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TACO / TACO.py
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# coding=utf-8
# Copyright 2023 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TACO dataset."""
import json
import datasets
_REPO_NAME = "BAAI/TACO"
_CITATION = """
"""
_DESCRIPTION = """
TACO is a benchmark for Python code generation, it includes 25443 problems and 1000 problems for train and test splits.
"""
_HOMEPAGE = "https://github.com/FlagOpen/TACO"
_DIFFICULTY = ["EASY", "MEDIUM", "MEDIUM_HARD", "HARD", "VERY_HARD"]
_DIFFICULTY_CONFIGS = ["ALL"] + _DIFFICULTY
_SKILL = ['Data structures', 'Sorting', 'Range queries', 'Complete search', 'Amortized analysis', 'Dynamic programming', 'Bit manipulation', 'Greedy algorithms']
_SKILL_CONFIGS = ["ALL"] + _SKILL
_URLS = {
"train": ['train/data-00000-of-00009.arrow', 'train/data-00001-of-00009.arrow', 'train/data-00002-of-00009.arrow', 'train/data-00003-of-00009.arrow', 'train/data-00004-of-00009.arrow', 'train/data-00005-of-00009.arrow', 'train/data-00006-of-00009.arrow', 'train/data-00007-of-00009.arrow', 'train/data-00008-of-00009.arrow'],
"test": ['test/data-00000-of-00001.arrow'],
}
class TACOConfig(datasets.BuilderConfig):
"""BuilderConfig for the TACO dataset."""
def __init__(self, *args, difficulties=["ALL"], skills=["ALL"], **kwargs):
"""BuilderConfig for the APPS Code dataset.
Args:
difficulties (:obj:`List[str]`): List of problem difficulty levels to load.
skills (:obj:`List[str]`): List of algorithm skills of problems to load.
**kwargs: keyword arguments forwarded to super.
"""
if "ALL" in difficulties:
assert len(difficulties) == 1
self.filter_difficulties = False
else:
self.filter_difficulties = True
if "ALL" in skills:
assert len(skills) == 1
self.filter_skills = False
else:
self.filter_skills = True
if self.filter_difficulties:
subset_name = '+'.join(sorted(difficulties))
assert not self.filter_skills, "Not supported to filter difficulties and skills together."
elif self.filter_skills:
subset_name = '+'.join(sorted(skills))
else:
subset_name = 'ALL'
super().__init__(
*args,
name=subset_name,
**kwargs,
)
self.subsets = {"difficulties": difficulties, "skills": skills}
class TACO(datasets.GeneratorBasedBuilder):
"""TACO dataset."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = TACOConfig
BUILDER_CONFIGS = [
TACOConfig(difficulties=[level]) for level in _DIFFICULTY_CONFIGS
] + [
TACOConfig(skills=[skill]) for skill in _SKILL_CONFIGS if skill!='ALL'
]
DEFAULT_CONFIG_NAME = "ALL"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
'question': datasets.Value(dtype='string', id=None),
'solutions': datasets.Value(dtype='string', id=None),
'starter_code': datasets.Value(dtype='string', id=None),
'input_output': datasets.Value(dtype='string', id=None),
'difficulty': datasets.Value(dtype='string', id=None),
'raw_tags': datasets.Value(dtype='string', id=None),
'name': datasets.Value(dtype='string', id=None),
'source': datasets.Value(dtype='string', id=None),
'tags': datasets.Value(dtype='string', id=None),
'skill_types': datasets.Value(dtype='string', id=None),
'url': datasets.Value(dtype='string', id=None),
'Expected Auxiliary Space': datasets.Value(dtype='string', id=None),
'time_limit': datasets.Value(dtype='string', id=None),
'date': datasets.Value(dtype='string', id=None),
'picture_num': datasets.Value(dtype='string', id=None),
'memory_limit': datasets.Value(dtype='string', id=None),
'Expected Time Complexity': datasets.Value(dtype='string', id=None),
}),
supervised_keys=None,
citation=_CITATION,
homepage=_HOMEPAGE,
license="MIT License",
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
key = 0
dataset = datasets.concatenate_datasets([datasets.Dataset.from_file(file) for file in filepath])
for idx, data in enumerate(dataset):
difficulty = data['difficulty']
skills = eval(data['skill_types'])
if self.config.filter_difficulties and not difficulty in self.config.subsets['difficulties']:
continue
if self.config.filter_skills:
valid_skills = self.config.subsets['skills']
if not bool(set(valid_skills) & set(skills)):
continue
yield key, data
key += 1