問(wèn)題：Roger有5個(gè)網(wǎng)球。他買了2罐網(wǎng)球。每罐有3個(gè)網(wǎng)球。他現(xiàn)在有多少個(gè)網(wǎng)球？ 
答案：答案是11個(gè)。 
問(wèn)題：自助餐廳有23個(gè)蘋果。如果他們用了20個(gè)來(lái)做午餐，又買了6個(gè)，他們現(xiàn)在有多少個(gè)蘋果？

# 輸出
答案：答案是29個(gè)。

問(wèn)題：Roger有5個(gè)網(wǎng)球。他買了2罐網(wǎng)球。每罐有3個(gè)網(wǎng)球。他現(xiàn)在有多少個(gè)網(wǎng)球？ 
答案：Roger一開(kāi)始有5個(gè)網(wǎng)球。2罐每罐3個(gè)網(wǎng)球，共6個(gè)網(wǎng)球。5 + 6 = 11。答案是11個(gè)。 
問(wèn)題：自助餐廳有23個(gè)蘋果。如果他們用了20個(gè)來(lái)做午餐，又買了6個(gè)，他們現(xiàn)在有多少個(gè)蘋果？

# 輸出
自助餐廳有23個(gè)蘋果。
他們用了20個(gè)來(lái)做午餐。
又買了6個(gè)。
他們現(xiàn)在有多少個(gè)蘋果？
23 - 20 + 6 = 9
答案是9個(gè)。

========== manual_cot
2023/09/26 22:33:22
*****************************
Test Question:
一名服務(wù)員有14個(gè)客人要服務(wù)。如果有3個(gè)客人離開(kāi)了，他又得到了另外39個(gè)客人，那么他現(xiàn)在總共有多少個(gè)客人
*****************************
Prompted Input:
問(wèn)題: 林地里有15棵樹。林地工人今天將在林地里種樹。完成后，林地里將有21棵樹。林地工人今天種了多少棵樹？
答案: 原本有15棵樹。然后種了一些樹后，變成了21棵。所以一定種了21-15=6棵樹。 這個(gè)答案是 6.
問(wèn)題: 如果停車場(chǎng)里有3輛車，又有2輛車到達(dá)，那么停車場(chǎng)里有多少輛車？
答案: 原本有3輛車。又到來(lái)了2輛車。3+2=5輛車。 這個(gè)答案是 5.
Q: 一名服務(wù)員有14個(gè)客人要服務(wù)。如果有3個(gè)客人離開(kāi)了，他又得到了另外39個(gè)客人，那么他現(xiàn)在總共有多少個(gè)客人
A:
*****************************
Output:
原本有14個(gè)客人。又來(lái)了39個(gè)客人。14+39=53個(gè)客人。 這個(gè)答案是 53.
*****************************
========== auto_cot
2023/09/26 22:33:26
*****************************
Test Question:
一名服務(wù)員有14個(gè)客人要服務(wù)。如果有3個(gè)客人離開(kāi)了，他又得到了另外39個(gè)客人，那么他現(xiàn)在總共有多少個(gè)客人
*****************************
Prompted Input:
問(wèn)題: Wendy在Facebook上傳了45張照片。她將27張照片放在一個(gè)相冊(cè)中，將其余的照片放在了9個(gè)不同的相冊(cè)中。每個(gè)相冊(cè)有多少?gòu)堈掌?答案: 讓我們逐步思考。首先，我們知道Wendy總共上傳了45張照片。其次，我們知道Wendy將27張照片放在一個(gè)相冊(cè)中。這意味著Wendy將剩余的18張照片放在了9個(gè)不同的相冊(cè)中。這意味著每個(gè)相冊(cè)都有2張照片。 這個(gè)答案是 2.
問(wèn)題: 在萬(wàn)圣節(jié)，Katie和她的妹妹合并了他們收到的糖果。Katie有8塊糖果，而她的妹妹有23塊糖果。如果他們?cè)诘谝惶焱砩铣粤?塊糖果，那么他們還剩下多少塊糖果？
答案: 讓我們逐步思考。Katie和她的妹妹一共有8+23=31塊糖果。如果他們?cè)诘谝惶焱砩铣粤?塊糖果，那么他們還剩下23塊糖果。 這個(gè)答案是 23.
Q: 一名服務(wù)員有14個(gè)客人要服務(wù)。如果有3個(gè)客人離開(kāi)了，他又得到了另外39個(gè)客人，那么他現(xiàn)在總共有多少個(gè)客人
A: 讓我們一步一步思考.
*****************************
Output:
他最初有14個(gè)客人. 然后3個(gè)客人離開(kāi)了, 所以他只剩下11個(gè)客人. 然后他又得到了39個(gè)客人, 所以他現(xiàn)在總共有50個(gè)客人. 這個(gè)答案是 50.
*****************************

# -*- coding: utf-8 -*-

import argparse
import time
import json
import datetime
import requests

TOKEN = "{你的TOKEN}"

multiarith_auto_json = {
    "demo": [
        {
            "question": "問(wèn)題: Wendy在Facebook上傳了45張照片。她將27張照片放在一個(gè)相冊(cè)中，將其余的照片放在了9個(gè)不同的相冊(cè)中。每個(gè)相冊(cè)有多少?gòu)堈掌?？\n答案:",
            "rationale": "讓我們逐步思考。首先，我們知道Wendy總共上傳了45張照片。其次，我們知道Wendy將27張照片放在一個(gè)相冊(cè)中。這意味著Wendy將剩余的18張照片放在了9個(gè)不同的相冊(cè)中。這意味著每個(gè)相冊(cè)都有2張照片。",
            "pred_ans": "2",
            "gold_ans": "2"
        },
        {
            "question": "問(wèn)題: 在萬(wàn)圣節(jié)，Katie和她的妹妹合并了他們收到的糖果。Katie有8塊糖果，而她的妹妹有23塊糖果。如果他們?cè)诘谝惶焱砩铣粤?塊糖果，那么他們還剩下多少塊糖果？\n答案:",
            "rationale": "讓我們逐步思考。Katie和她的妹妹一共有8+23=31塊糖果。如果他們?cè)诘谝惶焱砩铣粤?塊糖果，那么他們還剩下23塊糖果。",
            "pred_ans": "23",
            "gold_ans": "23"
        }
    ]
}

multiarith_manual_json = {
    "demo": [
        {
            "question": "問(wèn)題: 林地里有15棵樹。林地工人今天將在林地里種樹。完成后，林地里將有21棵樹。林地工人今天種了多少棵樹？\n答案:",
            "rationale": "原本有15棵樹。然后種了一些樹后，變成了21棵。所以一定種了21-15=6棵樹。",
            "pred_ans": "6"
        },
        {
            "question": "問(wèn)題: 如果停車場(chǎng)里有3輛車，又有2輛車到達(dá)，那么停車場(chǎng)里有多少輛車？\n答案:",
            "rationale": "原本有3輛車。又到來(lái)了2輛車。3+2=5輛車。",
            "pred_ans": "5"
        }
    ]
}


def request_api(prompt, engine, max_tokens, temperature, stop, top_p, frequency_penalty, presence_penalty):
    url = "https://{你的代理地址}/v1/completions"
    headers = {
        "Content-Type": "application/json",
        "QPilot-ID": "246",
        "Authorization": "Bearer " + TOKEN,
    }
    params = {
        "model": engine,
        "prompt": prompt,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "stop": stop,
        "top_p": top_p,
        "frequency_penalty": frequency_penalty,
        "presence_penalty": presence_penalty,
    }
    result = requests.post(url, data=json.dumps(params),
                           headers=headers)
    # print("result: ", str(result.text))
    return result.json()


def decoder_for_gpt3(args, input, max_length):
    # GPT-3 API allows each users execute the API within 60 times in a minute ...
    # time.sleep(1)
    time.sleep(args.api_time_interval)
    # https://beta.openai.com/account/api-keys
    # openai.api_key = "[Your OpenAI API Key]"

    # Specify engine ...
    # Instruct GPT3
    if args.model == "gpt3":
        engine = "text-ada-001"
    elif args.model == "gpt3-medium":
        engine = "text-babbage-001"
    elif args.model == "gpt3-large":
        engine = "text-curie-001"
    elif args.model == "gpt3-xl":
        engine = "text-davinci-002"
    elif args.model == "text-davinci-001":
        engine = "text-davinci-001"
    elif args.model == "code-davinci-002":
        engine = "code-davinci-002"
    else:
        raise ValueError("model is not properly defined ...")

    if ("few_shot" in args.method or "auto" in args.method) and engine == "code-davinci-002":
        response = request_api(
            engine=engine,
            prompt=input,
            max_tokens=max_length,
            temperature=args.temperature,
            top_p=1,
            frequency_penalty=0,
            presence_penalty=0,
            stop=["\n"]
        )
    else:
        response = request_api(
            engine=engine,
            prompt=input,
            max_tokens=max_length,
            temperature=args.temperature,
            top_p=1,
            frequency_penalty=0,
            presence_penalty=0,
            stop=None
        )
    return response["choices"][0]["text"]


def create_demo_text(args, cot_flag):
    x, z, y = [], [], []
    json_data = args.demo_content["demo"]
    for line in json_data:
        x.append(line["question"])
        z.append(line["rationale"])
        y.append(line["pred_ans"])

    index_list = list(range(len(x)))

    demo_text = ""
    for i in index_list:
        if cot_flag:
            demo_text += x[i] + " " + z[i] + " " + \
                args.direct_answer_trigger_for_fewshot + " " + y[i] + ".\n\n"
        else:
            demo_text += x[i] + " " + \
                args.direct_answer_trigger_for_fewshot + " " + y[i] + ".\n\n"
    return demo_text


class Decoder():
    def __init__(self):
        pass

    def decode(self, args, input, max_length):
        response = decoder_for_gpt3(args, input, max_length)
        return response


def cot(method, question):
    args = parse_arguments()
    decoder = Decoder()

    args.method = method
    if args.method != "zero_shot_cot":
        if args.method == "auto_cot":
            args.demo_content = multiarith_auto_json
        else:
            args.demo_content = multiarith_manual_json
        demo = create_demo_text(args, cot_flag=True)
    else:
        demo = None

    x = "Q: " + question + "\n" + "A:"
    print('*****************************')
    print("Test Question:")
    print(question)
    print('*****************************')

    if args.method == "zero_shot":
        x = x + " " + args.direct_answer_trigger_for_zeroshot
    elif args.method == "zero_shot_cot":
        x = x + " " + args.cot_trigger
    elif args.method == "manual_cot":
        x = demo + x
    elif args.method == "auto_cot":
        x = demo + x + " " + args.cot_trigger
    else:
        raise ValueError("method is not properly defined ...")

    print("Prompted Input:")
    print(x.replace("\n\n", "\n").strip())
    print('*****************************')

    max_length = args.max_length_cot if "cot" in args.method else args.max_length_direct
    z = decoder.decode(args, x, max_length)
    z = z.replace("\n\n", "\n").replace("\n", "").strip()
    if args.method == "zero_shot_cot":
        z2 = x + z + " " + args.direct_answer_trigger_for_zeroshot_cot
        max_length = args.max_length_direct
        pred = decoder.decode(args, z2, max_length)
        print("Output:")
        print(z + " " + args.direct_answer_trigger_for_zeroshot_cot + " " + pred)
        print('*****************************')
    else:
        pred = z
        print("Output:")
        print(pred)
        print('*****************************')


def parse_arguments():
    parser = argparse.ArgumentParser(descriptinotallow="Zero-shot-CoT")

    parser.add_argument("--max_num_worker", type=int, default=0,
                        help="maximum number of workers for dataloader")
    parser.add_argument(
        "--model", type=str, default="gpt3-xl", help="model used for decoding. Note that 'gpt3' are the smallest models."
    )
    parser.add_argument(
        "--method", type=str, default="auto_cot", choices=["zero_shot", "zero_shot_cot", "few_shot", "few_shot_cot", "auto_cot"], help="method"
    )
    parser.add_argument(
        "--cot_trigger_no", type=int, default=1, help="A trigger sentence that elicits a model to execute chain of thought"
    )
    parser.add_argument(
        "--max_length_cot", type=int, default=256, help="maximum length of output tokens by model for reasoning extraction"
    )
    parser.add_argument(
        "--max_length_direct", type=int, default=32, help="maximum length of output tokens by model for answer extraction"
    )
    parser.add_argument(
        "--api_time_interval", type=float, default=1.0, help=""
    )
    parser.add_argument(
        "--temperature", type=float, default=0, help=""
    )
    args = parser.parse_args()
    args.direct_answer_trigger_for_fewshot = "這個(gè)答案是"
    args.direct_answer_trigger_for_zeroshot = "這個(gè)答案是"
    args.direct_answer_trigger_for_zeroshot_cot = "這個(gè)答案是"
    args.cot_trigger = "讓我們一步一步思考."
    return args


if __name__ == "__main__":
    ans = "一名服務(wù)員有14個(gè)客人要服務(wù)。如果有3個(gè)客人離開(kāi)了，他又得到了另外39個(gè)客人，那么他現(xiàn)在總共有多少個(gè)客人"
    print("========== manual_cot")
    cot("manual_cot", ans)
    print("========== auto_cot")
    cot("auto_cot", ans)

問(wèn)題 科羅拉多造山帶東部區(qū)域延伸到的區(qū)域的海拔范圍是多少?
思考 1 我需要搜索科羅拉多造山帶，找到科羅拉多造山帶東段延伸的區(qū)域，然后找到該區(qū)域的海拔范圍。
操作 1 搜索 [科羅拉多造山帶]
觀察 1 科羅拉多造山帶是科羅拉多及其周邊地區(qū)造山運(yùn)動(dòng)的一段。
思考 2 它沒(méi)有提到東部地區(qū)。所以我需要查一下東部地區(qū)。
操作 2 查找 [東部地區(qū)]
觀察 2 (結(jié)果 1 / 1) 東部區(qū)域延伸至高平原，稱為中原造山帶。
思考 3 科羅拉多造山運(yùn)動(dòng)的東部區(qū)域延伸到高平原。所以我需要搜索高平原，找到它的海拔范圍。
操作 3 搜索 [高平原]
觀察 3 高平原指的是兩個(gè)截然不同的陸地區(qū)域之一。
思考 4 我需要搜索高平原（美國(guó)）
操作 4 搜索 [高平原（美國(guó)）]
觀察 4 高平原是大平原的一個(gè)分區(qū)。從東到西，高平原的海拔從 1800 到 7000 英尺 (550 到 2130 米) 不等。[3]
思考 5 高平原海拔高度從 1800 到 7000 英尺，所以答案是 1800 到 7000 英尺。
操作 5 結(jié)束 [1800 到 7000 英尺]
...

# 更新或安裝必要的庫(kù)
# pip install --upgrade openai
# pip install --upgrade langchain
# pip install google-search-results

import os
from langchain.llms import OpenAI
from langchain.agents import load_tools
from langchain.agents import initialize_agent

os.environ["OPENAI_API_KEY"] = "{你的TOKEN}"
os.environ["OPENAI_API_BASE"] = "{你的代理地址}"
# https://serpapi.com/manage-api-key
os.environ["SERPAPI_API_KEY"] = "{serpapi.com注冊(cè)可以獲得key}"
llm = OpenAI(model_name="text-davinci-003", temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(
    tools, llm, agent="zero-shot-react-description", verbose=True)
agent.run("珠穆拉瑪峰旁邊的西北方向的山峰叫什么名字？有誰(shuí)成功登頂這座山峰？請(qǐng)用中文回答")

> Entering new AgentExecutor chain...
 I need to find out who successfully climbed the mountain and then calculate their age cubed
Action: Search
Action Input: "who successfully climbed the mountain near Mount Everest northwest"
Observation: Edmund Hillary (left) and Sherpa Tenzing Norgay reached the 29,035-foot summit of Everest on May 29, 1953, becoming the first people to stand atop the world's highest mountain.
Thought: I need to find out Edmund Hillary's age
Action: Search
Action Input: "Edmund Hillary age"
Observation: 88 years
Thought: I need to calculate 88 cubed
Action: Calculator
Action Input: 88^3
Observation: Answer: 681472

Thought: I now know the final answer
Final Answer: 意蒙德·希拉里（Edmund Hillary）成功登頂過(guò)山峰珠穆拉瑪峰旁邊的西北方向的山峰，他的年齡的3次方是681472。

> Finished chain

EXAMPLES = [
    """Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought: 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.
Action: Search[Colorado orogeny]
Observation: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.
Thought: It does not mention the eastern sector. So I need to look up eastern sector.
Action: Lookup[eastern sector]
Observation: (Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny.
Thought: The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range.
Action: Search[High Plains]
Observation: High Plains refers to one of two distinct land regions
Thought: I need to instead search High Plains (United States).
Action: Search[High Plains (United States)]
Observation: 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]
Thought: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action: Finish[1,800 to 7,000 ft]""",
    """Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought: 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.
Action: Search[Milhouse]
Observation: 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.
Thought: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action: Lookup[named after]
Observation: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action: Finish[Richard Nixon]""",
    """Question: Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture?
Thought: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.
Action: Search[Adam Clayton Powell]
Observation: 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’].
Thought: To find the documentary, I can search Adam Clayton Powell (film).
Action: Search[Adam Clayton Powell (film)]
Observation: Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg. 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.
Thought: 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.
Action: Finish[The Saimaa Gesture]""",
    """Question: What profession does Nicholas Ray and Elia Kazan have in common?
Thought: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.
Action: Search[Nicholas Ray]
Observation: 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.
Thought: Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions.
Action: Search[Elia Kazan]
Observation: Elia Kazan was an American film and theatre director, producer, screenwriter and actor.
Thought: 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.
Action: Finish[director, screenwriter, actor]""",
    """Question: Which magazine was started first Arthur’s Magazine or First for Women?
Thought: I need to search Arthur’s Magazine and First for Women, and find which was started first.
Action: Search[Arthur’s Magazine]
Observation: Arthur’s Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century.
Thought: Arthur’s Magazine was started in 1844. I need to search First for Women next.
Action: Search[First for Women]
Observation: First for Women is a woman’s magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989.
Thought: First for Women was started in 1989. 1844 (Arthur’s Magazine) < 1989 (First for Women), so Arthur’s Magazine was started first.
Action: Finish[Arthur’s Magazine]""",
    """Question: Were Pavel Urysohn and Leonid Levin known for the same type of work?
Thought: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.
Action: Search[Pavel Urysohn]
Observation: Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory.
Thought: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work.
Action: Search[Leonid Levin]
Observation: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist.
Thought: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work.
Action: Finish[yes]""",
]
SUFFIX = """\nQuestion: {input}
{agent_scratchpad}"""

WIKI_PROMPT = PromptTemplate.from_examples(
    EXAMPLES, SUFFIX, ["input", "agent_scratchpad"]
)