Delete loading script

code_x_glue_tc_nl_code_search_adv.py

@@ -1,206 +0,0 @@
-import json
-import os
-import os.path
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
-- Remove examples that codes cannot be parsed into an abstract syntax tree.
-- Remove examples that #tokens of documents is < 3 or >256
-- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
-- Remove examples that documents are not English.
-"""
-_CITATION = """@article{husain2019codesearchnet,
-title={Codesearchnet challenge: Evaluating the state of semantic code search},
-author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
-journal={arXiv preprint arXiv:1909.09436},
-year={2019}
-}"""
-
-
-class CodeXGlueCtCodeToTextBaseImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    # For each file, each line in the uncompressed file represents one function.
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "repo": datasets.Value("string"),  # repo: the owner/repo
-        "path": datasets.Value("string"),  # path: the full path to the original file
-        "func_name": datasets.Value("string"),  # func_name: the function or method name
-        "original_string": datasets.Value("string"),  # original_string: the raw string before tokenization or parsing
-        "language": datasets.Value("string"),  # language: the programming language name
-        "code": datasets.Value("string"),  # code/function: the part of the original_string that is code
-        "code_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # code_tokens/function_tokens: tokenized version of code
-        "docstring": datasets.Value(
-            "string"
-        ),  # docstring: the top-level comment or docstring, if it exists in the original string
-        "docstring_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # docstring_tokens: tokenized version of docstring
-        "sha": datasets.Value("string"),  # sha of the file
-        "url": datasets.Value("string"),  # url of the file
-    }
-
-    _SUPERVISED_KEYS = ["docstring", "docstring_tokens"]
-
-    def generate_urls(self, split_name, language):
-        yield "language", f"https://huggingface.co/datasets/code_search_net/resolve/main/data/{language}.zip"
-        yield "dataset", "dataset.zip"
-
-    def get_data_files(self, split_name, file_paths, language):
-        language_specific_path = file_paths["language"]
-        final_path = os.path.join(language_specific_path, language, "final")
-        # Make some cleanup to save space
-        for path in os.listdir(final_path):
-            if path.endswith(".pkl"):
-                os.unlink(path)
-
-        data_files = []
-        for root, dirs, files in os.walk(final_path):
-            for file in files:
-                temp = os.path.join(root, file)
-                if ".jsonl" in temp:
-                    if split_name in temp:
-                        data_files.append(temp)
-        return data_files
-
-    def post_process(self, split_name, language, js):
-        return js
-
-    def _generate_examples(self, split_name, file_paths, language):
-        import gzip
-
-        data_set_path = file_paths["dataset"]
-
-        data_files = self.get_data_files(split_name, file_paths, language)
-
-        urls = {}
-        f1_path_parts = [data_set_path, "dataset", language, f"{split_name}.txt"]
-        if self.SINGLE_LANGUAGE:
-            del f1_path_parts[2]
-
-        f1_path = os.path.join(*f1_path_parts)
-        with open(f1_path, encoding="utf-8") as f1:
-            for line in f1:
-                line = line.strip()
-                urls[line] = True
-
-        idx = 0
-        for file in data_files:
-            if ".gz" in file:
-                f = gzip.open(file)
-            else:
-                f = open(file, encoding="utf-8")
-
-            for line in f:
-                line = line.strip()
-                js = json.loads(line)
-                if js["url"] in urls:
-                    js["id"] = idx
-                    js = self.post_process(split_name, language, js)
-                    if "partition" in js:
-                        del js["partition"]
-                    yield idx, js
-                    idx += 1
-            f.close()
-
-
-class CodeXGlueTcNLCodeSearchAdvImpl(CodeXGlueCtCodeToTextBaseImpl):
-    LANGUAGE = "python"
-    SINGLE_LANGUAGE = True
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "repo": datasets.Value("string"),  # repo: the owner/repo
-        "path": datasets.Value("string"),  # path: the full path to the original file
-        "func_name": datasets.Value("string"),  # func_name: the function or method name
-        "original_string": datasets.Value("string"),  # original_string: the raw string before tokenization or parsing
-        "language": datasets.Value("string"),  # language: the programming language
-        "code": datasets.Value("string"),  # code/function: the part of the original_string that is code
-        "code_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # code_tokens/function_tokens: tokenized version of code
-        "docstring": datasets.Value(
-            "string"
-        ),  # docstring: the top-level comment or docstring, if it exists in the original string
-        "docstring_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # docstring_tokens: tokenized version of docstring
-        "sha": datasets.Value("string"),  # sha of the file
-        "url": datasets.Value("string"),  # url of the file
-        "docstring_summary": datasets.Value("string"),  # Summary of the docstring
-        "parameters": datasets.Value("string"),  # parameters of the function
-        "return_statement": datasets.Value("string"),  # return statement
-        "argument_list": datasets.Value("string"),  # list of arguments of the function
-        "identifier": datasets.Value("string"),  # identifier
-        "nwo": datasets.Value("string"),  # nwo
-        "score": datasets.Value("float"),  # score for this search
-    }
-
-    def post_process(self, split_name, language, js):
-        for suffix in "_tokens", "":
-            key = "function" + suffix
-            if key in js:
-                js["code" + suffix] = js[key]
-                del js[key]
-
-        for key in self._FEATURES:
-            if key not in js:
-                if key == "score":
-                    js[key] = -1
-                else:
-                    js[key] = ""
-
-        return js
-
-    def generate_urls(self, split_name):
-        for e in super().generate_urls(split_name, self.LANGUAGE):
-            yield e
-
-    def get_data_files(self, split_name, file_paths, language):
-        if split_name == "train":
-            return super().get_data_files(split_name, file_paths, language)
-        else:
-            data_set_path = file_paths["dataset"]
-            data_file = os.path.join(data_set_path, "dataset", "test_code.jsonl")
-            return [data_file]
-
-    def _generate_examples(self, split_name, file_paths):
-        for e in super()._generate_examples(split_name, file_paths, self.LANGUAGE):
-            yield e
-
-
-CLASS_MAPPING = {
-    "CodeXGlueTcNLCodeSearchAdv": CodeXGlueTcNLCodeSearchAdvImpl,
-}
-
-
-class CodeXGlueTcNlCodeSearchAdv(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = datasets.BuilderConfig
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
-    ]
-
-    def _info(self):
-        name = self.config.name
-        info = DEFINITIONS[name]
-        if info["class_name"] in CLASS_MAPPING:
-            self.child = CLASS_MAPPING[info["class_name"]](info)
-        else:
-            raise RuntimeError(f"Unknown python class for dataset configuration {name}")
-        ret = self.child._info()
-        return ret
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        return self.child._split_generators(dl_manager=dl_manager)
-
-    def _generate_examples(self, split_name, file_paths):
-        return self.child._generate_examples(split_name, file_paths)