xCodeEval.py

# 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
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 dev and 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 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.
		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 dev and test data.
		Objective: Given a source code in lang, generate a code in target lang."""
	),
	"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 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
			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
	 		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
	 		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
			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.
   		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.  
   		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"),
	# "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": {
		"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": {
		"lang",
		"source_code",
  		"code_uid",
		"src_uid",
		"difficulty",
		"exec_outcome",
	},
	"program_synthesis": {
		"lang",
		"source_code",
		"tags",
		"lang_cluster",
		"src_uid",
		"code_uid",
		"difficulty",
		"exec_outcome"
	},
	"retrieval_code_code": {
		"positive_code",
		"negative_code",
		"src_uid",
		"source_code",
	},
	"retrieval_nl_code": {
		"nl",
		"positive_code",
		"negative_code",
		"src_uid",
	},
	"retrieval_corpus": {
		"idx", 
		"source_code",
	},
 	"code_compilation": {
		"lang",
		"source_code",
		"tags",
		"compilation_error",
		"code_uid",
		"src_uid",
		"difficulty",
	},
	"tag_classification": {
		"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"},
				{"type":"file","name":"train_080.jsonl"},
				{"type":"file","name":"train_081.jsonl"},
				{"type":"file","name":"train_082.jsonl"},
				{"type":"file","name":"train_083.jsonl"},
				{"type":"file","name":"train_084.jsonl"},
				{"type":"file","name":"train_085.jsonl"},
				{"type":"file","name":"train_086.jsonl"},
				{"type":"file","name":"train_087.jsonl"},
				{"type":"file","name":"train_088.jsonl"},
				{"type":"file","name":"train_089.jsonl"},
				{"type":"file","name":"train_090.jsonl"},
				{"type":"file","name":"train_091.jsonl"},
				{"type":"file","name":"train_092.jsonl"},
				{"type":"file","name":"train_093.jsonl"}
			  ]},
			  {"type":"directory","name":"apr/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":116}
			],
	"code_translation":[
			  {"type":"directory","name":"code_translation/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":"code_translation/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"},
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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_test"] = datasets.Value("string")
		# 	features["hidden_unit_test"] = datasets.features.Sequence(
        #                 {
        #                     "input": datasets.Value("string"),
        #                     "output": datasets.Sequence(datasets.Value("string")),
        #                 }
        #             ),
		return datasets.DatasetInfo(
			# This is the description that will appear on the datasets page.
			description=self.config.description + "\n" + _DESCRIPTION,
			# datasets.features.FeatureConnectors
			features=datasets.Features(
				features
				# These are the features of your dataset like images, labels ...
			),
			# If there's a common (input, target) tuple from the features,
			# specify them here. They'll be used if as_supervised=True in
			# builder.as_dataset.
			supervised_keys=None,
			# Homepage of the dataset for documentation
			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="train", file_name=file_name) for file_name in  TEST_FILE_NAMES]
			test_downloaded_files = dl_manager.download(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_downloaded_files = dl_manager.download(train_urls)
		validation_downloaded_files = dl_manager.download(validation_urls)
		test_downloaded_files = dl_manager.download(test_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)
		
		return [
			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,
	 			}
			),
		]
		
	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)

		for filepath in filepaths:
			with open(filepath) as rp:
				for idx, line in enumerate(rp):
					sample = json.loads(line)
					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
						continue
					
					# if problem_description_file is not None, the sample has `src_uid`
					src_uid = sample["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
						continue
					
					sample["hidden_unit_test"] = json.dumps(unit_test_db[src_uid])
					yield idx, sample