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bowen92 commited on
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Create TACO.py

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  1. TACO.py +145 -0
TACO.py ADDED
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+ # coding=utf-8
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+ # Copyright 2023 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """APPS dataset."""
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+
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+ import json
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+ import datasets
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+
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+
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+ _REPO_NAME = "BAAI/TACO"
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+
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+ _CITATION = """
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+ """
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+
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+ _DESCRIPTION = """
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+ TACO is a benchmark for Python code generation, it includes 25443 problems and 1000 problems for train and test splits.
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+ """
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+
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+ _HOMEPAGE = "https://github.com/FlagOpen/TACO"
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+ _DIFFICULTY = ["EASY", "MEDIUM", "MEDIUM_HARD", "HARD", "VERY_HARD"]
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+ _DIFFICULTY_CONFIGS = ["ALL"] + _DIFFICULTY
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+ _SKILL = ['Data structures', 'Sorting', 'Range queries', 'Complete search', 'Amortized analysis', 'Dynamic programming', 'Bit manipulation', 'Greedy algorithms']
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+ _SKILL_CONFIGS = ["ALL"] + _SKILL
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+ _URLS = {
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+ "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'],
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+ "test": ['test/data-00000-of-00001.arrow'],
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+ }
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+
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+
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+ class TACOConfig(datasets.BuilderConfig):
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+ """BuilderConfig for the TACO dataset."""
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+
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+ def __init__(self, *args, difficulties=["ALL"], skills=["ALL"], **kwargs):
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+ """BuilderConfig for the APPS Code dataset.
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+
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+ Args:
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+ difficulties (:obj:`List[str]`): List of problem difficulty levels to load.
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+ skills (:obj:`List[str]`): List of algorithm skills of problems to load.
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ if "ALL" in difficulties:
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+ assert len(difficulties) == 1
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+ self.filter_difficulties = False
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+ else:
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+ self.filter_difficulties = True
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+ if "ALL" in skills:
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+ assert len(skills) == 1
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+ self.filter_skills = False
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+ else:
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+ self.filter_skills = True
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+
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+ if self.filter_difficulties:
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+ subset_name = '+'.join(sorted(difficulties))
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+ assert not self.filter_skills, "Not supported to filter difficulties and skills together."
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+ elif self.filter_skills:
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+ subset_name = '+'.join(sorted(skills))
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+ else:
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+ subset_name = 'ALL'
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+
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+ super().__init__(
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+ *args,
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+ name=subset_name,
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+ **kwargs,
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+ )
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+
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+ self.subsets = {"difficulties": difficulties, "skills": skills}
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+
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+
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+ class TACO(datasets.GeneratorBasedBuilder):
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+ """TACO dataset."""
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+
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+ VERSION = datasets.Version("1.0.0")
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+
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+ BUILDER_CONFIG_CLASS = TACOConfig
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+ BUILDER_CONFIGS = [
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+ TACOConfig(difficulties=[level]) for level in _DIFFICULTY_CONFIGS
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+ ] + [
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+ TACOConfig(skills=[skill]) for skill in _SKILL_CONFIGS if skill!='ALL'
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+ ]
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+ DEFAULT_CONFIG_NAME = "ALL"
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features({
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+ 'question': datasets.Value(dtype='string', id=None),
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+ 'solutions': datasets.Value(dtype='string', id=None),
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+ 'starter_code': datasets.Value(dtype='string', id=None),
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+ 'input_output': datasets.Value(dtype='string', id=None),
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+ 'difficulty': datasets.Value(dtype='string', id=None),
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+ 'raw_tags': datasets.Value(dtype='string', id=None),
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+ 'name': datasets.Value(dtype='string', id=None),
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+ 'source': datasets.Value(dtype='string', id=None),
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+ 'tags': datasets.Value(dtype='string', id=None),
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+ 'skill_types': datasets.Value(dtype='string', id=None),
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+ 'url': datasets.Value(dtype='string', id=None),
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+ 'Expected Auxiliary Space': datasets.Value(dtype='string', id=None),
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+ 'time_limit': datasets.Value(dtype='string', id=None),
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+ 'date': datasets.Value(dtype='string', id=None),
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+ 'picture_num': datasets.Value(dtype='string', id=None),
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+ 'memory_limit': datasets.Value(dtype='string', id=None),
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+ 'Expected Time Complexity': datasets.Value(dtype='string', id=None),
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+ }),
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+ supervised_keys=None,
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+ citation=_CITATION,
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+ homepage=_HOMEPAGE,
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+ license="MIT License",
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+
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+
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+ downloaded_files = _URLS
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ key = 0
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+ dataset = datasets.concatenate_datasets([datasets.Dataset.from_file(file) for file in filepath])
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+ for idx, data in enumerate(dataset):
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+ difficulty = data['difficulty']
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+ skills = eval(data['skill_types'])
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+ if self.config.filter_difficulties and not difficulty in self.config.subsets['difficulties']:
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+ continue
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+ if self.config.filter_skills:
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+ valid_skills = self.config.subsets['skills']
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+ if not bool(set(valid_skills) & set(skills)):
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+ continue
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+
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+ yield key, {k:v for k, v in data.items() if k!='eval_topic'}
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+ key += 1