evaluation/math_eval/eval/utils.py

import os
import json
import random
import json
import os
import numpy as np
from pathlib import Path
from typing import Iterable, Union, Any

from examples import get_examples


def set_seed(seed: int = 42) -> None:
    np.random.seed(seed)
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    print(f"Random seed set as {seed}")


def load_jsonl(file: Union[str, Path]) -> Iterable[Any]:
    with open(file, "r", encoding="utf-8") as f:
        for line in f:
            try:
                yield json.loads(line)
            except:
                print("Error in loading:", line)
                exit()


def save_jsonl(samples, save_path):
    # ensure path
    folder = os.path.dirname(save_path)
    os.makedirs(folder, exist_ok=True)

    with open(save_path, "w", encoding="utf-8") as f:
        for sample in samples:
            f.write(json.dumps(sample, ensure_ascii=False) + "\n")
    print("Saved to", save_path)


def lower_keys(example):
    new_example = {}
    for key, value in example.items():
        if key != key.lower():
            new_key = key.lower()
            new_example[new_key] = value
        else:
            new_example[key] = value
    return new_example


EXAMPLES = get_examples()


def load_prompt(data_name, prompt_type, num_shots):
    if not num_shots:
        return []

    if data_name in ["gsm_hard", "svamp", "tabmwp", "asdiv", "mawps"]:
        data_name = "gsm8k"
    if data_name in ["math_oai", "hungarian_exam", "math-oai", "aime24", "amc23"]:
        data_name = "math"
    if data_name in ["sat_math"]:
        data_name = "mmlu_stem"
    if data_name in [
        "gaokao2024_I",
        "gaokao2024_II",
        "gaokao_math_qa",
        "gaokao2024_mix",
        "cn_middle_school",
    ]:
        data_name = "gaokao"

    if prompt_type in ["tool-integrated"]:
        prompt_type = "tora"

    return EXAMPLES[data_name][:num_shots]


PROMPT_TEMPLATES = {
    "direct": ("Question: {input}\nAnswer: ", "{output}", "\n\n"),
    "cot": ("Question: {input}\nAnswer: ", "{output}", "\n\n\n"),
    "pal": ("Question: {input}\n\n", "{output}", "\n---\n"),
    "tool-integrated": ("Question: {input}\n\nSolution:\n", "{output}", "\n---\n"),
    "self-instruct": ("<|user|>\n{input}\n<|assistant|>\n", "{output}", "\n"),
    "tora": ("<|user|>\n{input}\n<|assistant|>\n", "{output}", "\n"),
    "wizard_zs": (
        "### Instruction:\n{input}\n\n### Response: Let's think step by step.",
        "{output}",
        "\n\n\n",
    ),
    "platypus_fs": (
        "### Instruction:\n{input}\n\n### Response:\n",
        "{output}",
        "\n\n\n",
    ),
    "deepseek-math": (
        "User: {input}\nPlease reason step by step, "
        "and put your final answer within \\boxed{{}}.\n\nAssistant:",
        "{output}",
        "\n\n\n",
    ),
    "kpmath": (
        "User: Please reason step by step and put your final answer at the end "
        'with "The answer is: ".\n\n{input}\n\nAssistant:',
        "{output}",
    ),
    "jiuzhang": (
        "## Question\n{input}\n\n## Solution\n",
        "{output}",
        "\n\n\n",
    ),
    "jiuzhang_tora": (
        "## Question\n{input}\n\n## Code Solution\n",
        "{output}",
        "\n\n\n",
    ),
    "jiuzhang_nl": (
        "## Question\n{input}\n\n## Natural Language Solution\n",
        "{output}",
        "\n\n\n",
    ),
    "mmiqc": (
        'Please solve the following problem and put your answer at the end with "The answer is: ".\n\n{input}\n\n',
        "{output}",
        "\n\n\n",
    ),
    "abel": (
        "Question:\n{input}\nAnswer:\nLet's think step by step.\n",
        "{output}",
        "\n\n",
    ),
    "shepherd": ("{input}\n", "{output}", "\n\n\n"),
    "qwen-boxed": (
        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
        "<|im_start|>user\n{input}\nPlease reason step by step, and put your final answer within \\boxed{{}}.<|im_end|>\n"
        "<|im_start|>assistant\n",
        "{output}",
        "\n\n",
    ),
    "qwen25-math-cot": (
        "<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{{}}.<|im_end|>\n"
        "<|im_start|>user\n{input}<|im_end|>\n"
        "<|im_start|>assistant\n",
        "{output}",
        "\n\n",
    ),
    "prime": (
        "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: {input}\n\nPresent the answer in LaTex format: \\boxed{{Your answer}}. Assistant:",
        "{output}",
        "\n\n",
    ),
    # "mathstral": (
    #     "{input}\nPlease reason step by step, and put your final answer within \\boxed{{}}.",
    #     "{output}",
    #     "\n\n",
    # ),
    "orz": (
    # prefix
    "A conversation between User and Assistant. The User asks a question, and the Assistant solves it. "
    "The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. "
    "The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, "
    "i.e., <think> reasoning process here </think> <answer> answer here </answer>. "
    "User: You must put your answer inside <answer> </answer> tags, i.e., <answer> answer here </answer>. "
    "And your final answer will be extracted automatically by the \\boxed{{}} tag.\n"
    "This is the problem:\n{input}\nAssistant: <think>",
    # output (the model’s generated response)
    "{output}",
    "\n\n"
    ),
    "azr": (
        "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: {input}\nAssistant:",
        "{output}",
        "\n\n",
    ),
    "azr_think": (
        "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: {input}\nAssistant: <think>",
        "{output}",
        "\n\n",
    ),
    "azr_boxed": (
        "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. And your final answer will be extracted automatically by the \\boxed{{}} tag. User: {input}\nAssistant:",
        "{output}",
        "\n\n",
    ),
    "mathstral": (
        "Example: Question: What is the area of a square with side length 3? Answer: 9.\n{input}\n Now, solve the problem, like the example above, do not think, do not output any process, but just directly put your final answer within \\boxed{{}}.",
        "{output}",
        "\n\n",
    ),
    "llama3": (
        """<|start_header_id|>user<|end_header_id|>\n{input}\nPlease reason step by step, and put your final answer within \\boxed{{}}.<|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
        "{output}",
        "\n\n",
    ),
    "internlm-math-fs": ("Question:{input}\nAnswer:", "{output}", "\n"),
    "internlm-math-chat": (
        "<|im_start|>user\n{input}<|im_end|>\n" "<|im_start|>assistant\n",
        "{output}",
        "\n\n",
    ),
    "mistral": (
        "[INST] {input}[/INST]",
        "{output}",
        "\n\n",
    ),
    "numina": ("### Problem: {input}\n### Solution:", " {output}", "\n\n"),
    "o1_cot": (
        '[Round 0] USER:\n{input}\nPlease reason step by step, and put your final answer within \\boxed{{}}. ASSISTANT:\n',
        "{output}",
        "\n\n"
    ),
    "deepseek-r1": (
        'A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: {input}. Assistant:',
        "{output}",
        "\n\n"
    ),
}


def construct_prompt(example, data_name, args):
    if args.adapt_few_shot and data_name in [
        "gaokao2024_I",
        "gaokao2024_II",
        "gaokao_math_qa",
        "gaokao2024_mix",
        "cn_middle_school",
    ]:
        demos = load_prompt(data_name, args.prompt_type, 5)
    else:
        demos = load_prompt(data_name, args.prompt_type, args.num_shots)
    prompt_type = args.prompt_type
    if prompt_type == "platypus_fs":
        prompt_type = "cot"
    if prompt_type == "tool-integrated":
        prompt_type = "tora"

    prompt_temp = PROMPT_TEMPLATES[args.prompt_type]

    splitter = prompt_temp[2]
    input_template, output_template, splitter = (
        prompt_temp[0],
        prompt_temp[1],
        prompt_temp[2],
    )
    if args.prompt_type == "qwen25-math-cot":
        # Hotfix to support putting all demos into a single turn
        demo_prompt = splitter.join([q + "\n" + a for q, a in demos])
    else:
        demo_prompt = splitter.join(
            [
                input_template.format(input=q) + output_template.format(output=a)
                for q, a in demos
            ]
        )
    context = input_template.format(input=example["question"])
    if len(demo_prompt) == 0 or (
        args.adapt_few_shot and example["gt_ans"] not in ["A", "B", "C", "D", "E"]
    ):
        full_prompt = context
    else:
        if args.prompt_type == "qwen25-math-cot":
            # Hotfix to supportting put all demos into a single turn
            full_prompt = demo_prompt + splitter + example["question"]
            full_prompt = input_template.format(input=full_prompt)
        else:
            full_prompt = demo_prompt + splitter + context

    if args.prompt_type == "platypus_fs":
        full_prompt_temp = (
            "Below is an instruction that describes a task. "
            "Write a response that appropriately completes the request.\n\n"
            "### Instruction:\n{instruction}\n\n### Response:\n"
        )
        full_prompt = full_prompt_temp.format(instruction=full_prompt)

    if prompt_type == "tora":
        full_prompt = (
            """Integrate step-by-step reasoning and Python code to solve math problems using the following guidelines:

- Analyze the question and write functions to solve the problem; the function should not take any arguments.
- Present the final result in LaTeX using a `\boxed{}` without any units.
- Utilize the `pi` symbol and `Rational`` from Sympy for $\pi$ and fractions, and simplify all fractions and square roots without converting them to decimal values.

Here are some examples you may refer to:

---

"""
            + full_prompt
        )

    return full_prompt.strip(" ")  # important!


key_map = {
    "gt": "Ground Truth",
    "pred": "Prediction",
    "gt_cot": "Reference CoT",
    "score": "Score",
}


def show_sample(sample, print_all_preds=False):
    print("==" * 20)
    for key in ["idx", "type", "level", "dataset"]:
        if key in sample:
            # capitalize
            print("{}: {}".format(key[0].upper() + key[1:], sample[key]))
    print("Question:", repr(sample["question"]))
    if "code" in sample:
        if print_all_preds:
            for code in sample["code"]:
                print("-" * 20)
                print("code:", code)
            print("Execution:", sample["report"])
        else:
            print("Solution:\n", sample["code"][0])
            print("Execution:", sample["report"][0])
    if "pred" in sample:
        print("Prediction:", repr(sample["pred"][0]))
    for key in ["gt", "score", "unit", "gt_cot"]:
        if key in sample:
            _key = key_map.get(key, key)
            print("{}: {}".format(_key, repr(sample[key])))
    print()