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import json |
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import datasets |
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_DESCRIPTION = """\ |
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The MBPP (Mostly Basic Python Problems) dataset consists of around 1,000 crowd-sourced Python |
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programming problems, designed to be solvable by entry level programmers, covering programming |
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fundamentals, standard library functionality, and so on. Each problem consists of a task |
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description, code solution and 3 automated test cases. The sanitized subset of the data has been |
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hand-verified by the authors. |
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""" |
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_URLs = { |
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"full": "https://raw.githubusercontent.com/google-research/google-research/master/mbpp/mbpp.jsonl", |
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"sanitized": "https://raw.githubusercontent.com/google-research/google-research/master/mbpp/sanitized-mbpp.json", |
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} |
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_CITATION = """\ |
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@article{austin2021program, |
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title={Program Synthesis with Large Language Models}, |
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author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others}, |
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journal={arXiv preprint arXiv:2108.07732}, |
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year={2021} |
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}""" |
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_HOMEPAGE = "https://github.com/google-research/google-research/tree/master/mbpp" |
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_LICENSE = "CC-BY-4.0" |
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class MBPP(datasets.GeneratorBasedBuilder): |
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"""MBPP: Mostly Basic Python Problems Dataset""" |
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VERSION = datasets.Version("1.0.2") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="full", |
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version=datasets.Version("1.0.2"), |
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description=_DESCRIPTION, |
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), |
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datasets.BuilderConfig(name="sanitized", version=datasets.Version("1.0.2"), description=_DESCRIPTION), |
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] |
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DEFAULT_CONFIG_NAME = "full" |
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def _info(self): |
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if self.config.name == "full": |
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features = datasets.Features( |
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{ |
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"task_id": datasets.Value("int32"), |
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"text": datasets.Value("string"), |
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"code": datasets.Value("string"), |
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"test_list": datasets.Sequence(datasets.Value("string")), |
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"test_setup_code": datasets.Value("string"), |
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"challenge_test_list": datasets.Sequence(datasets.Value("string")), |
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} |
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) |
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elif self.config.name == "sanitized": |
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features = datasets.Features( |
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{ |
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"source_file": datasets.Value("string"), |
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"task_id": datasets.Value("int32"), |
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"prompt": datasets.Value("string"), |
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"code": datasets.Value("string"), |
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"test_imports": datasets.Sequence(datasets.Value("string")), |
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"test_list": datasets.Sequence(datasets.Value("string")), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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config_urls = _URLs[self.config.name] |
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data_dir = dl_manager.download_and_extract(config_urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": data_dir, "split": "train"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": data_dir, "split": "test"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": data_dir, "split": "validation"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("prompt"), |
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gen_kwargs={"filepath": data_dir, "split": "prompt"}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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if self.config.name == "full": |
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def _read_lines(fn, start, end): |
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data = [] |
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with open(fn, encoding="utf-8") as f: |
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for line in f: |
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sample = json.loads(line) |
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if start <= sample["task_id"] <= end: |
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data.append(sample) |
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elif sample["task_id"] > end: |
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break |
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return data |
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if split == "test": |
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data = _read_lines(filepath, 11, 510) |
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elif split == "train": |
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data = _read_lines(filepath, 601, 974) |
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elif split == "validation": |
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data = _read_lines(filepath, 511, 600) |
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elif split == "prompt": |
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data = _read_lines(filepath, 1, 10) |
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elif self.config.name == "sanitized": |
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with open(filepath, encoding="utf-8") as f: |
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data = json.load(f) |
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if split == "test": |
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data = [sample for sample in data if 11 <= sample["task_id"] <= 510] |
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elif split == "train": |
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data = [sample for sample in data if 601 <= sample["task_id"] <= 974] |
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elif split == "validation": |
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data = [sample for sample in data if 511 <= sample["task_id"] <= 600] |
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elif split == "prompt": |
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data = [sample for sample in data if 1 <= sample["task_id"] <= 10] |
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id_ = 0 |
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for sample in data: |
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yield id_, sample |
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id_ += 1 |
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