misc: add pre-commit config (#637)

examples/usage/triton/models/character_generation/1/model.py

@@ -1,45 +1,55 @@
-import triton_python_backend_utils as pb_utils
 import numpy
+import triton_python_backend_utils as pb_utils
+from pydantic import BaseModel
+
 import sglang as sgl
 from sglang import function, set_default_backend
 from sglang.srt.constrained import build_regex_from_object
 
-from pydantic import BaseModel
-
 sgl.set_default_backend(sgl.RuntimeEndpoint("http://localhost:30000"))
 
+
 class Character(BaseModel):
     name: str
     eye_color: str
     house: str
 
+
 @function
 def character_gen(s, name):
     s += (
         name
         + " is a character in Harry Potter. Please fill in the following information about this character.\n"
     )
-    s += sgl.gen("json_output", max_tokens=256, regex=build_regex_from_object(Character))
+    s += sgl.gen(
+        "json_output", max_tokens=256, regex=build_regex_from_object(Character)
+    )
 
 
 class TritonPythonModel:
     def initialize(self, args):
         print("Initialized.")
+
     def execute(self, requests):
         responses = []
         for request in requests:
             tensor_in = pb_utils.get_input_tensor_by_name(request, "INPUT_TEXT")
             if tensor_in is None:
                 return pb_utils.InferenceResponse(output_tensors=[])
-            
-            input_list_names = [i.decode('utf-8') if isinstance(i, bytes) else i for i in tensor_in.as_numpy().tolist()]
 
-            input_list_dicts = [{"name":i} for i in input_list_names]
+            input_list_names = [
+                i.decode("utf-8") if isinstance(i, bytes) else i
+                for i in tensor_in.as_numpy().tolist()
+            ]
+
+            input_list_dicts = [{"name": i} for i in input_list_names]
 
             states = character_gen.run_batch(input_list_dicts)
             character_strs = [state.text() for state in states]
 
-            tensor_out = pb_utils.Tensor("OUTPUT_TEXT", numpy.array(character_strs, dtype=object))
+            tensor_out = pb_utils.Tensor(
+                "OUTPUT_TEXT", numpy.array(character_strs, dtype=object)
+            )
 
-            responses.append(pb_utils.InferenceResponse(output_tensors = [tensor_out]))
-        return responses
+            responses.append(pb_utils.InferenceResponse(output_tensors=[tensor_out]))
+        return responses