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Datasets:
NTU-NLP-sg
/
xCodeEval

Tasks:
Translation
Token Classification
Text2Text Generation
Languages:
code
English
Size:
1M<n<10M
ArXiv:
Tags:
programming-language
code
program-synthesis
automatic-code-repair
code-retrieval
code-translation
License:
Dataset card Files Community
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xCodeEval / xCodeEval.py
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# xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval
import io
import json
import zipfile
from collections import defaultdict
import datasets
import urllib.parse
import textwrap
from multiprocessing import Pool, cpu_count
import os
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@misc{khan2023xcodeeval,
title={xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval},
author={Mohammad Abdullah Matin Khan and M Saiful Bari and Xuan Long Do and Weishi Wang and Md Rizwan Parvez and Shafiq Joty},
year={2023},
eprint={2303.03004},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
The ability to solve problems is a hallmark of intelligence and has been an enduring goal in AI. AI systems that can create programs as solutions to problems or assist developers in writing programs can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown impressive abilities in generating new codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the evaluation of these models has often been performed in a scattered way on only one or two specific tasks, in a few languages, at a partial granularity (e.g., function) level and in many cases without proper training data. Even more concerning is that in most cases the evaluation of generated codes has been done in terms of mere lexical overlap rather than actual execution whereas semantic similarity (or equivalence) of two code segments depends only on their ``execution similarity'', i.e., being able to get the same output for a given input.
"""
_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",
]
_NON_RETRIEVAL_LANGUAGES = [
"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",
]
_PROBLEM_DESC_REQ_TASK = [
"apr",
"code_translation",
"program_synthesis",
"tag_classification",
]
_UNIT_TEST_REQ_TASK = [
"apr",
"code_translation",
"program_synthesis",
]
_DESCRIPTIONS = {
"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.
4. `delete_cnt`: A metric comparing `bug_source_code` and `fix_source_code`. Recommended by difflib.
5. `insert_cnt`: A metric comparing `bug_source_code` and `fix_source_code`. Recommended by difflib.
6. `fix_ops_cnt`: A metric comparing `bug_source_code` and `fix_source_code`. Recommended by difflib.
7. `bug_source_code`: Buggy code.
8. `fix_source_code`: A potential fix of the buggy code that passed all the unit tests.
9. `lang`: Runtime/Compiler version of the `source_code`.
10. `fix_code_uid`: A unique ID for the fix code. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `fix_code_uid`.
11. `bug_code_uid`: A unique ID for the buggy code. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `bug_code_uid`.
12. `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)
13. `apr_id`: A unique ID for the apr sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `apr_id`.
14. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder.
15. `tags`: List of potential algorithmic techniques required to write the program.
16. `bug_exec_outcome`: A pre-run execution outcome of `bug_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 compact and titan test data.
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 compact and titan 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
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`.
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)
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 compact and titan test data.
7. `lang_cluster`: A generic programming language name the value of `lang` belongs to.
8. `prob_desc_description`: Problem description in textual format, math operations are written in latex.
9. `prob_desc_input_from`: How the program should take the unit test.
10. `prob_desc_output_to`: Where the program should output the result of the unit test.
11. `prob_desc_time_limit`: Time limit to solve the problem.
12. `prob_desc_memory_limit`: Memory limit to solve the problem.
13. `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.
14. `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.
15. `prob_desc_sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
16. `prob_desc_sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
17. `prob_desc_notes`: Explanation of `sample_inputs` & `sample_outputs`.
18. `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.
19. `file_name`: Name of the source jsonl file from where data is loaded.
20. `hidden_unit_tests`: a list of unit tests returned as string. use `json.loads(hidden_unit_tests)` to load the data.
Objective: Given a source code (`source_code`) in `lang_cluster`, generate a code in target programming language."""
),
"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.
4. `lang_cluster`: A generic programming language name the value of `lang` belongs to.
5. `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)
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 compact and titan test data.
9. `prob_desc_description`: Problem description in textual format, math operations are written in latex.
10. `prob_desc_input_from`: How the program should take the unit test.
11. `prob_desc_output_to`: Where the program should output the result of the unit test.
12. `prob_desc_time_limit`: Time limit to solve the problem.
13. `prob_desc_memory_limit`: Memory limit to solve the problem.
14. `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.
15. `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.
16. `prob_desc_sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
17. `prob_desc_sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
18. `prob_desc_notes`: Explanation of `sample_inputs` & `sample_outputs`.
19. `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.
20. `file_name`: Name of the source jsonl file from where data is loaded.
21. `hidden_unit_tests`: a list of unit tests returned as string. use `json.loads(hidden_unit_tests)` to load the 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
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.
5. `file_name`: Name of the source jsonl file from where data is loaded.
Objective: Given a source_code retrieve similar source code from `retrieval_corpus`."""
),
"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)
5. `file_name`: Name of the source jsonl file from where data is loaded.
Objective: Given a nl (problem description) retrieve similar source code from `retrieval_corpus`."""
),
"retrieval_corpus": textwrap.dedent(
"""### Key Definitions
1. `idx` : unique index for each sample on a specific langauge (read language from filename).
2. `source_code` : A source code given as retrieval document.
3. `file_name`: Name of the source jsonl file from where data is loaded.
Objective: Use the retrival_corpus to perform query for retrieval_nl_code and retrieval_code_code ."""
),
"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.
4. `compilation_error`: True/False, Indicates if the code generates a compilation error or not.
5. `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`.
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.
8. `file_name`: Name of the source jsonl file from where data is loaded.
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
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.
4. `lang_cluster`: A generic programming language name the value of `lang` belongs to.
5. `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`.
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.
8. `prob_desc_description`: Problem description in textual format, math operations are written in latex.
9. `prob_desc_input_from`: How the program should take the unit test.
10. `prob_desc_output_to`: Where the program should output the result of the unit test.
11. `prob_desc_time_limit`: Time limit to solve the problem.
12. `prob_desc_memory_limit`: Memory limit to solve the problem.
13. `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.
14. `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.
15. `prob_desc_sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
16. `prob_desc_sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
17. `prob_desc_notes`: Explanation of `sample_inputs` & `sample_outputs`.
18. `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.
19. `file_name`: Name of the source jsonl file from where data is loaded.
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")),
}
_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",
}
_TEXT_FEATURES = {
"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",
"lang_cluster",
"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",
"lang_cluster",
"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()))
_SHARDS = {
"apr": [
{
"type": "directory",
"name": "apr/test",
"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": "directory",
"name": "apr/train",
"contents": [
{"type": "file", "name": "train_000.jsonl"},
{"type": "file", "name": "train_001.jsonl"},
{"type": "file", "name": "train_002.jsonl"},
{"type": "file", "name": "train_003.jsonl"},
{"type": "file", "name": "train_004.jsonl"},
{"type": "file", "name": "train_005.jsonl"},
{"type": "file", "name": "train_006.jsonl"},
{"type": "file", "name": "train_007.jsonl"},
{"type": "file", "name": "train_008.jsonl"},
{"type": "file", "name": "train_009.jsonl"},
{"type": "file", "name": "train_010.jsonl"},
{"type": "file", "name": "train_011.jsonl"},
{"type": "file", "name": "train_012.jsonl"},
{"type": "file", "name": "train_013.jsonl"},
{"type": "file", "name": "train_014.jsonl"},
{"type": "file", "name": "train_015.jsonl"},
{"type": "file", "name": "train_016.jsonl"},
{"type": "file", "name": "train_017.jsonl"},
{"type": "file", "name": "train_018.jsonl"},
{"type": "file", "name": "train_019.jsonl"},
{"type": "file", "name": "train_020.jsonl"},
{"type": "file", "name": "train_021.jsonl"},
{"type": "file", "name": "train_022.jsonl"},
{"type": "file", "name": "train_023.jsonl"},
{"type": "file", "name": "train_024.jsonl"},
{"type": "file", "name": "train_025.jsonl"},
{"type": "file", "name": "train_026.jsonl"},
{"type": "file", "name": "train_027.jsonl"},
{"type": "file", "name": "train_028.jsonl"},
{"type": "file", "name": "train_029.jsonl"},
{"type": "file", "name": "train_030.jsonl"},
{"type": "file", "name": "train_031.jsonl"},
{"type": "file", "name": "train_032.jsonl"},
{"type": "file", "name": "train_033.jsonl"},
{"type": "file", "name": "train_034.jsonl"},
{"type": "file", "name": "train_035.jsonl"},
{"type": "file", "name": "train_036.jsonl"},
{"type": "file", "name": "train_037.jsonl"},
{"type": "file", "name": "train_038.jsonl"},
{"type": "file", "name": "train_039.jsonl"},
{"type": "file", "name": "train_040.jsonl"},
{"type": "file", "name": "train_041.jsonl"},
{"type": "file", "name": "train_042.jsonl"},
{"type": "file", "name": "train_043.jsonl"},
{"type": "file", "name": "train_044.jsonl"},
{"type": "file", "name": "train_045.jsonl"},
{"type": "file", "name": "train_046.jsonl"},
{"type": "file", "name": "train_047.jsonl"},
{"type": "file", "name": "train_048.jsonl"},
{"type": "file", "name": "train_049.jsonl"},
{"type": "file", "name": "train_050.jsonl"},
{"type": "file", "name": "train_051.jsonl"},
{"type": "file", "name": "train_052.jsonl"},
{"type": "file", "name": "train_053.jsonl"},
{"type": "file", "name": "train_054.jsonl"},
{"type": "file", "name": "train_055.jsonl"},
{"type": "file", "name": "train_056.jsonl"},
{"type": "file", "name": "train_057.jsonl"},
{"type": "file", "name": "train_058.jsonl"},
{"type": "file", "name": "train_059.jsonl"},
{"type": "file", "name": "train_060.jsonl"},
{"type": "file", "name": "train_061.jsonl"},
{"type": "file", "name": "train_062.jsonl"},
{"type": "file", "name": "train_063.jsonl"},
{"type": "file", "name": "train_064.jsonl"},
{"type": "file", "name": "train_065.jsonl"},
{"type": "file", "name": "train_066.jsonl"},
{"type": "file", "name": "train_067.jsonl"},
{"type": "file", "name": "train_068.jsonl"},
{"type": "file", "name": "train_069.jsonl"},
{"type": "file", "name": "train_070.jsonl"},
{"type": "file", "name": "train_071.jsonl"},
{"type": "file", "name": "train_072.jsonl"},
{"type": "file", "name": "train_073.jsonl"},
{"type": "file", "name": "train_074.jsonl"},
{"type": "file", "name": "train_075.jsonl"},
{"type": "file", "name": "train_076.jsonl"},
{"type": "file", "name": "train_077.jsonl"},
{"type": "file", "name": "train_078.jsonl"},
{"type": "file", "name": "train_079.jsonl"},
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{"type": "file", "name": "train_0320.jsonl"},
{"type": "file", "name": "train_0321.jsonl"},
{"type": "file", "name": "train_0322.jsonl"},
{"type": "file", "name": "train_0323.jsonl"},
{"type": "file", "name": "train_0324.jsonl"},
{"type": "file", "name": "train_0325.jsonl"},
{"type": "file", "name": "train_0326.jsonl"},
{"type": "file", "name": "train_0327.jsonl"},
{"type": "file", "name": "train_0328.jsonl"},
{"type": "file", "name": "train_0329.jsonl"},
{"type": "file", "name": "train_0330.jsonl"},
{"type": "file", "name": "train_0331.jsonl"},
{"type": "file", "name": "train_0332.jsonl"},
{"type": "file", "name": "train_0333.jsonl"},
{"type": "file", "name": "train_0334.jsonl"},
{"type": "file", "name": "train_0335.jsonl"},
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{"type": "file", "name": "train_0339.jsonl"},
{"type": "file", "name": "train_0340.jsonl"},
{"type": "file", "name": "train_0341.jsonl"},
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{"type": "file", "name": "train_0343.jsonl"},
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{"type": "file", "name": "train_0350.jsonl"},
{"type": "file", "name": "train_0351.jsonl"},
{"type": "file", "name": "train_0352.jsonl"},
{"type": "file", "name": "train_0353.jsonl"},
{"type": "file", "name": "train_0354.jsonl"},
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{"type": "file", "name": "train_0368.jsonl"},
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{"type": "file", "name": "train_0370.jsonl"},
{"type": "file", "name": "train_0371.jsonl"},
{"type": "file", "name": "train_0372.jsonl"},
{"type": "file", "name": "train_0373.jsonl"},
{"type": "file", "name": "train_0374.jsonl"},
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{"type": "file", "name": "train_0376.jsonl"},
{"type": "file", "name": "train_0377.jsonl"},
{"type": "file", "name": "train_0378.jsonl"},
{"type": "file", "name": "train_0379.jsonl"},
{"type": "file", "name": "train_0380.jsonl"},
{"type": "file", "name": "train_0381.jsonl"},
{"type": "file", "name": "train_0382.jsonl"},
{"type": "file", "name": "train_0383.jsonl"},
{"type": "file", "name": "train_0384.jsonl"},
{"type": "file", "name": "train_0385.jsonl"},
{"type": "file", "name": "train_0386.jsonl"},
{"type": "file", "name": "train_0387.jsonl"},
{"type": "file", "name": "train_0388.jsonl"},
{"type": "file", "name": "train_0389.jsonl"},
{"type": "file", "name": "train_0390.jsonl"},
{"type": "file", "name": "train_0391.jsonl"},
{"type": "file", "name": "train_0392.jsonl"},
{"type": "file", "name": "train_0393.jsonl"},
{"type": "file", "name": "train_0394.jsonl"},
{"type": "file", "name": "train_0395.jsonl"},
{"type": "file", "name": "train_0396.jsonl"},
{"type": "file", "name": "train_0397.jsonl"},
{"type": "file", "name": "train_0398.jsonl"},
],
},
{
"type": "directory",
"name": "code_compilation/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": 421},
],
"tag_classification": [
{
"type": "directory",
"name": "tag_classification/test",
"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": "directory",
"name": "tag_classification/train",
"contents": [
{"type": "file", "name": "train_000.jsonl"},
{"type": "file", "name": "train_001.jsonl"},
{"type": "file", "name": "train_002.jsonl"},
{"type": "file", "name": "train_003.jsonl"},
{"type": "file", "name": "train_004.jsonl"},
{"type": "file", "name": "train_005.jsonl"},
{"type": "file", "name": "train_006.jsonl"},
{"type": "file", "name": "train_007.jsonl"},
{"type": "file", "name": "train_008.jsonl"},
{"type": "file", "name": "train_009.jsonl"},
{"type": "file", "name": "train_010.jsonl"},
{"type": "file", "name": "train_011.jsonl"},
{"type": "file", "name": "train_012.jsonl"},
{"type": "file", "name": "train_013.jsonl"},
{"type": "file", "name": "train_014.jsonl"},
{"type": "file", "name": "train_015.jsonl"},
{"type": "file", "name": "train_016.jsonl"},
{"type": "file", "name": "train_017.jsonl"},
{"type": "file", "name": "train_018.jsonl"},
{"type": "file", "name": "train_019.jsonl"},
{"type": "file", "name": "train_020.jsonl"},
{"type": "file", "name": "train_021.jsonl"},
{"type": "file", "name": "train_022.jsonl"},
{"type": "file", "name": "train_023.jsonl"},
{"type": "file", "name": "train_024.jsonl"},
{"type": "file", "name": "train_025.jsonl"},
{"type": "file", "name": "train_026.jsonl"},
{"type": "file", "name": "train_027.jsonl"},
{"type": "file", "name": "train_028.jsonl"},
{"type": "file", "name": "train_029.jsonl"},
{"type": "file", "name": "train_030.jsonl"},
{"type": "file", "name": "train_031.jsonl"},
{"type": "file", "name": "train_032.jsonl"},
{"type": "file", "name": "train_033.jsonl"},
{"type": "file", "name": "train_034.jsonl"},
{"type": "file", "name": "train_035.jsonl"},
{"type": "file", "name": "train_036.jsonl"},
{"type": "file", "name": "train_037.jsonl"},
{"type": "file", "name": "train_038.jsonl"},
{"type": "file", "name": "train_039.jsonl"},
{"type": "file", "name": "train_040.jsonl"},
{"type": "file", "name": "train_041.jsonl"},
{"type": "file", "name": "train_042.jsonl"},
{"type": "file", "name": "train_043.jsonl"},
{"type": "file", "name": "train_044.jsonl"},
{"type": "file", "name": "train_045.jsonl"},
{"type": "file", "name": "train_046.jsonl"},
{"type": "file", "name": "train_047.jsonl"},
{"type": "file", "name": "train_048.jsonl"},
{"type": "file", "name": "train_049.jsonl"},
{"type": "file", "name": "train_050.jsonl"},
{"type": "file", "name": "train_051.jsonl"},
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{"type": "file", "name": "train_093.jsonl"},
{"type": "file", "name": "train_094.jsonl"},
{"type": "file", "name": "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
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"
)
class xCodeEvalConfig(datasets.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
class xCodeEval(datasets.GeneratorBasedBuilder):
"""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)
split_info = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": train_downloaded_files,
"problem_description_file": prob_desc_file,
},
),
datasets.SplitGenerator(
name='compact',
gen_kwargs={
"filepaths": validation_downloaded_files,
"problem_description_file": prob_desc_file,
"unit_test_db_file": unit_test_db_file,
},
),
datasets.SplitGenerator(
name='titan',
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="compact_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
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)
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
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_tests"] = json.dumps(unit_test_db[src_uid])
yield idx, sample
idx += 1