diff --git "a/xCodeEval.py" "b/xCodeEval.py" --- "a/xCodeEval.py" +++ "b/xCodeEval.py" @@ -9,6 +9,7 @@ import urllib.parse import textwrap from multiprocessing import Pool, cpu_count import os + logger = datasets.logging.get_logger(__name__) _CITATION = """\ @@ -31,66 +32,66 @@ _HOMEPAGE = "https://github.com/ntunlp/xCodeEval" _LICENSE = "CC-BY-NC-4.0" _RETRIEVAL_LANGUAGES = [ - "C#", - "C++", - "C", - "D", - "Go", - "Haskell", - "Java", - "Javascript", - "Kotlin", - "Ocaml", - "PHP", - "Pascal", - "Perl", - "Python", - "Ruby", - "Rust", - "Scala" + "C#", + "C++", + "C", + "D", + "Go", + "Haskell", + "Java", + "Javascript", + "Kotlin", + "Ocaml", + "PHP", + "Pascal", + "Perl", + "Python", + "Ruby", + "Rust", + "Scala", ] _NON_RETRIEVAL_LANGUAGES = [ - "C#", - "C++", - "C", - "Go", - "Java", - "Javascript", - "Kotlin", - "PHP", - "Python", - "Ruby", - "Rust", + "C#", + "C++", + "C", + "Go", + "Java", + "Javascript", + "Kotlin", + "PHP", + "Python", + "Ruby", + "Rust", ] _TASKS = [ - "apr", - "code_compilation", - "code_translation", - "program_synthesis", - "retrieval_code_code", - "retrieval_nl_code", - "retrieval_corpus", - "tag_classification", + "apr", + "code_compilation", + "code_translation", + "program_synthesis", + "retrieval_code_code", + "retrieval_nl_code", + "retrieval_corpus", + "tag_classification", ] _PROBLEM_DESC_REQ_TASK = [ - "apr", - "code_translation", - "program_synthesis", - "tag_classification", + "apr", + "code_translation", + "program_synthesis", + "tag_classification", ] _UNIT_TEST_REQ_TASK = [ - "apr", - "code_translation", - "program_synthesis", + "apr", + "code_translation", + "program_synthesis", ] _DESCRIPTIONS = { - "apr": textwrap.dedent( - """### Key Definitions + "apr": textwrap.dedent( + """### Key Definitions 1. `similarity_score`: A similarity score between `bug_source_code` and `fix_source_code` given by difflib. 2. `equal_cnt`: A metric comparing `bug_source_code` and `fix_source_code`. Recommended by difflib. 3. `replace_cnt`: A metric comparing `bug_source_code` and `fix_source_code`. Recommended by difflib. @@ -110,10 +111,23 @@ _DESCRIPTIONS = { 17. `fix_exec_outcome`: A pre-run execution outcome of `fix_source_code`. Follow [Section 4.1](https://arxiv.org/pdf/2303.03004.pdf) to know the potential list of outcomes. The `exec_outcome` flags in the training data comes from a pre-run environmeent. However, training data doesn't includes unit-test to avoid potential hacks. We provide unit test for only dev and test data. 18. `potential_dominant_fix_op`: A potential fix op recommended by difflib. 19. `lang_cluster`: A generic programming language name the value of `lang` belongs to. + 20. `prob_desc_description`: Problem description in textual format, math operations are written in latex. + 21. `prob_desc_input_from`: How the program should take the unit test. + 22. `prob_desc_output_to`: Where the program should output the result of the unit test. + 23. `prob_desc_time_limit`: Time limit to solve the problem. + 24. `prob_desc_memory_limit`: Memory limit to solve the problem. + 25. `prob_desc_input_spec`: How and in what order the input will be given to the program? It also includes the date range, types, and sizes. + 26. `prob_desc_output_spec`: How the outputs should be printed. Most of the time the unit test results are matched with an *exact string match* or *floating point comparison* with a precision boundary. + 27. `prob_desc_sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`. + 28. `prob_desc_sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`. + 29. `prob_desc_notes`: Explanation of `sample_inputs` & `sample_outputs`. + 30. `prob_desc_created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format. + 31. `file_name`: Name of the source jsonl file from where data is loaded. + 32. `hidden_unit_tests`: a list of unit tests returned as string. use `json.loads(hidden_unit_tests)` to load the data. Objective: Given a `bug_source_code` the objective is to generate a fixed version of the code that passes all the unit test. Use `fix_source_code` for training.""" - ), - "code_translation": textwrap.dedent( - """### Key Definitions + ), + "code_translation": textwrap.dedent( + """### Key Definitions 1. `lang`: Runtime/Compiler version of the `source_code`. 2. `source_code`: A program. 3. `code_uid`: A unique ID for the sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `code_uid`. @@ -121,9 +135,9 @@ _DESCRIPTIONS = { 5. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder. 6. `exec_outcome`: Execution outcome status. Follow [Section 4.1](https://arxiv.org/pdf/2303.03004.pdf) to know the potential list of outcomes. The `exec_outcome` flags in the training data comes from a pre-run environmeent. However, training data doesn't includes unit-test to avoid potential hacks. We provide unit test for only dev and test data. Objective: Given a source code in lang, generate a code in target lang.""" - ), - "program_synthesis": textwrap.dedent( - """### Key Definitions + ), + "program_synthesis": textwrap.dedent( + """### Key Definitions 1. `lang`: Runtime/Compiler version of the `source_code`. 2. `source_code`: A program. 3. `tags`: List of potential algorithmic techniques required to write the program. @@ -132,31 +146,32 @@ _DESCRIPTIONS = { 6. `code_uid`: A unique ID for the sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `code_uid`. 7. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder. 8. `exec_outcome`: Execution outcome status. Follow [Section 4.1](https://arxiv.org/pdf/2303.03004.pdf) to know the potential list of outcomes. The `exec_outcome` flags in the training data comes from a pre-run environmeent. However, training data doesn't includes unit-test to avoid potential hacks. We provide unit test for only dev and test data. - Objective: Given a src_uid read problem description from `problem_descriptions.jsonl` and generate a solution for problem description."""), - "retrieval_code_code": textwrap.dedent( - """### Key Definitions + Objective: Given a src_uid read problem description from `problem_descriptions.jsonl` and generate a solution for problem description.""" + ), + "retrieval_code_code": textwrap.dedent( + """### Key Definitions 1. `positive_code` : list of positive codes for `nl` 2. `negative_code` : list of negative codes for `nl` 3. `src_uid` : A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl) 4. `source_code` : A source code given as input query. Objective: Given a source_code retrieve similar source code from `retrieval_corpus`.""" - ), - "retrieval_nl_code": textwrap.dedent( - """### Key Definitions + ), + "retrieval_nl_code": textwrap.dedent( + """### Key Definitions 1. `nl` : Problem description in textual format, math operations are written in latex. Given as input query. 2. `positive_code` : list of positive codes for `nl` 3. `negative_code` : list of negative codes for `nl` 4. `src_uid` : A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl) Objective: Given a nl (problem description) retrieve similar source code from `retrieval_corpus`.""" - ), - "retrieval_corpus": textwrap.dedent( - """### Key Definitions + ), + "retrieval_corpus": textwrap.dedent( + """### Key Definitions 1. `idx` : unique index for each sample on a specific langauge (read language from filename). 4. `source_code` : A source code given as retrieval document. Objective: Use the retrival_corpus to perform query for retrieval_nl_code and retrieval_code_code .""" - ), - "code_compilation": textwrap.dedent( - """### Key Definitions + ), + "code_compilation": textwrap.dedent( + """### Key Definitions 1. `lang`: Runtime/Compiler version of the `source_code`. 2. `source_code`: A program. 3. `lang_cluster`: A generic programming language name the value of `lang` belongs to. @@ -165,9 +180,9 @@ _DESCRIPTIONS = { 6. `src_uid`: A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl) 7. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder. Objective: Given a `source_code` the objective is to classify if the code compiles or not (label:compilation_error) .""" - ), - "tag_classification": textwrap.dedent( - """### Key Definitions + ), + "tag_classification": textwrap.dedent( + """### Key Definitions 1. `lang`: Runtime/Compiler version of the `source_code`. 2. `source_code`: A program. 3. `tags`: List of potential algorithmic techniques required to write the program. @@ -176,1962 +191,2295 @@ _DESCRIPTIONS = { 6. `src_uid`: A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl) 7. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder. Objective: Given a `source_code` the objective is to classify the code into multi-label tags (label:tags) .""" - ), + ), } _CUSTOM_FEATURES = { - "tags": datasets.Sequence(datasets.Value("string")), - "similarity_score": datasets.Value("float32"), - "equal_cnt": datasets.Value("int32"), - "replace_cnt": datasets.Value("int32"), - "delete_cnt": datasets.Value("int32"), - "insert_cnt": datasets.Value("int32"), - "fix_ops_cnt": datasets.Value("int32"), - "difficulty": datasets.Value("int32"), - "compilation_error": datasets.Value("bool"), - # "prob_desc_sample_inputs": datasets.Sequence(datasets.Value("string")), - # "prob_desc_sample_outputs": datasets.Sequence(datasets.Value("string")), + "tags": datasets.Sequence(datasets.Value("string")), + "similarity_score": datasets.Value("float32"), + "equal_cnt": datasets.Value("int32"), + "replace_cnt": datasets.Value("int32"), + "delete_cnt": datasets.Value("int32"), + "insert_cnt": datasets.Value("int32"), + "fix_ops_cnt": datasets.Value("int32"), + "difficulty": datasets.Value("int32"), + "compilation_error": datasets.Value("bool"), + # "prob_desc_sample_inputs": datasets.Sequence(datasets.Value("string")), + # "prob_desc_sample_outputs": datasets.Sequence(datasets.Value("string")), } _PROB_DESC_TEXT_FEATURES = { - "description" : "prob_desc_description", - "input_from" : "prob_desc_input_from", - "output_to" : "prob_desc_output_to", - "time_limit" : "prob_desc_time_limit", - "memory_limit" : "prob_desc_memory_limit", - "input_spec" : "prob_desc_input_spec", - "output_spec" : "prob_desc_output_spec", - "sample_inputs" : "prob_desc_sample_inputs", - "sample_outputs" : "prob_desc_sample_outputs", - "notes" : "prob_desc_notes", - "created_at" : "prob_desc_created_at", + "description": "prob_desc_description", + "input_from": "prob_desc_input_from", + "output_to": "prob_desc_output_to", + "time_limit": "prob_desc_time_limit", + "memory_limit": "prob_desc_memory_limit", + "input_spec": "prob_desc_input_spec", + "output_spec": "prob_desc_output_spec", + "sample_inputs": "prob_desc_sample_inputs", + "sample_outputs": "prob_desc_sample_outputs", + "notes": "prob_desc_notes", + "created_at": "prob_desc_created_at", } _TEXT_FEATURES = { - "apr": { - "file_name", - "similarity_score", - "equal_cnt", - "replace_cnt", - "delete_cnt", - "delete_cnt", - "fix_ops_cnt", - "bug_source_code", - "fix_source_code", - "lang", - "fix_code_uid", - "bug_code_uid", - "src_uid", - "apr_id", - "difficulty", - "tags", - "bug_exec_outcome", - "fix_exec_outcome", - "potential_dominant_fix_op", - "lang_cluster", - }, - "code_translation": { - "file_name", - "lang", - "source_code", - "code_uid", - "src_uid", - "difficulty", - "exec_outcome", - }, - "program_synthesis": { - "file_name", - "lang", - "source_code", - "tags", - "lang_cluster", - "src_uid", - "code_uid", - "difficulty", - "exec_outcome" - }, - "retrieval_code_code": { - "file_name", - "positive_code", - "negative_code", - "src_uid", - "source_code", - }, - "retrieval_nl_code": { - "file_name", - "nl", - "positive_code", - "negative_code", - "src_uid", - }, - "retrieval_corpus": { - "file_name", - "idx", - "source_code", - }, - "code_compilation": { - "file_name", - "lang", - "source_code", - "compilation_error", - "code_uid", - "src_uid", - "difficulty", - }, - "tag_classification": { - "file_name", - "lang", - "source_code", - "tags", - "lang_cluster", - "code_uid", - "src_uid", - "difficulty", - }, + "apr": { + "file_name", + "similarity_score", + "equal_cnt", + "replace_cnt", + "insert_cnt", + "delete_cnt", + "fix_ops_cnt", + "bug_source_code", + "fix_source_code", + "lang", + "fix_code_uid", + "bug_code_uid", + "src_uid", + "apr_id", + "difficulty", + "tags", + "bug_exec_outcome", + "fix_exec_outcome", + "potential_dominant_fix_op", + "lang_cluster", + }, + "code_translation": { + "file_name", + "lang", + "source_code", + "code_uid", + "src_uid", + "difficulty", + "exec_outcome", + }, + "program_synthesis": { + "file_name", + "lang", + "source_code", + "tags", + "lang_cluster", + "src_uid", + "code_uid", + "difficulty", + "exec_outcome", + }, + "retrieval_code_code": { + "file_name", + "positive_code", + "negative_code", + "src_uid", + "source_code", + }, + "retrieval_nl_code": { + "file_name", + "nl", + "positive_code", + "negative_code", + "src_uid", + }, + "retrieval_corpus": { + "file_name", + "idx", + "source_code", + }, + "code_compilation": { + "file_name", + "lang", + "source_code", + "compilation_error", + "code_uid", + "src_uid", + "difficulty", + }, + "tag_classification": { + "file_name", + "lang", + "source_code", + "tags", + "lang_cluster", + "code_uid", + "src_uid", + "difficulty", + }, } for task in _PROBLEM_DESC_REQ_TASK: - _TEXT_FEATURES[task].update(set(_PROB_DESC_TEXT_FEATURES.values())) - - + _TEXT_FEATURES[task].update(set(_PROB_DESC_TEXT_FEATURES.values())) + + _SHARDS = { - "apr":[ - {"type":"directory","name":"apr/test","contents":[ - 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"train_095.jsonl"}, + {"type": "file", "name": "train_096.jsonl"}, + {"type": "file", "name": "train_097.jsonl"}, + {"type": "file", "name": "train_098.jsonl"}, + {"type": "file", "name": "train_099.jsonl"}, + {"type": "file", "name": "train_100.jsonl"}, + {"type": "file", "name": "train_101.jsonl"}, + {"type": "file", "name": "train_102.jsonl"}, + {"type": "file", "name": "train_103.jsonl"}, + {"type": "file", "name": "train_104.jsonl"}, + {"type": "file", "name": "train_105.jsonl"}, + {"type": "file", "name": "train_106.jsonl"}, + {"type": "file", "name": "train_107.jsonl"}, + {"type": "file", "name": "train_108.jsonl"}, + {"type": "file", "name": "train_109.jsonl"}, + {"type": "file", "name": "train_110.jsonl"}, + ], + }, + { + "type": "directory", + "name": "tag_classification/validation", + "contents": [ + {"type": "file", "name": "C#.jsonl"}, + {"type": "file", "name": "C++.jsonl"}, + {"type": "file", "name": "C.jsonl"}, + {"type": "file", "name": "Go.jsonl"}, + {"type": "file", "name": "Java.jsonl"}, + {"type": "file", "name": "Javascript.jsonl"}, + {"type": "file", "name": "Kotlin.jsonl"}, + {"type": "file", "name": "PHP.jsonl"}, + {"type": "file", "name": "Python.jsonl"}, + {"type": "file", "name": "Ruby.jsonl"}, + {"type": "file", "name": "Rust.jsonl"}, + ], + }, + {"type": "report", "directories": 0, "files": 133}, + ], } + def get_file_name(task_name, split_name): - data = _SHARDS[task_name] - file_names = [] - for split_dict in data: - if split_dict["type"] == "directory" and split_dict["name"] == f"{task_name}/{split_name}": - file_names = [d["name"] for d in split_dict["contents"]] - else: continue - return file_names + data = _SHARDS[task_name] + file_names = [] + for split_dict in data: + if ( + split_dict["type"] == "directory" + and split_dict["name"] == f"{task_name}/{split_name}" + ): + file_names = [d["name"] for d in split_dict["contents"]] + else: + continue + return file_names + -BASE_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/{task_name}/{split}/{file_name}" +BASE_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/{task_name}/{split}/{file_name}" PROBLEM_DESC_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/problem_descriptions.jsonl" -UNIT_TEST_DB_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/unittest_db.json" - +UNIT_TEST_DB_URL = ( + "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/unittest_db.json" +) + + class xCodeEvalConfig(datasets.BuilderConfig): - """BuilderConfig""" + """BuilderConfig""" + + def __init__(self, description, text_features, citation, **kwargs): + """ + Args: + name: Task name from xCodeEval. see tin $_TASKS + text_features: `dict[string, string]`, map from the name of the feature + dict for each text field to the name of the column in the tsv file + citation: Citation of the paper. + **kwargs: keyword arguments forwarded to super. + """ + super(xCodeEvalConfig, self).__init__( + version=datasets.Version("0.1.0"), **kwargs + ) + self.description = description + self.text_features = text_features + self.citation = citation + - def __init__(self, description, text_features, citation, **kwargs): - """ - Args: - name: Task name from xCodeEval. see tin $_TASKS - text_features: `dict[string, string]`, map from the name of the feature - dict for each text field to the name of the column in the tsv file - citation: Citation of the paper. - **kwargs: keyword arguments forwarded to super. - """ - super(xCodeEvalConfig, self).__init__(version=datasets.Version("0.1.0"), **kwargs) - self.description = description - self.text_features = text_features - self.citation = citation - - class xCodeEval(datasets.GeneratorBasedBuilder): - """xCodeEval data loader.""" + """xCodeEval data loader.""" + + # TODO(xtreme): Set up version. + VERSION = datasets.Version("0.1.0") + BUILDER_CONFIGS = [ + xCodeEvalConfig( + name=task_name, + text_features=_TEXT_FEATURES[task_name], + description=_DESCRIPTIONS[task_name], + citation=_CITATION, + ) + for task_name in _TASKS + ] + + def _info(self): + features = { + feature: _CUSTOM_FEATURES[feature] + if feature in _CUSTOM_FEATURES + else datasets.Value("string") + for feature in self.config.text_features + } + if self.config.name in _UNIT_TEST_REQ_TASK: + features["hidden_unit_tests"] = datasets.Value("string") + return datasets.DatasetInfo( + description=self.config.description + "\n" + _DESCRIPTION, + features=datasets.Features(features), + supervised_keys=None, + homepage="https://github.com/ntunlp/xCodeEval", + citation=self.config.citation, + ) + + def _split_generators(self, dl_manager): + task_name = self.config.name + if task_name == "retrieval_corpus": + TEST_FILE_NAMES = get_file_name(task_name, "") + test_urls = [ + BASE_URL.format( + task_name=task_name, + split="", + file_name=urllib.parse.quote(file_name), + ).replace("s//", "s/") + for file_name in TEST_FILE_NAMES + ] + # test_urls = [ BASE_URL.format(task_name=task_name, split="", file_name=file_name) for file_name in TEST_FILE_NAMES] + test_downloaded_files = dl_manager.download(test_urls) + # test_downloaded_files = test_urls + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepaths": test_downloaded_files, + }, + ), + ] + + TRAIN_FILE_NAMES = get_file_name(task_name, "train") + VALIDATION_FILE_NAMES = get_file_name(task_name, "validation") + TEST_FILE_NAMES = get_file_name(task_name, "test") + + train_urls = [ + BASE_URL.format( + task_name=task_name, + split="train", + file_name=urllib.parse.quote(file_name), + ) + for file_name in TRAIN_FILE_NAMES + ] + validation_urls = [ + BASE_URL.format( + task_name=task_name, + split="validation", + file_name=urllib.parse.quote(file_name), + ) + for file_name in VALIDATION_FILE_NAMES + ] + test_urls = [ + BASE_URL.format( + task_name=task_name, + split="test", + file_name=urllib.parse.quote(file_name), + ) + for file_name in TEST_FILE_NAMES + ] + + # train_urls = [ BASE_URL.format(task_name=task_name, split="train", file_name=file_name) for file_name in TRAIN_FILE_NAMES] + # validation_urls = [ BASE_URL.format(task_name=task_name, split="validation", file_name=file_name) for file_name in VALIDATION_FILE_NAMES] + # test_urls = [ BASE_URL.format(task_name=task_name, split="test", file_name=file_name) for file_name in TEST_FILE_NAMES] + + train_downloaded_files = dl_manager.download(train_urls) + validation_downloaded_files = dl_manager.download(validation_urls) + test_downloaded_files = dl_manager.download(test_urls) + # train_downloaded_files = train_urls + # validation_downloaded_files = validation_urls + # test_downloaded_files = test_urls + + if task_name == "code_translation": + VALIDATION_SMALL_FILE_NAMES = get_file_name(task_name, "validation_small") + validation_small_urls = [ + BASE_URL.format( + task_name=task_name, + split="validation_small", + file_name=urllib.parse.quote(file_name), + ) + for file_name in VALIDATION_SMALL_FILE_NAMES + ] + validation_small_downloaded_files = dl_manager.download( + validation_small_urls + ) + # validation_small_urls = [ BASE_URL.format(task_name=task_name, split="validation_small", file_name=file_name) for file_name in VALIDATION_SMALL_FILE_NAMES] + # validation_small_downloaded_files = validation_small_urls + + prob_desc_file, unit_test_db_file = None, None + if task_name in _PROBLEM_DESC_REQ_TASK: + prob_desc_file = dl_manager.download(PROBLEM_DESC_URL) + if task_name in _UNIT_TEST_REQ_TASK: + unit_test_db_file = dl_manager.download(UNIT_TEST_DB_URL) - # TODO(xtreme): Set up version. - VERSION = datasets.Version("0.1.0") - BUILDER_CONFIGS = [ - xCodeEvalConfig( - name=task_name, - text_features = _TEXT_FEATURES[task_name], - description=_DESCRIPTIONS[task_name], - citation=_CITATION - ) - for task_name in _TASKS - ] + split_info = [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepaths": train_downloaded_files, + "problem_description_file": prob_desc_file, + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filepaths": validation_downloaded_files, + "problem_description_file": prob_desc_file, + "unit_test_db_file": unit_test_db_file, + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepaths": test_downloaded_files, + "problem_description_file": prob_desc_file, + "unit_test_db_file": unit_test_db_file, + }, + ), + ] + if task_name == "code_translation": + split_info.append( + datasets.SplitGenerator( + name="validation_small", + gen_kwargs={ + "filepaths": validation_small_downloaded_files, + "problem_description_file": prob_desc_file, + "unit_test_db_file": unit_test_db_file, + }, + ), + ) + return split_info - - def _info(self): - features = { - feature: _CUSTOM_FEATURES[feature] if feature in _CUSTOM_FEATURES else datasets.Value("string") - for feature in self.config.text_features - } - if self.config.name in _UNIT_TEST_REQ_TASK: - features["hidden_unit_test"] = datasets.Value("string") - return datasets.DatasetInfo( - description=self.config.description + "\n" + _DESCRIPTION, - features=datasets.Features( - features - ), - supervised_keys=None, - homepage="https://github.com/ntunlp/xCodeEval", - citation=self.config.citation, - ) - - - def _split_generators(self, dl_manager): - task_name = self.config.name - if task_name == "retrieval_corpus": - TEST_FILE_NAMES = get_file_name(task_name, "") - test_urls = [ BASE_URL.format(task_name=task_name, split="", file_name=urllib.parse.quote(file_name)).replace("s//", "s/") for file_name in TEST_FILE_NAMES] - # test_urls = [ BASE_URL.format(task_name=task_name, split="", file_name=file_name) for file_name in TEST_FILE_NAMES] - test_downloaded_files = dl_manager.download(test_urls) - # test_downloaded_files = test_urls - return [ - datasets.SplitGenerator( - name=datasets.Split.TEST, - gen_kwargs={ - "filepaths": test_downloaded_files, - } - ), - ] - - TRAIN_FILE_NAMES = get_file_name(task_name, "train") - VALIDATION_FILE_NAMES = get_file_name(task_name, "validation") - TEST_FILE_NAMES = get_file_name(task_name, "test") + def _generate_examples( + self, filepaths, problem_description_file=None, unit_test_db_file=None + ): + """This function returns the examples""" + task_name = self.config.name + problem_descriptions = None + if problem_description_file is not None: + problem_descriptions = {} + with open(problem_description_file) as rp: + for line in rp: + prob_desc = json.loads(line) + src_uid = prob_desc["src_uid"] + problem_descriptions[src_uid] = prob_desc - train_urls = [ BASE_URL.format(task_name=task_name, split="train", file_name=urllib.parse.quote(file_name)) for file_name in TRAIN_FILE_NAMES] - validation_urls = [ BASE_URL.format(task_name=task_name, split="validation", file_name=urllib.parse.quote(file_name)) for file_name in VALIDATION_FILE_NAMES] - test_urls = [ BASE_URL.format(task_name=task_name, split="test", file_name=urllib.parse.quote(file_name)) for file_name in TEST_FILE_NAMES] + unit_test_db = None + if unit_test_db_file is not None: + with open(unit_test_db_file) as rp: + unit_test_db = json.load(rp) - # train_urls = [ BASE_URL.format(task_name=task_name, split="train", file_name=file_name) for file_name in TRAIN_FILE_NAMES] - # validation_urls = [ BASE_URL.format(task_name=task_name, split="validation", file_name=file_name) for file_name in VALIDATION_FILE_NAMES] - # test_urls = [ BASE_URL.format(task_name=task_name, split="test", file_name=file_name) for file_name in TEST_FILE_NAMES] - - - train_downloaded_files = dl_manager.download(train_urls) - validation_downloaded_files = dl_manager.download(validation_urls) - test_downloaded_files = dl_manager.download(test_urls) - # train_downloaded_files = train_urls - # validation_downloaded_files = validation_urls - # test_downloaded_files = test_urls + idx = 0 + for filepath in filepaths: + file_name = filepath.split("/")[-1] + with open(filepath) as rp: + for line in rp: + sample = json.loads(line) + sample["file_name"] = file_name + for pre_feature in list(sample.keys()): + if pre_feature not in _TEXT_FEATURES[task_name]: + sample.pop(pre_feature) + for feature in _TEXT_FEATURES[task_name]: + if feature not in sample: + sample[feature] = "" + if task_name in _UNIT_TEST_REQ_TASK: + sample["hidden_unit_tests"] = "" + if ( + task_name not in _PROBLEM_DESC_REQ_TASK + or problem_descriptions is None + ): + yield idx, sample # if problem_description_file is None then unit_test_db_file should be None + idx += 1 + continue - if task_name == "code_translation": - VALIDATION_SMALL_FILE_NAMES = get_file_name(task_name, "validation_small") - validation_small_urls = [ BASE_URL.format(task_name=task_name, split="validation_small", file_name=urllib.parse.quote(file_name)) for file_name in VALIDATION_SMALL_FILE_NAMES] - validation_small_downloaded_files = dl_manager.download(validation_small_urls) - # validation_small_urls = [ BASE_URL.format(task_name=task_name, split="validation_small", file_name=file_name) for file_name in VALIDATION_SMALL_FILE_NAMES] - # validation_small_downloaded_files = validation_small_urls - - prob_desc_file, unit_test_db_file = None, None - if task_name in _PROBLEM_DESC_REQ_TASK: - prob_desc_file = dl_manager.download(PROBLEM_DESC_URL) - if task_name in _UNIT_TEST_REQ_TASK: - unit_test_db_file = dl_manager.download(UNIT_TEST_DB_URL) - - split_info = [ - datasets.SplitGenerator( - name=datasets.Split.TRAIN, - gen_kwargs={ - "filepaths": train_downloaded_files, - "problem_description_file": prob_desc_file, - }), - datasets.SplitGenerator( - name=datasets.Split.VALIDATION, - gen_kwargs={ - "filepaths": validation_downloaded_files, - "problem_description_file": prob_desc_file, - "unit_test_db_file": unit_test_db_file, - } - ), - datasets.SplitGenerator( - name=datasets.Split.TEST, - gen_kwargs={ - "filepaths": test_downloaded_files, - "problem_description_file": prob_desc_file, - "unit_test_db_file": unit_test_db_file, - } - ), - ] - if task_name == "code_translation": - split_info.append( - datasets.SplitGenerator( - name="validation_small", - gen_kwargs={ - "filepaths": validation_small_downloaded_files, - "problem_description_file": prob_desc_file, - "unit_test_db_file": unit_test_db_file, - } - ), - ) - return split_info - - def _generate_examples(self, filepaths, problem_description_file = None, unit_test_db_file = None): - """This function returns the examples""" - task_name = self.config.name - problem_descriptions = None - if problem_description_file is not None: - problem_descriptions = {} - with open(problem_description_file) as rp: - for line in rp: - prob_desc = json.loads(line) - src_uid = prob_desc["src_uid"] - problem_descriptions[src_uid] = prob_desc + src_uid = sample["src_uid"] + # if problem_description_file is not None, the sample has `src_uid` + prob_desc = problem_descriptions[src_uid] + for key, ckey in _PROB_DESC_TEXT_FEATURES.items(): + if ( + ckey == "prob_desc_sample_inputs" + or ckey == "prob_desc_sample_outputs" + ): + sample[ckey] = json.dumps(prob_desc[key]) + else: + sample[ckey] = prob_desc[key] - unit_test_db = None - if unit_test_db_file is not None: - with open(unit_test_db_file) as rp: - unit_test_db = json.load(rp) + if task_name not in _UNIT_TEST_REQ_TASK or unit_test_db is None: + yield idx, sample + idx += 1 + continue - idx = 0 - for filepath in filepaths: - file_name = filepath.split("/")[-1] - with open(filepath) as rp: - for line in rp: - sample = json.loads(line) - sample["file_name"] = file_name - for pre_feature in list(sample.keys()): - if pre_feature not in _TEXT_FEATURES[task_name]: - sample.pop(pre_feature) - for feature in _TEXT_FEATURES[task_name]: - if feature not in sample: - sample[feature] = "" - if task_name in _UNIT_TEST_REQ_TASK: - sample["hidden_unit_test"] = "" - if task_name not in _PROBLEM_DESC_REQ_TASK or problem_descriptions is None: - yield idx, sample # if problem_description_file is None then unit_test_db_file should be None - idx += 1 - continue - - - src_uid = sample["src_uid"] - # if problem_description_file is not None, the sample has `src_uid` - prob_desc = problem_descriptions[src_uid] - for key, ckey in _PROB_DESC_TEXT_FEATURES.items(): - if ckey == "prob_desc_sample_inputs" or ckey == "prob_desc_sample_outputs": - sample[ckey] = json.dumps(prob_desc[key]) - else: - sample[ckey] = prob_desc[key] - - if task_name not in _UNIT_TEST_REQ_TASK or unit_test_db is None: - yield idx, sample - idx += 1 - continue - - sample["hidden_unit_test"] = json.dumps(unit_test_db[src_uid]) - yield idx, sample - idx += 1 \ No newline at end of file + sample["hidden_unit_tests"] = json.dumps(unit_test_db[src_uid]) + yield idx, sample + idx += 1