Datasets:

BAAI
/

TACO

	# 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