# coding=utf-8 # Copyright 2022 CodeQueries Authors and the HuggingFace Datasets Authors. # # 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. """The CodeQueries benchmark.""" import json import os import datasets logger = datasets.logging.get_logger(__name__) _CODEQUERIES_CITATION = """\ @article{codequeries2022, title={Learning to Answer Semantic Queries over Code}, author={A, B, C, D, E, F}, journal={arXiv preprint arXiv:<.>}, year={2022} } """ _IDEAL_DESCRIPTION = """\ CodeQueries Ideal setup. """ _PREFIX_DESCRIPTION = """\ CodeQueries Prefix setup.""" _FILE_IDEAL_DESCRIPTION = """\ CodeQueries File level Ideal setup.""" _TWOSTEP_DESCRIPTION = """\ CodeQueries Twostep setup.""" class CodequeriesConfig(datasets.BuilderConfig): """BuilderConfig for Codequeries.""" def __init__(self, features, citation, data_url, url, **kwargs): """BuilderConfig for Codequeries. Args: features: `list[string]`, list of the features that will appear in the feature dict. Should not include "label". citation: `string`, citation for the data set. **kwargs: keyword arguments forwarded to super. """ # Version history: # 1.0.0: Initial version. super(CodequeriesConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) self.features = features self.citation = citation self.data_url = data_url self.url = url class Codequeries(datasets.GeneratorBasedBuilder): """The Codequeries benchmark.""" BUILDER_CONFIGS = [ CodequeriesConfig( name="ideal", description=_IDEAL_DESCRIPTION, features=["query_name", "context_blocks", "answer_spans", "supporting_fact_spans", "code_file_path", "example_type", "subtokenized_input_sequence", "label_sequence"], citation=_CODEQUERIES_CITATION, data_url={ "train": "ideal_train.json", "dev": "ideal_val.json", "test": "ideal_test.json" }, url="", ), CodequeriesConfig( name="prefix", description=_PREFIX_DESCRIPTION, features=["query_name", "answer_spans", "supporting_fact_spans", "code_file_path", "example_type", "subtokenized_input_sequence", "label_sequence"], citation=_CODEQUERIES_CITATION, data_url={ "test": "prefix_test.json" }, url="", ), CodequeriesConfig( name="file_ideal", description=_FILE_IDEAL_DESCRIPTION, features=["query_name", "context_blocks", "answer_spans", "supporting_fact_spans", "code_file_path", "example_type", "subtokenized_input_sequence", "label_sequence"], citation=_CODEQUERIES_CITATION, data_url={ "test": "file_ideal_test.json" }, url="", ), CodequeriesConfig( name="twostep", description=_TWOSTEP_DESCRIPTION, features=["query_name", "context_blocks", "answer_spans", "supporting_fact_spans", "code_file_path", "example_type", "subtokenized_input_sequence", "label_sequence"], citation=_CODEQUERIES_CITATION, data_url={ "test": ["twostep_relevance/" + "twostep_relevance_test_" + str(i) + ".json" for i in range(0,10)] }, url="", ), ] DEFAULT_CONFIG_NAME = "ideal" def _info(self): features = {} features["query_name"] = datasets.Value("string") features["context_blocks"] = [ { "content": datasets.Value("string"), "metadata": datasets.Value("string"), "header": datasets.Value("string") } ] features["answer_spans"] = [ { 'span': datasets.Value("string"), 'start_line': datasets.Value("int32"), 'start_column': datasets.Value("int32"), 'end_line': datasets.Value("int32"), 'end_column': datasets.Value("int32") } ] features["supporting_fact_spans"] = [ { 'span': datasets.Value("string"), 'start_line': datasets.Value("int32"), 'start_column': datasets.Value("int32"), 'end_line': datasets.Value("int32"), 'end_column': datasets.Value("int32") } ] features["code_file_path"] = datasets.Value("string") features["example_type"] = datasets.Value("int32") features["subtokenized_input_sequence"] = datasets.features.Sequence(datasets.Value("string")) features["label_sequence"] = datasets.features.Sequence(datasets.Value("int32")) features["relevance_label"] = datasets.Value("int32") return datasets.DatasetInfo( description=self.config.description, features=datasets.Features(features), homepage=self.config.url, citation=_CODEQUERIES_CITATION, ) def _split_generators(self, dl_manager): dl_dir = dl_manager.download_and_extract(self.config.data_url) print(dl_dir) if self.config.name in ["prefix", "file_ideal", "twostep"]: return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": dl_dir["test"], "split": datasets.Split.TEST, }, ), ] else: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": dl_dir["train"], "split": datasets.Split.TRAIN, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": dl_dir["dev"], "split": datasets.Split.VALIDATION, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": dl_dir["test"], "split": datasets.Split.TEST, }, ), ] def _generate_examples(self, filepath, split): if self.config.name in ["prefix", "file_ideal", "twostep"]: assert split == datasets.Split.TEST logger.info("generating examples from = %s", filepath) if self.config.name == "twostep": key = 0 for fp in filepath: with open(fp, encoding="utf-8") as f: for line in f: row = json.loads(line) instance_key = str(key) + "_" + row["query_name"] + "_" + row["code_file_path"] yield instance_key, { "query_name": row["query_name"], "context_blocks": row["context_blocks"], "answer_spans": row["answer_spans"], "supporting_fact_spans": row["supporting_fact_spans"], "code_file_path": row["code_file_path"], "example_type": row["example_type"], "subtokenized_input_sequence": row["subtokenized_input_sequence"], "relevance_label": row["relevance_label"], } key += 1 else: with open(filepath, encoding="utf-8") as f: key = 0 for line in f: row = json.loads(line) instance_key = str(key) + "_" + row["query_name"] + "_" + row["code_file_path"] yield instance_key, { "query_name": row["query_name"], "context_blocks": row["context_blocks"], "answer_spans": row["answer_spans"], "supporting_fact_spans": row["supporting_fact_spans"], "code_file_path": row["code_file_path"], "example_type": row["example_type"], "subtokenized_input_sequence": row["subtokenized_input_sequence"], "label_sequence": row["label_sequence"], } key += 1