecksdk/dora_pyorbbecksdk/__init__.py

@@ -5,7 +5,7 @@
 
 # Read the content of the README file
 try:
-    with open(readme_path, "r", encoding="utf-8") as f:
+    with open(readme_path, encoding="utf-8") as f:
         __doc__ = f.read()
 except FileNotFoundError:
     __doc__ = "README file not found."