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* Initial commit of AutoGenBench * wording * typo * pre-commit reformulation * Updated README to point to contributor's guide earlier. * Simplified the description of the JSON format. * Added print statements to indicate when run.sh and scenario.py are starting. * Added SocietyOfMind scenario to GAIA. * Pointing autogenbench clone command to the latest branch. * Temporarily disable subsample option. * Updated the GAIA readme to specify how to define a BING API key. * Fixed and re-enabled the subsample option. * Added a draft of a blog post. * Updated authors. * Incorporating Gagan's feedback. * Fixed code formatting. * Updated the help string in the docs. * Light editing of the AutoGenBench blogpost. * Support filtering on model tags. * Added websurfer dependencies to Dockerfile. * Renamed testbed -> autogenbench * Attempting to fix formatting. * Added more gracefull handling of task timeouts (the script is allowed to terminate before Docker is stopped). * Updated the blogpost based on Saleema's and Julia's feedback. * Fixed formatting... again. * Added a main MANIFEST to list available scenarios. * Limit main manifest to directories. * Manifests now use relative paths. * All manifests are now relative. * Updated the contributing guide, and address windows path issues. * Updated the version. Fixed formatting. * Fixed formatting. * De-listing Examples, since it has no clear tabulate criteria. * Updated email in pyproject * typo in blogpost * wording --------- Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: Qingyun Wu <qingyun0327@gmail.com>
122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
#
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# Run this file to download the human_eval dataset, and create a corresponding testbed scenario:
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# (default: ../scenarios/human_eval_two_agents_gpt4.jsonl and ./scenarios/human_eval_two_agents_gpt35.jsonl)
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#
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import requests
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import gzip
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import io
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import json
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import os
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import base64
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URL = "https://github.com/openai/human-eval/raw/master/data/HumanEval.jsonl.gz"
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SCRIPT_PATH = os.path.realpath(__file__)
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SCRIPT_NAME = os.path.basename(SCRIPT_PATH)
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SCRIPT_DIR = os.path.dirname(SCRIPT_PATH)
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# A selected subset of HumanEval problems to work with during development
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REDUCED_SET = [
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"HumanEval/2",
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"HumanEval/26",
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"HumanEval/32",
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"HumanEval/33",
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"HumanEval/36",
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"HumanEval/38",
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"HumanEval/41",
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"HumanEval/50",
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"HumanEval/56",
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"HumanEval/65",
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"HumanEval/67",
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"HumanEval/84",
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"HumanEval/85",
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"HumanEval/86",
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"HumanEval/89",
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"HumanEval/99",
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"HumanEval/104",
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"HumanEval/113",
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"HumanEval/115",
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"HumanEval/120",
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"HumanEval/124",
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"HumanEval/126",
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"HumanEval/132",
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"HumanEval/135",
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"HumanEval/140",
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"HumanEval/146",
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]
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def download_human_eval():
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"""Download the HumanEval dataset, un-gzips it, and returns a list of its parsed JSON objects."""
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# Send a HTTP request to the URL of the file
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response = requests.get(URL)
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# Ensure we raise an error if the download failed
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response.raise_for_status()
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# Create a BytesIO object from the response content
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buffer = io.BytesIO(response.content)
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# Read the file, line by line, populating a list of parsed JSON objects
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results = []
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with gzip.GzipFile(fileobj=buffer) as f_in:
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for line in f_in:
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# Parse each line as JSON
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results.append(json.loads(line))
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return results
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def create_jsonl(name, tasks, template):
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"""Creates a JSONL scenario file with a given name, list of HumanEval tasks, and template path."""
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# Create a task directory if it doesn't exist
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scenario_dir = os.path.realpath(os.path.join(SCRIPT_DIR, os.path.pardir))
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task_dir = os.path.join(scenario_dir, "Tasks")
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if not os.path.isdir(task_dir):
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os.mkdir(task_dir)
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# Create the jsonl file
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with open(os.path.join(task_dir, name + ".jsonl"), "wt") as fh:
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for task in tasks:
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print(f"Converting: [{name}] {task['task_id']}")
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record = {
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"id": task["task_id"].replace("/", "_"),
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"template": os.path.join(os.path.pardir, template),
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"substitutions": {
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"scenario.py": {
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"__ENTRY_POINT__": task["entry_point"],
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"__SELECTION_METHOD__": "auto",
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},
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"prompt.txt": {"__PROMPT__": task["prompt"]},
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"coding/my_tests.py": {"__TEST__": task["test"]},
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},
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}
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fh.write(json.dumps(record).strip() + "\n")
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###############################################################################
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def main():
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human_eval = download_human_eval()
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reduced_human_eval = [t for t in human_eval if t["task_id"] in REDUCED_SET]
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templates = {
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"two_agents": "Templates/TwoAgents",
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# "gc3_distractor": "Templates/GroupChatThreeAgents_Distractor",
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# "gc3_guardrails": "Templates/GroupChatThreeAgents_Guardrails",
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# "gc4": "Templates/GroupChatFourAgents",
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}
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# Create the various combinations of [models] x [templates]
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for t in templates.items():
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create_jsonl(f"human_eval_{t[0]}", human_eval, t[1])
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create_jsonl(f"r_human_eval_{t[0]}", reduced_human_eval, t[1])
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if __name__ == "__main__" and __package__ is None:
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main()
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