dataset script file

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.
+"""APPS 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 = _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, {k:v for k, v in data.items() if k!='eval_topic'}  
+            key += 1