Datasets:
Size:
1M<n<10M
ArXiv:
Tags:
programming-language
code
program-synthesis
automatic-code-repair
code-retrieval
code-translation
License:
data loader updated
Browse files- xCodeEval.py +35 -4
xCodeEval.py
CHANGED
@@ -560,7 +560,20 @@ _SHARDS = {
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{"type":"file","name":"Ruby.jsonl"},
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{"type":"file","name":"Rust.jsonl"}
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]},
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-
{"type":"
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],
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"program_synthesis":[
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{"type":"directory","name":"program_synthesis/test","contents":[
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@@ -1927,7 +1940,6 @@ def get_file_name(task_name, split_name):
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BASE_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/{task_name}/{split}/{file_name}"
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PROBLEM_DESC_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/problem_descriptions.jsonl"
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UNIT_TEST_DB_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/unittest_db.json"
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-
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class xCodeEvalConfig(datasets.BuilderConfig):
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"""BuilderConfig"""
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@@ -2005,7 +2017,7 @@ class xCodeEval(datasets.GeneratorBasedBuilder):
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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]
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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]
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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]
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-
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# train_urls = [ BASE_URL.format(task_name=task_name, split="train", file_name=file_name) for file_name in TRAIN_FILE_NAMES]
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# validation_urls = [ BASE_URL.format(task_name=task_name, split="validation", file_name=file_name) for file_name in VALIDATION_FILE_NAMES]
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# test_urls = [ BASE_URL.format(task_name=task_name, split="test", file_name=file_name) for file_name in TEST_FILE_NAMES]
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@@ -2018,13 +2030,20 @@ class xCodeEval(datasets.GeneratorBasedBuilder):
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# validation_downloaded_files = validation_urls
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# test_downloaded_files = test_urls
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prob_desc_file, unit_test_db_file = None, None
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if task_name in _PROBLEM_DESC_REQ_TASK:
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prob_desc_file = dl_manager.download(PROBLEM_DESC_URL)
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if task_name in _UNIT_TEST_REQ_TASK:
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unit_test_db_file = dl_manager.download(UNIT_TEST_DB_URL)
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-
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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@@ -2048,6 +2067,18 @@ class xCodeEval(datasets.GeneratorBasedBuilder):
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}
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),
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]
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def _generate_examples(self, filepaths, problem_description_file = None, unit_test_db_file = None):
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"""This function returns the examples"""
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{"type":"file","name":"Ruby.jsonl"},
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{"type":"file","name":"Rust.jsonl"}
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]},
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+
{"type":"directory","name":"code_translation/validation_small","contents":[
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{"type":"file","name":"C#.jsonl"},
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{"type":"file","name":"C++.jsonl"},
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{"type":"file","name":"C.jsonl"},
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{"type":"file","name":"Go.jsonl"},
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{"type":"file","name":"Java.jsonl"},
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{"type":"file","name":"Javascript.jsonl"},
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{"type":"file","name":"Kotlin.jsonl"},
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{"type":"file","name":"PHP.jsonl"},
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{"type":"file","name":"Python.jsonl"},
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{"type":"file","name":"Ruby.jsonl"},
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{"type":"file","name":"Rust.jsonl"}
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]},
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{"type":"report","directories":0,"files":144}
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],
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"program_synthesis":[
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{"type":"directory","name":"program_synthesis/test","contents":[
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BASE_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/{task_name}/{split}/{file_name}"
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PROBLEM_DESC_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/problem_descriptions.jsonl"
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UNIT_TEST_DB_URL = "https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/resolve/main/unittest_db.json"
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class xCodeEvalConfig(datasets.BuilderConfig):
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"""BuilderConfig"""
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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]
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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]
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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]
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+
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# train_urls = [ BASE_URL.format(task_name=task_name, split="train", file_name=file_name) for file_name in TRAIN_FILE_NAMES]
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# validation_urls = [ BASE_URL.format(task_name=task_name, split="validation", file_name=file_name) for file_name in VALIDATION_FILE_NAMES]
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# test_urls = [ BASE_URL.format(task_name=task_name, split="test", file_name=file_name) for file_name in TEST_FILE_NAMES]
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# validation_downloaded_files = validation_urls
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# test_downloaded_files = test_urls
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+
if task_name == "code_translation":
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VALIDATION_SMALL_FILE_NAMES = get_file_name(task_name, "validation_small")
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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]
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validation_small_downloaded_files = dl_manager.download(validation_small_urls)
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# 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]
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# validation_small_downloaded_files = validation_small_urls
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prob_desc_file, unit_test_db_file = None, None
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if task_name in _PROBLEM_DESC_REQ_TASK:
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prob_desc_file = dl_manager.download(PROBLEM_DESC_URL)
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if task_name in _UNIT_TEST_REQ_TASK:
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unit_test_db_file = dl_manager.download(UNIT_TEST_DB_URL)
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+
split_info = [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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}
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),
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]
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if task_name == "code_translation":
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split_info.append(
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datasets.SplitGenerator(
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name="validation_small",
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gen_kwargs={
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"filepaths": validation_small_downloaded_files,
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"problem_description_file": prob_desc_file,
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"unit_test_db_file": unit_test_db_file,
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}
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),
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)
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return split_info
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def _generate_examples(self, filepaths, problem_description_file = None, unit_test_db_file = None):
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"""This function returns the examples"""
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