Delete taco.py

taco.py

@@ -1,145 +0,0 @@
-# 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