Removed loading script

csqa-sparqltotext.py

@@ -1,239 +0,0 @@
-
-import os
-
-import json
-
-import datasets
-from typing import Any
-
-import sys
-
-
-
-_CITATION = """\
-    @inproceedings{lecorve2022sparql2text,
-        title={Coqar: Question rewriting on coqa},
-        author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.},
-        journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)},
-        year={2022}
-    }
-"""
-
-_HOMEPAGE = ""
-
-_DESCRIPTION = """\
-Special version of CSQA for the SPARQL-to-Text task
-"""
-
-_URLS = {
-    "all": "json/csqa_sparql_to_text.tar.gz"
-}
-
-class CSQA(datasets.GeneratorBasedBuilder):
-    """
-    Complex Sequential Question Answering dataset
-    """
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            #"active_set"
-            #"all_entities"
-            #"bool_ques_type"
-            #"count_ques_sub_type"
-            #"count_ques_type"
-            #"description"
-            #"entities"
-            #"entities_in_utterance"
-            #"gold_actions"
-            #"inc_ques_type"
-            #"is_inc"
-            #"is_incomplete"
-            #"is_spurious"
-            #"masked_verbalized_answer"
-            #"parsed_active_set"
-            #"ques_type_id"
-            #"question-type"
-            #"relations"
-            #"sec_ques_sub_type"
-            #"sec_ques_type"
-            #"set_op_choice"
-            #"set_op"
-            #"sparql_query"
-            #"speaker"
-            #"type_list"
-            #"utterance"
-            #"utterance"
-            #"verbalized_all_entities"
-            #"verbalized_answer"
-            #"verbalized_entities_in_utterance"
-            #"verbalized_gold_actions"
-            #"verbalized_parsed_active_set"
-            #"verbalized_sparql_query"
-            #"verbalized_triple"
-            #"verbalized_type_list"
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "turns": [
-                        {
-                            "id": datasets.Value("int64"),
-                            "ques_type_id": datasets.Value("int64"),
-                            "question-type": datasets.Value("string"),
-                            "description": datasets.Value("string"),
-                            "entities_in_utterance": [datasets.Value("string")],
-                            "relations": [datasets.Value("string")],
-                            "type_list": [datasets.Value("string")],
-                            "speaker": datasets.Value("string"),
-                            "utterance": datasets.Value("string"),
-                            "all_entities": [datasets.Value("string")],
-                            "active_set": [datasets.Value("string")],
-                            'sec_ques_sub_type': datasets.Value("int64"),
-                            'sec_ques_type': datasets.Value("int64"),
-                            'set_op_choice': datasets.Value("int64"),
-                            'is_inc': datasets.Value("int64"),
-                            'count_ques_sub_type': datasets.Value("int64"),
-                            'count_ques_type': datasets.Value("int64"),
-                            'is_incomplete': datasets.Value("int64"),
-                            'inc_ques_type': datasets.Value("int64"),
-                            'set_op': datasets.Value("int64"),
-                            'bool_ques_type': datasets.Value("int64"),
-                            'entities': [datasets.Value("string")],
-                            "clarification_step": datasets.Value("int64"),
-                            "gold_actions": [[datasets.Value("string")]],
-                            "is_spurious": datasets.Value("bool"),
-                            "masked_verbalized_answer": datasets.Value("string"),
-                            "parsed_active_set": [datasets.Value("string")],
-                            "sparql_query": datasets.Value("string"),
-                            "verbalized_all_entities": [datasets.Value("string")],
-                            "verbalized_answer": datasets.Value("string"),
-                            "verbalized_entities_in_utterance": [datasets.Value("string")],
-                            "verbalized_gold_actions": [[datasets.Value("string")]],
-                            "verbalized_parsed_active_set": [datasets.Value("string")],
-                            "verbalized_sparql_query": datasets.Value("string"),
-                            "verbalized_triple": datasets.Value("string"),
-                            "verbalized_type_list": [datasets.Value("string")]
-                        }
-                    ]
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-        train_path = os.path.join(downloaded_files['all'],'csqa_sparql_to_text/train/')
-        test_path = os.path.join(downloaded_files['all'],'csqa_sparql_to_text/test/')
-        valid_path = os.path.join(downloaded_files['all'],'csqa_sparql_to_text/valid/')
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": train_path,
-                            "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": test_path,
-                            "split": "test"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": valid_path,
-                            "split": "valid"}
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        # Yields (key, example) tuples from the dataset
-        def _transform(x):
-            pattern = {
-                "id": None,
-                "ques_type_id": None,
-                "question-type": "",
-                "description": "",
-                "entities_in_utterance": [],
-                "relations": [],
-                "type_list": [],
-                "speaker": "",
-                "utterance": "",
-                "all_entities": [],
-                "active_set": [],
-                'sec_ques_sub_type': None,
-                'sec_ques_type': None,
-                'set_op_choice': None,
-                'is_inc': None,
-                'count_ques_sub_type': None,
-                'count_ques_type': None,
-                'is_incomplete': None,
-                'inc_ques_type': None,
-                'set_op': None,
-                'bool_ques_type': None,
-                'entities': [],
-                "clarification_step": None,
-                "gold_actions": [],
-                "is_spurious": None,
-                "masked_verbalized_answer": None,
-                "parsed_active_set": [],
-                "sparql_query": None,
-                "verbalized_all_entities": [],
-                "verbalized_answer": None,
-                "verbalized_entities_in_utterance": [],
-                "verbalized_gold_actions": [],
-                "verbalized_parsed_active_set": [],
-                "verbalized_sparql_query": None,
-                "verbalized_triple": [],
-                "verbalized_type_list": []
-            }
-
-            # if "verbalized_triple" in x:
-            #     x["verbalized_triple"] = json.dumps(x["verbalized_triple"])
-            # for k in ["parsed_active_set", "verbalized_gold_actions", "verbalized_parsed_active_set"]:
-            #     if k in x:
-            #         del x[k]
-            pattern.update(x)
-            # if "verbalized_triple" in pattern:
-            #     if type(pattern["verbalized_triple"]) != list:
-            #         print(pattern["verbalized_triple"])
-            #         sys.exit()
-            return pattern
-        data_keys = {}
-        for root, dirs, files in os.walk(filepath):
-            dialog_id = root.split('/')[-1]
-            for i,filename in enumerate(files):
-                sample_id = "%s:%s"%(dialog_id,i)
-                with open(os.path.join(root,filename),'r') as f:
-                    data = json.load(f)
-                    # print("--")
-                    for x in data:
-                        for k,v in x.items():
-                            if not k in data_keys:
-                                data_keys[k] = type(v)
-                    new_data = list()
-                    for i,_ in enumerate(data):
-                        # if "verbalized_triple" in data[i]:
-                        #     print(json.dumps(data[i]["verbalized_triple"], indent=2))
-                        # if i < len(data)-1:
-                        #     if "verbalized_triple" in data[i+1]:
-                        #         print("i+1", json.dumps(data[i+1]["verbalized_triple"], indent=2))
-                        new_data.append(data[i])
-                    data = [ _transform(x) for x in data]
-                    yield sample_id, {
-                        "id": sample_id,
-                        "turns": data
-                    }
-