Duplicate from BAAI/TACO

Co-authored-by: bowenzhang <bowen92@users.noreply.huggingface.co>

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README.md

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+---
+annotations_creators: []
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- code
+license: apache-2.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets: []
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+paperswithcode_id: taco-topics-in-algorithmic-code-generation
+pretty_name: TACO
+tags:
+- code
+dataset_info:
+  config_name: ALL
+  features:
+  - name: question
+    dtype: string
+  - name: solutions
+    dtype: string
+  - name: starter_code
+    dtype: string
+  - name: input_output
+    dtype: string
+  - name: difficulty
+    dtype: string
+  - name: raw_tags
+    dtype: string
+  - name: name
+    dtype: string
+  - name: source
+    dtype: string
+  - name: tags
+    dtype: string
+  - name: skill_types
+    dtype: string
+  - name: url
+    dtype: string
+  - name: Expected Auxiliary Space
+    dtype: string
+  - name: time_limit
+    dtype: string
+  - name: date
+    dtype: string
+  - name: picture_num
+    dtype: string
+  - name: memory_limit
+    dtype: string
+  - name: Expected Time Complexity
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4239311973
+    num_examples: 25443
+  - name: test
+    num_bytes: 481480755
+    num_examples: 1000
+  download_size: 2419844942
+  dataset_size: 4720792728
+configs:
+- config_name: ALL
+  data_files:
+  - split: train
+    path: ALL/train-*
+  - split: test
+    path: ALL/test-*
+---
+
+# TACO Dataset
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6335113375bed9932474315e/rMxdXcC56S3FEh37oRa2s.png" width="200" height="200">
+
+[TACO](https://github.com/FlagOpen/TACO) is a benchmark for code generation with 26443 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications.
+
+## Key Update:
+We remove and modified some test cases in test set. Please update to use the newest version.
+
+## Dataset Description
+
+- **Repository:** https://github.com/FlagOpen/TACO/
+- **Paper:** [TACO: Topics in Algorithmic COde generation dataset](https://arxiv.org/abs/2312.14852)
+- **Leaderboard:** [Code Generation on CodeContests](https://paperswithcode.com/sota/code-generation-on-taco-code)
+- **Point of Contact:** [Bo-Wen Zhang](mailto:bwzhang@baai.ac.cn)
+
+## Languages
+
+The dataset contains questions in English and code solutions in Python.
+
+## Dataset Structure
+
+```python
+from datasets import load_dataset
+load_dataset("BAAI/TACO")
+
+DatasetDict({
+    train: Dataset({
+        features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'],
+        num_rows: 25443
+    })
+    test: Dataset({
+        features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'],
+        num_rows: 1000
+    })
+})
+```
+
+### How to use it
+
+You can load and iterate through the dataset with the following two lines of code for the train split:
+
+```python
+from datasets import load_dataset
+import json
+
+ds = load_dataset("BAAI/TACO", split="train")
+sample = next(iter(ds))
+# non-empty solutions and input_output features can be parsed from text format this way:
+sample["solutions"] = json.loads(sample["solutions"])
+sample["input_output"] = json.loads(sample["input_output"])
+sample["raw_tags"] = eval(sample["raw_tags"])
+sample["tags"] = eval(sample["tags"])
+sample["skill_types"] = eval(sample["skill_types"])
+print(sample)
+
+#OUTPUT:
+{
+  "question": "You have a deck of $n$ cards, and you'd like to reorder it to a new one.\n\nEach card has a value between $1$ and $n$ equal to $p_i$. ...",
+  "solutions": [
+    "import heapq\nfrom math import sqrt\nimport operator\nimport sys\ninf_var = 0\nif inf_var == 1:\n\tinf = open('input.txt', 'r')\nelse:\n\tinf = sys.stdin\n ...",
+    "t = int(input())\nfor _ in range(t):\n\tn = int(input())\n\tp = list(map(int, input().split()))\n\tans = []\n\tp1 = [-1] * (n + 1)\n\tfor i in range(n):\n\t\tp1[p[i]] = i\n\ti = n\n\twhile i:\n\t\twhile i > 0 and p1[i] == -1:\n\t\t\ti -= 1\n\t\telse:\n\t\t\tif i:\n\t\t\t\tk = 0\n\t\t\t\tfor j in range(p1[i], n):\n\t\t\t\t\tans.append(p[j])\n\t\t\t\t\tp1[p[j]] = -1\n\t\t\t\t\tk += 1\n\t\t\t\tn -= k\n\t\t\t\ti -= 1\n\t\t\telse:\n\t\t\t\tbreak\n\tprint(*ans)\n",
+    "import sys\n\ndef get_ints():\n\treturn map(int, sys.stdin.readline().strip().split())\n\ndef get_list():\n\treturn list(map(int, sys.stdin.readline().strip().split()))\n\ndef get_list_string():\n\treturn list(map(str, sys.stdin.readline().strip().split()))\n\ndef get_string():\n\treturn sys.stdin.readline().strip()\n\ndef get_int():\n\treturn int(sys.stdin.readline().strip())\n\ndef get_print_int(x):\n\tsys.stdout.write(str(x) + '\\n')\n\ndef get_print(x):\n\tsys.stdout.write(x + '\\n')\n\ndef get_print_int_same(x):\n\tsys.stdout.write(str(x) + ' ')\n\ndef get_print_same(x):\n\tsys.stdout.write(x + ' ')\nfrom sys import maxsize\n\ndef solve():\n\tfor _ in range(get_int()):\n\t\tn = get_int()\n\t\tarr = get_list()\n\t\ti = n - 1\n\t\tj = n - 1\n\t\ttemp = sorted(arr)\n\t\tvis = [False] * n\n\t\tans = []\n\t\twhile j >= 0:\n\t\t\tt = j\n\t\t\ttt = []\n\t\t\twhile t >= 0 and arr[t] != temp[i]:\n\t\t\t\tvis[arr[t] - 1] = True\n\t\t\t\ttt.append(arr[t])\n\t\t\t\tt -= 1\n\t\t\tvis[arr[t] - 1] = True\n\t\t\ttt.append(arr[t])\n\t\t\ttt = tt[::-1]\n\t\t\tfor k in tt:\n\t\t\t\tans.append(k)\n\t\t\tj = t - 1\n\t\t\twhile i >= 0 and vis[i]:\n\t\t\t\ti -= 1\n\t\tget_print(' '.join(map(str, ans)))\nsolve()\n",
+    ...
+  ],
+  "starter_code": "",
+  "input_output": {
+    "inputs": [
+      "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n",
+      "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n",
+      "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n2 4 5 3 6 1\n1\n1\n",
+      "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n"
+    ],
+    "outputs": [
+      "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n",
+      "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n",
+      "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n",
+      "\n4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n"
+    ]
+  },
+  "difficulty": "EASY",
+  "raw_tags": [
+    "data structures",
+    "greedy",
+    "math"
+  ],
+  "name": null,
+  "source": "codeforces",
+  "tags": [
+    "Data structures",
+    "Mathematics",
+    "Greedy algorithms"
+  ],
+  "skill_types": [
+    "Data structures",
+    "Greedy algorithms"
+  ],
+  "url": "https://codeforces.com/problemset/problem/1492/B",
+  "Expected Auxiliary Space": null,
+  "time_limit": "1 second",
+  "date": "2021-02-23",
+  "picture_num": "0",
+  "memory_limit": "512 megabytes",
+  "Expected Time Complexity": null
+}
+```
+Each sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level (difficulty), topics of task (raw tags), algorithms (tags) as well as required programming skill types (skill_types) of the problem and its source. 
+
+If a sample has non empty `input_output` feature, you can read it as a dictionary with keys `inputs` and `outputs` and `fn_name` if it exists, and similarily you can parse the solutions into a list of solutions as shown in the code above. 
+
+You can also filter the dataset for the difficulty level: EASY, MEDIUM, MEDIUM_HARD, HARD and VERY_HARD, or filter the programming skill types: Amortized analysis, Bit manipulation, Complete search, Data structures, Dynamic programming, Greedy algorithms, Range queries, Sorting. Just pass the list of difficulties or skills as a list. E.g. if you want the most challenging problems, you need to select the VERY_HARD level:
+
+```python
+ds = load_dataset("BAAI/TACO", split="train", difficulties=["VERY_HARD"])
+print(next(iter(ds))["question"])
+```
+```
+#OUTPUT:
+"""Let S(n) denote the number that represents the digits of n in sorted order. For example, S(1) = 1, S(5) = 5, S(50394) = 3459, S(353535) = 333555.
+Given a number X, compute <image> modulo 109 + 7.
+
+Input
+The first line of input will contain the integer X (1 ≤ X ≤ 10700).
+
+Output
+Print a single integer, the answer to the question.
+
+Examples
+
+Input
+21
+
+Output
+195
+
+Input
+345342
+
+Output
+390548434
+
+Note
+
+The first few values of S are 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 12. The sum of these values is 195.
+```
+Or if you want the problems invovled with Range queries and Sorting, you need to select the skills Range queries and Sorting:
+
+```python
+ds = load_dataset("BAAI/TACO", split="train", skills=["Range queries", "Sorting"])
+```
+
+### Data Fields
+
+|Field|Type|Description|
+|---|---|---|
+|question|string|problem description|
+|solutions|string|some python solutions|
+|input_output|string|Json string with "inputs" and "outputs" of the test cases, might also include "fn_name" the name of the function|
+|difficulty|string|difficulty level of the problem|
+|picture_num|string|the number of pictures in the problem|
+|source|string|the source of the problem|
+|url|string|url of the source of the problem|
+|date|string|the date of the problem|
+|starter_code|string|starter code to include in prompts|
+|time_limit|string|the time consumption limit to solve the problem|
+|memory_limit|string|the memory consumption limit to solve the problem|
+|Expected Auxiliary Space|string|the extra auxiliary space expected to solve the problem|
+|Expected Time Complexity|string|the time complexity expected to solve the problem|
+|raw_tags|string|the topics of the programming task|
+|tags|string|the manually annoatated algorithms needed to solve the problem|
+|skill_types|string|the mapped programming skill types to solve the problem|
+
+
+
+### Data Splits
+
+The dataset contains a train with 25443 samples and test splits with 1000 samples.
+
+### Dataset Statistics
+* 26443 coding problems
+* 1.55M verified solutions
+* for tests split, the average number of test cases is 202.3
+* all files have ground-truth solutions in the test split
+
+## Dataset Creation
+
+To create the TACO dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Aizu
+AtCoder, CodeChef, Codeforces, CodeWars, GeeksforGeeks, HackerEarth, HackerRank, Katti and LeetCode. For more details please refer to the original paper.
+
+## License
+The TACO dataset that is authored by BAAI, Shandong Normal University and Peking University is released under an [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). However, the data also includes content licensed under other permissive licenses such as MIT License, or web-crawled data which is used under the terms of the CC BY 4.0 license ([Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/legalcode)).
+
+We gratefully acknowledge the contributions of the following:
+*   some AtCoder, Codeforces, CodeWars, Kattis, LeetCode material curated from APPS dataset (https://github.com/hendrycks/apps)
+*   some Aizu, AtCoder, CodeChef, Codeforces material curated from CodeContest dataset (https://github.com/google-deepmind/code_contests)
+*   Codeforces materials are sourced from http://codeforces.com.
+*   CodeChef materials are sourced from https://www.codechef.com.
+*   GeekforGeeks materials are sourced from https://www.geeksforgeeks.org
+*   HackerEarth materials are curated from:
+    [Description2Code Dataset](https://github.com/ethancaballero/description2code),
+    licensed under the
+    [MIT open source license](https://opensource.org/licenses/MIT), copyright
+    not specified.
+*   HackerRank materials are sourced from https://www.hackerrank.com. We don't know what the legal rights or data licenses of HackerRank. Please contact us if there is data license.
+## Citation Information
+
+If you find our data, or code helpful, please cite [the original paper](https://arxiv.org/abs/2312.14852):
+
+```
+@article{li2023taco,
+  title={TACO: Topics in Algorithmic COde generation dataset},
+  author={Rongao Li and Jie Fu and Bo-Wen Zhang and Tao Huang and Zhihong Sun and Chen Lyu and Guang Liu and Zhi Jin and Ge Li},
+  journal={arXiv preprint arXiv:2312.14852},
+  year={2023}
+}
+```

TACO.py

@@ -0,0 +1,145 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TACO dataset."""
+
+import json
+import datasets
+
+
+_REPO_NAME = "BAAI/TACO"
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """
+TACO is a benchmark for Python code generation, it includes 25443 problems and 1000 problems for train and test splits.
+"""
+
+_HOMEPAGE = "https://github.com/FlagOpen/TACO"
+_DIFFICULTY = ["EASY", "MEDIUM", "MEDIUM_HARD", "HARD", "VERY_HARD"]
+_DIFFICULTY_CONFIGS = ["ALL"] + _DIFFICULTY
+_SKILL = ['Data structures', 'Sorting', 'Range queries', 'Complete search', 'Amortized analysis', 'Dynamic programming', 'Bit manipulation', 'Greedy algorithms']
+_SKILL_CONFIGS = ["ALL"] + _SKILL
+_URLS = {
+    "train": ['train/data-00000-of-00009.arrow', 'train/data-00001-of-00009.arrow', 'train/data-00002-of-00009.arrow', 'train/data-00003-of-00009.arrow', 'train/data-00004-of-00009.arrow', 'train/data-00005-of-00009.arrow', 'train/data-00006-of-00009.arrow', 'train/data-00007-of-00009.arrow', 'train/data-00008-of-00009.arrow'],  
+    "test": ['test/data-00000-of-00001.arrow'],
+}
+
+    
+class TACOConfig(datasets.BuilderConfig):
+    """BuilderConfig for the TACO dataset."""
+
+    def __init__(self, *args, difficulties=["ALL"], skills=["ALL"], **kwargs):
+        """BuilderConfig for the APPS Code dataset.
+
+        Args:
+            difficulties (:obj:`List[str]`): List of problem difficulty levels to load.
+            skills (:obj:`List[str]`): List of algorithm skills of problems to load.
+            **kwargs: keyword arguments forwarded to super.
+        """
+        if "ALL" in difficulties:
+            assert len(difficulties) == 1
+            self.filter_difficulties = False
+        else:
+            self.filter_difficulties = True
+        if "ALL" in skills:
+            assert len(skills) == 1
+            self.filter_skills = False
+        else:
+            self.filter_skills = True
+        
+        if self.filter_difficulties:
+            subset_name = '+'.join(sorted(difficulties))
+            assert not self.filter_skills, "Not supported to filter difficulties and skills together."
+        elif self.filter_skills:
+            subset_name = '+'.join(sorted(skills))
+        else:
+            subset_name = 'ALL'
+            
+        super().__init__(
+            *args,
+            name=subset_name,
+            **kwargs,
+        )
+        
+        self.subsets = {"difficulties": difficulties, "skills": skills}
+
+
+class TACO(datasets.GeneratorBasedBuilder):
+    """TACO dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+    
+    BUILDER_CONFIG_CLASS = TACOConfig
+    BUILDER_CONFIGS = [
+        TACOConfig(difficulties=[level]) for level in _DIFFICULTY_CONFIGS
+    ] + [
+        TACOConfig(skills=[skill]) for skill in _SKILL_CONFIGS if skill!='ALL'
+    ]
+    DEFAULT_CONFIG_NAME = "ALL"
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'question': datasets.Value(dtype='string', id=None), 
+                'solutions': datasets.Value(dtype='string', id=None), 
+                'starter_code': datasets.Value(dtype='string', id=None), 
+                'input_output': datasets.Value(dtype='string', id=None), 
+                'difficulty': datasets.Value(dtype='string', id=None), 
+                'raw_tags': datasets.Value(dtype='string', id=None), 
+                'name': datasets.Value(dtype='string', id=None), 
+                'source': datasets.Value(dtype='string', id=None), 
+                'tags': datasets.Value(dtype='string', id=None), 
+                'skill_types': datasets.Value(dtype='string', id=None),
+                'url': datasets.Value(dtype='string', id=None), 
+                'Expected Auxiliary Space': datasets.Value(dtype='string', id=None), 
+                'time_limit': datasets.Value(dtype='string', id=None), 
+                'date': datasets.Value(dtype='string', id=None), 
+                'picture_num': datasets.Value(dtype='string', id=None), 
+                'memory_limit': datasets.Value(dtype='string', id=None), 
+                'Expected Time Complexity': datasets.Value(dtype='string', id=None),
+            }),
+            supervised_keys=None,
+            citation=_CITATION,
+            homepage=_HOMEPAGE,
+            license="MIT License",
+            
+        )   
+
+    def _split_generators(self, dl_manager):
+        
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]  
+        
+    def _generate_examples(self, filepath):
+        key = 0
+        dataset = datasets.concatenate_datasets([datasets.Dataset.from_file(file) for file in filepath])
+        for idx, data in enumerate(dataset):
+            difficulty = data['difficulty']
+            skills = eval(data['skill_types'])
+            if self.config.filter_difficulties and not difficulty in self.config.subsets['difficulties']:
+                continue
+            if self.config.filter_skills:
+                valid_skills = self.config.subsets['skills']
+                if not bool(set(valid_skills) & set(skills)):
+                    continue
+
+            yield key, data
+            key += 1

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