def convert_to_jsonl()

in dags/inference/utils/trtllm_bench_jsonl_converter.py [0:0]

• 18 lines of code
• 5 McCabe index (conditional complexity)

def convert_to_jsonl(input_path, jsonl_path):
  input_data = read_input_file(input_path)
  extracted_data = extract_data(patterns, input_data)
  data = dict()
  data["dimensions"] = dict()
  data["metrics"] = dict()
  for sections in extracted_data.items():
    for key in sections[1]:
      try:
        float(sections[1][key])
        data["metrics"][key] = float(sections[1][key])
      except:
        data["dimensions"][key] = str(sections[1][key])
  if len(data["dimensions"]) == 0 or len(data["metrics"]) == 0:
    print(f"{input_path} contains incomplete results.")
  else:
    with jsonlines.open(jsonl_path, "a") as writter:
      writter.write(data)