Update files from the datasets library (from 1.8.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+- code
+licenses:
+- other-C-UDA
+multilinguality:
+- other-programming-languages
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- machine-translation
+---
+# Dataset Card for "code_x_glue_tc_text_to_code"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits-sample-size)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code
+
+### Dataset Summary
+
+CodeXGLUE text-to-code dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code
+
+The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/.
+
+### Supported Tasks and Leaderboards
+
+- `machine-translation`: The dataset can be used to train a model for generating Java code from an **English** natural language description.
+
+### Languages
+
+- Java **programming** language
+
+## Dataset Structure
+
+### Data Instances
+
+An example of 'train' looks as follows.
+```
+{
+    "code": "boolean function ( ) { return isParsed ; }", 
+    "id": 0, 
+    "nl": "check if details are parsed . concode_field_sep Container parent concode_elem_sep boolean isParsed concode_elem_sep long offset concode_elem_sep long contentStartPosition concode_elem_sep ByteBuffer deadBytes concode_elem_sep boolean isRead concode_elem_sep long memMapSize concode_elem_sep Logger LOG concode_elem_sep byte[] userType concode_elem_sep String type concode_elem_sep ByteBuffer content concode_elem_sep FileChannel fileChannel concode_field_sep Container getParent concode_elem_sep byte[] getUserType concode_elem_sep void readContent concode_elem_sep long getOffset concode_elem_sep long getContentSize concode_elem_sep void getContent concode_elem_sep void setDeadBytes concode_elem_sep void parse concode_elem_sep void getHeader concode_elem_sep long getSize concode_elem_sep void parseDetails concode_elem_sep String getType concode_elem_sep void _parseDetails concode_elem_sep String getPath concode_elem_sep boolean verify concode_elem_sep void setParent concode_elem_sep void getBox concode_elem_sep boolean isSmallBox"
+}
+```
+
+### Data Fields
+
+In the following each data field in go is explained for each config. The data fields are the same among all splits.
+
+#### default
+
+|field name| type |                 description                 |
+|----------|------|---------------------------------------------|
+|id        |int32 | Index of the sample                         |
+|nl        |string| The natural language description of the task|
+|code      |string| The programming source code for the task    |
+
+### Data Splits
+
+| name  |train |validation|test|
+|-------|-----:|---------:|---:|
+|default|100000|      2000|2000|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+https://github.com/microsoft, https://github.com/madlag
+
+### Licensing Information
+
+Computational Use of Data Agreement (C-UDA) License.
+
+### Citation Information
+
+```
+@article{iyer2018mapping,
+  title={Mapping language to code in programmatic context},
+  author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
+  journal={arXiv preprint arXiv:1808.09588},
+  year={2018}
+}
+```
+
+### Contributions
+
+Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

code_x_glue_tc_text_to_code.py

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+import json
+from typing import List
+
+import datasets
+
+from .common import Child
+from .generated_definitions import DEFINITIONS
+
+
+_DESCRIPTION = """We use concode dataset which is a widely used code generation dataset from Iyer's EMNLP 2018 paper Mapping Language to Code in Programmatic Context. See paper for details."""
+_CITATION = """@article{iyer2018mapping,
+title={Mapping language to code in programmatic context},
+author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
+journal={arXiv preprint arXiv:1808.09588},
+year={2018}
+}"""
+
+
+class CodeXGlueTcTextToCodeImpl(Child):
+    _DESCRIPTION = _DESCRIPTION
+    _CITATION = _CITATION
+
+    _FEATURES = {
+        "id": datasets.Value("int32"),  # Index of the sample
+        "nl": datasets.Value("string"),  # The natural language description of the task
+        "code": datasets.Value("string"),  # The programming source code for the task
+    }
+
+    _SUPERVISED_KEYS = ["code"]
+
+    SPLITS = {"train": datasets.Split.TRAIN, "dev": datasets.Split.VALIDATION, "test": datasets.Split.TEST}
+
+    def generate_urls(self, split_name):
+        yield "data", f"concode/{split_name}.json"
+
+    def _generate_examples(self, split_name, file_paths):
+        with open(file_paths["data"], encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                entry = json.loads(line)
+                entry["id"] = idx
+                yield idx, entry
+
+
+CLASS_MAPPING = {
+    "CodeXGlueTcTextToCode": CodeXGlueTcTextToCodeImpl,
+}
+
+
+class CodeXGlueTcTextToCode(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)

common.py

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+from typing import List
+
+import datasets
+
+
+# Citation, taken from https://github.com/microsoft/CodeXGLUE
+_DEFAULT_CITATION = """@article{CodeXGLUE,
+         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
+         year={2020},}"""
+
+
+class Child:
+    _DESCRIPTION = None
+    _FEATURES = None
+    _CITATION = None
+    SPLITS = {"train": datasets.Split.TRAIN}
+    _SUPERVISED_KEYS = None
+
+    def __init__(self, info):
+        self.info = info
+
+    def homepage(self):
+        return self.info["project_url"]
+
+    def _info(self):
+        # This is the description that will appear on the datasets page.
+        return datasets.DatasetInfo(
+            description=self.info["description"] + "\n\n" + self._DESCRIPTION,
+            features=datasets.Features(self._FEATURES),
+            homepage=self.homepage(),
+            citation=self._CITATION or _DEFAULT_CITATION,
+            supervised_keys=self._SUPERVISED_KEYS,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        SPLITS = self.SPLITS
+        _URL = self.info["raw_url"]
+        urls_to_download = {}
+        for split in SPLITS:
+            if split not in urls_to_download:
+                urls_to_download[split] = {}
+
+            for key, url in self.generate_urls(split):
+                if not url.startswith("http"):
+                    url = _URL + "/" + url
+                urls_to_download[split][key] = url
+
+        downloaded_files = {}
+        for k, v in urls_to_download.items():
+            downloaded_files[k] = dl_manager.download_and_extract(v)
+
+        return [
+            datasets.SplitGenerator(
+                name=SPLITS[k],
+                gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
+            )
+            for k in SPLITS
+        ]
+
+    def check_empty(self, entries):
+        all_empty = all([v == "" for v in entries.values()])
+        all_non_empty = all([v != "" for v in entries.values()])
+
+        if not all_non_empty and not all_empty:
+            raise RuntimeError("Parallel data files should have the same number of lines.")
+
+        return all_empty
+
+
+class TrainValidTestChild(Child):
+    SPLITS = {
+        "train": datasets.Split.TRAIN,
+        "valid": datasets.Split.VALIDATION,
+        "test": datasets.Split.TEST,
+    }

dataset_infos.json

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+{"default": {"description": "CodeXGLUE text-to-code dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code\n\nWe use concode dataset which is a widely used code generation dataset from Iyer's EMNLP 2018 paper Mapping Language to Code in Programmatic Context. See paper for details.", "citation": "@article{iyer2018mapping,\ntitle={Mapping language to code in programmatic context},\nauthor={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},\njournal={arXiv preprint arXiv:1808.09588},\nyear={2018}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Text-Code/text-to-code", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "nl": {"dtype": "string", "id": null, "_type": "Value"}, "code": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "code", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_tc_text_to_code", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 96225611, "num_examples": 100000, "dataset_name": "code_x_glue_tc_text_to_code"}, "validation": {"name": "validation", "num_bytes": 1749751, "num_examples": 2000, "dataset_name": "code_x_glue_tc_text_to_code"}, "test": {"name": "test", "num_bytes": 1609306, "num_examples": 2000, "dataset_name": "code_x_glue_tc_text_to_code"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Text-Code/text-to-code/dataset/concode/train.json": {"num_bytes": 97365680, "checksum": "a130f375c415932ffe0188e76b3c8aaef92b1b52d228e342328657b9ae97f17f"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Text-Code/text-to-code/dataset/concode/dev.json": {"num_bytes": 1772646, "checksum": "cd4f91cfaa12a886a1d7acaf92eaf8ab066845c37a9221e56634d345958c922a"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Text-Code/text-to-code/dataset/concode/test.json": {"num_bytes": 1631312, "checksum": "3323b1d723c2183a0ef4693413d25ef5a6b988b50b37b2f401de6c2c5c55159f"}}, "download_size": 100769638, "post_processing_size": null, "dataset_size": 99584668, "size_in_bytes": 200354306}}

dummy/default/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7b0dd38ceaf2e9e89360ef81492a730865ddde60823af5b9b95380a32db20de
+size 4674

generated_definitions.py

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+DEFINITIONS = {
+    "default": {
+        "class_name": "CodeXGlueTcTextToCode",
+        "dataset_type": "Text-Code",
+        "description": "CodeXGLUE text-to-code dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code",
+        "dir_name": "text-to-code",
+        "name": "default",
+        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Text-Code/text-to-code",
+        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Text-Code/text-to-code/dataset",
+        "sizes": {"test": 2000, "train": 100000, "validation": 2000},
+    }
+}