186 lines
9.8 KiB
Python
186 lines
9.8 KiB
Python
import argparse
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from concurrent.futures import ThreadPoolExecutor
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from functools import partial
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import json
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import time
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from pathlib import Path
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from tqdm import tqdm
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from sglang.test.test_utils import (
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add_common_other_args_and_parse,
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call_generate_lightllm,
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call_generate_vllm,
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call_generate_srt_raw,
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)
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from sglang.utils import read_jsonl, dump_state_text
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def get_prompt(question):
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prompt = (
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"""Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
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(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
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(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
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(3) Finish[answer], which returns the answer and finishes the task.
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Here are some examples.
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Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
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Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
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Action 1: Search[Colorado orogeny]
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Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.
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Thought 2: It does not mention the eastern sector. So I need to look up eastern sector.
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Action 2: Lookup[eastern sector]
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Observation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny.
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Thought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range.
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Action 3: Search[High Plains]
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Observation 3: High Plains refers to one of two distinct land regions:
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Thought 4: I need to instead search High Plains (United States).
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Action 4: Search[High Plains (United States)]
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Observation 4: The High Plains are a subregion of the Great Plains. From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
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Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
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Action 5: Finish[1,800 to 7,000 ft]
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Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
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Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
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Action 1: Search[Milhouse]
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Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
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Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
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Action 2: Lookup[named after]
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Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
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Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
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Action 3: Finish[Richard Nixon]
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Question: Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture?
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Thought 1: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.
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Action 1: Search[Adam Clayton Powell]
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Observation 1: Could not find [Adam Clayton Powell]. Similar: ['Adam Clayton Powell III', 'Seventh Avenue (Manhattan)', 'Adam Clayton Powell Jr. State Office Building', 'Isabel Washington Powell', 'Adam Powell', 'Adam Clayton Powell (film)', 'Giancarlo Esposito'].
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Thought 2: To find the documentary, I can search Adam Clayton Powell (film).
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Action 2: Search[Adam Clayton Powell (film)]
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Observation 2: Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg.
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The film is about the rise and fall of influential African-American politician Adam Clayton Powell Jr.[3][4] It was later aired as part of the PBS series The American Experience.
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Thought 3: Adam Clayton Powell (film) is a documentary about an African-American politician, not Finnish rock groups. So the documentary about Finnish rock groups must instead be The Saimaa Gesture.
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Action 3: Finish[The Saimaa Gesture]
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Question: What profession does Nicholas Ray and Elia Kazan have in common?
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Thought 1: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.
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Action 1: Search[Nicholas Ray]
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Observation 1: Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 – June 16, 1979) was an American film director, screenwriter, and actor best known for the 1955 film Rebel Without a Cause.
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Thought 2: Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions.
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Action 2: Search[Elia Kazan]
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Observation 2: Elia Kazan was an American film and theatre director, producer, screenwriter and actor.
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Thought 3: Professions of Elia Kazan are director, producer, screenwriter, and actor. So profession Nicholas Ray and Elia Kazan have in common is director, screenwriter, and actor.
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Action 3: Finish[director, screenwriter, actor]
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Question: Which magazine was started first Arthur's Magazine or First for Women?
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Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
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Action 1: Search[Arthur's Magazine]
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Observation 1: Arthur's Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century.
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Thought 2: Arthur's Magazine was started in 1844. I need to search First for Women next.
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Action 2: Search[First for Women]
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Observation 2: First for Women is a woman's magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989.
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Thought 3: First for Women was started in 1989. 1844 (Arthur's Magazine) < 1989 (First for Women), so Arthur's Magazine was started first.
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Action 3: Finish[Arthur's Magazine]
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Question: Were Pavel Urysohn and Leonid Levin known for the same type of work?
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Thought 1: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.
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Action 1: Search[Pavel Urysohn]
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Observation 1: Pavel Samuilovich Urysohn (February 3, 1898 â August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory.
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Thought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work.
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Action 2: Search[Leonid Levin]
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Observation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist.
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Thought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work.
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Action 3: Finish[yes]
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""" + question)
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return prompt
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def main(args):
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lines = read_jsonl(args.data_path)[:args.num_questions]
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arguments = [{
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"question": k,
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"triplets": v
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} for l in lines for k, v in l.items()]
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states = []
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# Select backend
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if args.backend == "lightllm":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_lightllm, url=url)
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elif args.backend == "vllm":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_vllm, url=url)
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elif args.backend == "srt-raw":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_srt_raw, url=url)
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elif args.backend == "guidance":
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from guidance import models, gen
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model = models.LlamaCpp(
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str(Path.home()) + "/model_weights/Llama-2-7b-chat.gguf",
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n_gpu_layers=-1,
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n_ctx=4096,
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)
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def call_generate(prompt, temperature, max_tokens, stop):
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out = (model + prompt + gen(
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name="result",
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max_tokens=max_tokens,
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temperature=temperature,
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stop=stop,
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))
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return out["result"]
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# warmup
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call_generate("Hello,", 1.0, 8, ".")
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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def run_single_agent(argument):
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question = argument["question"]
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triplets = argument["triplets"]
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prompt = get_prompt(question)
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for i in range(1, len(triplets) + 2):
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prompt += "Thought " + str(i) + ":"
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states.append(prompt)
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answer = call_generate(prompt,
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max_tokens=200,
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temperature=0,
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stop="Observation")
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if i > len(triplets):
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break
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prompt += (triplets[i - 1]["thought"] + "\nAction " + str(i) +
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":" + triplets[i - 1]["action"] + "\nObservation " +
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str(i) + ":" + triplets[i - 1]["observation"] + "\n")
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states.append(answer)
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tic = time.time()
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if args.parallel == 1:
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for arg in tqdm(arguments):
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run_single_agent(arg)
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else:
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with ThreadPoolExecutor(args.parallel) as executor:
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executor.map(run_single_agent, arguments)
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latency = time.time() - tic
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print(f"Latency: {latency:.3f}")
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# Write results
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dump_state_text(f"tmp_output_{args.backend}.txt", states)
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with open(args.result_file, "a") as fout:
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value = {
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"task": "ReAct Agents",
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"backend": args.backend,
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"num_gpus": 1,
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"latency": round(latency, 3),
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"num_requests": len(arguments),
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"other": {
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"parallel": args.parallel,
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},
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}
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fout.write(json.dumps(value) + "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--data-path", type=str, default="hotpotqa_100.jsonl")
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parser.add_argument("--num-questions", type=int, default=10)
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args = add_common_other_args_and_parse(parser)
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main(args)
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