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import glob
import math
import os
import subprocess
import sys
from abc import ABC
from pathlib import Path
from typing import Any, Dict, List
import openai
import pytest
from agbenchmark.__main__ import OPTIONAL_CATEGORIES, TEMP_FOLDER_ABS_PATH
from agbenchmark.agent_api_interface import run_api_agent
from agbenchmark.utils.data_types import ChallengeData, Ground
from agbenchmark.utils.prompts import (
END_PROMPT,
FEW_SHOT_EXAMPLES,
PROMPT_MAP,
SCORING_MAP,
)
from agbenchmark.utils.utils import agent_eligibible_for_optional_categories
class Challenge(ABC):
"""The parent class to all specific challenges classes.
Defines helper methods for running a challenge"""
_data_cache: Dict[str, ChallengeData] = {}
CHALLENGE_LOCATION: str = ""
scores: dict[str, Any] = {} # this is for suites
# def __repr__(self) -> str:
# return f"{self.__class__.__name__}(CHALLENGE_LOCATION={self.CHALLENGE_LOCATION}, _data_cache={self._data_cache!r})"
@property
def data(self) -> ChallengeData:
if self.CHALLENGE_LOCATION not in self._data_cache:
self._data_cache[self.CHALLENGE_LOCATION] = ChallengeData.deserialize(
self.CHALLENGE_LOCATION
)
return self._data_cache[self.CHALLENGE_LOCATION]
@property
def task(self) -> str:
return self.data.task
@property
def dependencies(self) -> list:
print(f"got prop dependencies data: {self.data.dependencies}")
return self.data.dependencies
async def setup_challenge(self, config: Dict[str, Any], cutoff: int) -> None:
from agbenchmark.agent_interface import copy_artifacts_into_temp_folder
if not self.task:
return
print(
f"\033[1;35m============Starting {self.data.name} challenge============\033[0m"
)
print(f"\033[1;30mTask: {self.task}\033[0m")
await run_api_agent(self.data, config, self.ARTIFACTS_LOCATION, cutoff)
# hidden files are added after the agent runs. Hidden files can be python test files.
# We copy them in the temporary folder to make it easy to import the code produced by the agent
artifact_paths = [
self.ARTIFACTS_LOCATION,
str(Path(self.CHALLENGE_LOCATION).parent),
]
for path in artifact_paths:
copy_artifacts_into_temp_folder(TEMP_FOLDER_ABS_PATH, "custom_python", path)
def test_method(self, config: Dict[str, Any]) -> None:
raise NotImplementedError
def get_artifacts_out(
self, workspace: str | dict[str, str], ground: Ground
) -> List[str]:
if isinstance(workspace, dict):
workspace = workspace["output"]
script_dir = workspace
files_contents = []
for file_pattern in ground.files:
# Check if it is a file extension
if file_pattern.startswith("."):
# Find all files with the given extension in the workspace
matching_files = glob.glob(os.path.join(script_dir, "*" + file_pattern))
else:
# Otherwise, it is a specific file
matching_files = [os.path.join(script_dir, file_pattern)]
for file_path in matching_files:
if ground.eval.type == "python":
result = subprocess.run(
[sys.executable, file_path],
cwd=os.path.abspath(workspace),
capture_output=True,
text=True,
)
if "error" in result.stderr or result.returncode != 0:
print(result.stderr)
assert False, result.stderr
files_contents.append(f"Output: {result.stdout}\n")
else:
with open(file_path, "r") as f:
files_contents.append(f.read())
else:
if ground.eval.type == "pytest":
result = subprocess.run(
[sys.executable, "-m", "pytest"],
cwd=TEMP_FOLDER_ABS_PATH,
capture_output=True,
text=True,
)
if "error" in result.stderr or result.returncode != 0:
print(result.stderr)
assert False, result.stderr
files_contents.append(f"Output: {result.stdout}\n")
return files_contents
def scoring(self, config: Dict[str, Any], content: str, ground: Ground) -> float:
print("\033[1;34mScoring content:\033[0m", content)
if ground.should_contain:
for should_contain_word in ground.should_contain:
if not getattr(ground, "case_sensitive", True):
should_contain_word = should_contain_word.lower()
content = content.lower()
print_content = (
f"\033[1;34mWord that should exist\033[0m - {should_contain_word}:"
)
if should_contain_word not in content:
print(print_content, "False")
return 0.0
else:
print(print_content, "True")
if ground.should_not_contain:
for should_not_contain_word in ground.should_not_contain:
if not getattr(ground, "case_sensitive", True):
should_not_contain_word = should_not_contain_word.lower()
content = content.lower()
print_content = f"\033[1;34mWord that should not exist\033[0m - {should_not_contain_word}:"
if should_not_contain_word in content:
print(print_content, "False")
return 0.0
else:
print(print_content, "True")
return 1.0
def llm_eval(self, config: Dict[str, Any], content: str, ground: Ground) -> float:
openai.api_key = os.getenv("OPENAI_API_KEY")
if os.getenv("IS_MOCK"):
return 1.0
# the validation for this is done in the Eval BaseModel
scoring = SCORING_MAP[ground.eval.scoring] # type: ignore
prompt = PROMPT_MAP[ground.eval.template].format(task=self.data.task, scoring=scoring, answer=ground.answer, response=content) # type: ignore
if ground.eval.examples:
prompt += FEW_SHOT_EXAMPLES.format(examples=ground.eval.examples)
prompt += END_PROMPT
answer = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": prompt},
],
)
return float(answer["choices"][0]["message"]["content"]) # type: ignore
def get_scores(self, config: Dict[str, Any]) -> dict[str, Any]:
scores = []
scores_dict: Any = {}
percentage = None
answers = {}
try:
if self.data.task == "" and os.getenv("IS_MOCK"):
scores = [1.0]
answers = {"mock": "This is a mock answer"}
elif isinstance(self.data.ground, Ground):
files_contents = self.get_artifacts_out(
TEMP_FOLDER_ABS_PATH, self.data.ground
)
answers = {"answer": files_contents}
for file_content in files_contents:
score = self.scoring(config, file_content, self.data.ground)
print("\033[1;32mYour score is:\033[0m", score)
scores.append(score)
if self.data.ground.eval.type == "llm":
llm_eval = self.llm_eval(
config, "\n".join(files_contents), self.data.ground
)
if self.data.ground.eval.scoring == "percentage":
scores.append(math.ceil(llm_eval / 100))
elif self.data.ground.eval.scoring == "scale":
scores.append(math.ceil(llm_eval / 10))
print("\033[1;32mYour score is:\033[0m", llm_eval)
scores.append(llm_eval)
except Exception as e:
print("Error getting scores", e)
scores_data = {
"values": scores,
"scores_obj": scores_dict,
"percentage": percentage,
"answers": answers,
}
self.scores[self.__class__.__name__] = scores_data
return scores_data
def get_dummy_scores(self, test_name: str, scores: dict[str, Any]) -> int | None:
return 1 # remove this once this works
if 1 in scores.get("scores_obj", {}).get(test_name, []):
return 1
return None
def skip_optional_categories(self, config: Dict[str, Any]) -> None:
challenge_category = self.data.category
categories = [
category
for category in OPTIONAL_CATEGORIES
if category in challenge_category
]
if not agent_eligibible_for_optional_categories(
categories, config.get("category", [])
):
pytest.skip("Agent is not eligible for this category")
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